A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation

Xiean Huang; Caixi Liu

doi:doi:10.11648/j.ellc.20251004.12

Research Article |

| Peer-Reviewed

A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation

Xiean Huang^*

, Caixi Liu

Published in English Language, Literature & Culture (Volume 10, Issue 4)

Received: 28 September 2025 Accepted: 13 October 2025 Published: 30 October 2025

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Abstract

Preferential changes in revision are a phenomenon commonly observed in other-revision contexts (one translator revises another translator’s work). Revisers tend to over-revise the translations rendered by others even though the translations are accurate and adequate enough. Despite the ongoing debate within translation studies on preferential changes for years, detailed case studies of that phenomenon in the scenario of English-to-Chinese translation remain scarce. The current study selects an English text excerpted from a think tank handbook originally published in the United States and collects its unrevised Chinese translation alongside 12 versions of revision conducted independently by 8 undergraduates with different academic backgrounds, 2 postgraduate translation students, 1 doctoral translation student, and their advisor. All the 12 revisers are native speakers of Mandarin Chinese. This study counts the number of changes made by revisers, analyzes and assesses which changes are necessary and which are preferential (unnecessary), and categorizes and quantifies those preferential changes following the classification proposed by Jean Nitzke and Anne-Kathrin Gros in their research on English-to-German translation. Though adopting this classification, this study adapts it slightly so that it can better suit the scenario of English-to-Chinese translation. The research result reveals that the rate of preferential changes declines notably with the revisers’ advancement in academic levels and improvement in specialized training of translation and revision. This study then explores the reasons behind the phenomenon of preferential changes based on the case study. Besides the linguistic reasons and the revisers’ translation competence, sociological reasons are also considered. Some revisers may prefer to actively look for mistakes in the target text in order to demonstrate that they’ve taken the task seriously and performed their duty well, even though the target text does not need so many changes. Regarding future research, a case study of preferential changes in the post-editing of machine translation, or MTPE, will be conducted as a follow-up. With the rapid development and wide application of artificial intelligence-empowered large language models, it is more and more common to post-edit a machine translation rather than translate a script manually from scratch. Post-editing, like revision, is still conducted by humans, at least in the present and for the several years to come.

Published in	English Language, Literature & Culture (Volume 10, Issue 4)
DOI	10.11648/j.ellc.20251004.12
Page(s)	137-145
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Preferential Change, Over-revision, Other-revision, English-Chinese Translation

1. Introduction

The practice of revising translations is as old as the act of translation itself. It involves examining a translation and making necessary adjustments to ensure its compliance with recognized linguistic and functional criteria. According to the definition provided by the International Organization for Standardization

[7]

, revision entails a “bilingual examination of target language content against source language content for its suitability for the agreed purpose.” In this study, the term “revision” exclusively refers to other-revision, where someone other than the original translator conducts the review and revision.

Preferential changes in the revision process represent a common phenomenon observed in contexts where one translator reviews another translator’s work. Revisers often make modifications that exceed what is strictly necessary, even when the initial translation is already accurate and adequate enough. This tendency towards over-revision can be attributed to a variety of factors, including the reviser’s desire to assert their authority, their perception of the original translator’s competence, and their own stylistic preferences. Despite discussions within the field of translation studies regarding preferential changes, detailed case studies focusing on the English-to-Chinese translation scenario remain limited. Therefore, this study aims to explore the phenomenon of preferential changes among translators/revisers working within the English-to-Chinese translation context and to uncover the underlying reasons for these practices, such as revisers’ levels of expertise, understanding of the source and target languages, and personal preferences. By doing so, it seeks to contribute to a deeper understanding of the dynamics of translation revision and to provide some insights that can inform both translation practice and pedagogy.

To address this research gap, this study formulates the following research questions:

1) How does the rate of preferential changes vary across revisers with different levels of academic training and expertise in English-to-Chinese translation?

2) What types of preferential changes are most prevalent in English-to-Chinese translation revision?

3) What factors contribute to the occurrence of preferential changes in other-revision contexts?

By delving into the phenomenon of preferential changes, we aim to shed light on the linguistic factors, translators’ competence, and sociological influences that contribute to this behavior. Some revisers exhibit a tendency to actively seek out mistakes in the target text as a means of demonstrating their thoroughness and commitment to the task, even if the target text does not require extensive modifications. In fact, this mindset is not unique to cross-language translation and revision; it also manifests in same-language editing contexts.

We believe that, whether in university courses or professional training programs, emphasis should be placed on educating translators about adhering to guidelines and predefined quality standards while suppressing personal preferences, particularly concerning lexical choices. Training should equip translators with the skills to discern which edits are truly necessary, thereby enhancing the value and efficiency of most revision tasks. This focus on education and training is particularly important in the context of English-to-Chinese translation, where the linguistic and cultural differences between the two languages can make the revision process particularly challenging. By understanding the factors that drive preferential changes, we can develop more effective strategies for managing the revision process and ensuring that translations meet the best balance of quality and efficiency.

Regarding to the structure of this study, we will initially review relevant literature related to preferential changes in revision, establishing a theoretical foundation for our investigation. Following that, an outline of our setup will be presented, detailing the characteristics of the selected English source text, the unrevised Chinese translation, the profiles of the revisers we recruited, and the methodology employed for data collection. [Two sentences deleted here.] The conclusion section summarizes the findings from our statistical analysis and proposes potential directions for future research, such as exploring preferential changes in machine translation post-editing (MTPE). With the rapid advancement and widespread application of large language models, it has become increasingly common to revise, or post-editing actually, machine-generated translations instead of translating texts manually from scratch. Nevertheless, at least for now and in the coming years, both revision and post-editing processes continue to rely heavily on human judgment.

2. Literature Review

Recent years have witnessed a surge in research interest in the field of translation revision and post-editing, with a growing body of publications. Some of these works provide a comprehensive overview of the field, while others focus on specific aspects, such as the products, processes, and participants of revision and post-editing. Those studies typically employ qualitative and/or quantitative methods, including error analysis, quality assessment, key-logging, eye-tracking, and think-aloud protocols. Because revision and post-editing are two terms with an increasingly blurred boundary due to the quality improvement of machine translation outputs, many studies cover both terms simultaneously and interchangeably. Since our current study is still limited to the revision of another translator’s work rather than post-editing of machine output, our following literature review will try to keep focusing on revision as much as possible, though sometimes those two terms are still inevitably intertwined.

The Introduction section of Translation Revision and Post-editing: Industry Practices and Cognitive Processes

[10]

, a collection of academic papers edited by Maarit Koponen, Brian Mossop, Isabelle S. Robert, and Giovanna Scocchera, provides a well-structured overview of translation revision and post-editing and covers key contributions prior to its publication. In chronological order, Mossop

[14, 15]

and Garcia

[6]

discuss revision in localization. Künzli

[11]

offers a detailed analysis of revision concepts, research status, and future directions. Scocchera

[21]

focuses on revision in literary translation. Robert

[20]

provides a comprehensive review, combining a literature review with bibliometric data on revision. Jakobsen

[9]

explores the blurred boundaries between translation, revision, and post-editing. After the introduction, the volume then presents new research on revision and post-editing in government and corporate translation departments, translation agencies, the literary publishing sector and the volunteer sector, as well as on training in both types of translation checking work, including empirical studies based on surveys, interviews and keystroke logging, as well as more theoretical contributions questioning such traditional distinctions as translating versus editing.

Beyond that collection, Jaccomard

[8]

discusses the self-revision, in collaboration with an English native-speaker, of her own English translation of Yasmina Reza’s play Dans la luge d’Arthur Schopenhauer. She shows how their respective inter-languages affected the final choices made, and a corpus-based study comparing the first draft with the final draft demonstrates that a largely unconscious adherence to translation norms and the reviser’s stylistic preferences both converge to arrive at a translation for the stage rather than the page. Parra-Galiano

[18]

proposes a hierarchy of translator and reviser competences in prototypical scenarios in legal translation with a view to determining the most appropriate revision foci to ensure translation quality. Do Carmo and Koponen

[5]

focus on the complex relations that exist in the translation industry around management, control and production of translations with adequate levels of quality. Translators, by playing different roles in revision and post-editing, carry the responsibility over the quality of the translations that feed a globalized economy. Riondel

[19]

takes stock of a large interview study on revision to make research-informed proposals for revision courses in translation curricula. It addresses four topics: the diversity of revision, the situated nature of revision, the difficulty of the task (including the main faults of young revisers), and the social dimension of revision. Mohammed

[13]

, based on text-typology linguistic models and Mossop’s parameters of revising and editing in translation, examines the potential use of technologies like neural machine translation (NMT) systems and large language models (LLMs) in editing and revisions to enhance both quality and productivity. Asrifan

[2]

navigates ethical dilemmas regarding accuracy, cultural sensitivity, and data privacy in AI-powered translation, and a coordinated strategy including AI developers, enterprises, and expert translators is crucial to resolve these difficulties.

Specifically related to the scenario of English-Chinese translation, revision and post-editing are also widely-discussed topics in the Chinese academia, especially in the recent years with the rising tide of AI and LLMs. Cui Qiliang

[3]

, after comparing distinctions between translation revision and post-editing, especially in the context of Chinese language, explores a range of skills required for creating high-quality revision/post-editing content: an active and conscientious attitude from the reviser/post-editor, knowledge about the project, domain, terminology involved, and the translation environment. It necessitates the ability to swiftly read and identify translation errors that hinder the reader’s comprehension, and to revise/post-edit translations that deviate from the original author’s intention. Yeh

[24]

explores how a class of English as a Foreign Language (EFL) undergraduate students perceived the revision task in an English-Chinese translation classroom. Results suggested that students placed greater importance on accuracy, tailoring, smoothness, and mechanics when revising; students appeared to favor group work and peer review; students generally agreed on the positive role of revision in the improvement of translation quality. Liang

[12]

explores the impact of the initial translation on other-revisers. She investigates two related questions: How does trainees’ revision performance relate to their translation performance? And how is trainees’ revision performance impacted by the initial translated text they are provided with? The results suggest that good translator trainees tend to be also competent in revision, and poor translator trainees tend to be also weak at revision. The trainees tend to find revision tasks more challenging than the translation tasks and negative interference of the initial translation is prevalent both lexically and syntactically. They are very likely to be distracted and misled by the initial translation.

China’s young translation practitioners are also closely following and actively embracing the state-of-the-art developments of the revision and post-editing theories within the field of translation studies. A short guide to post-editing by Jean Nitzke and Silvia Hansen-Schirra was translated and introduced into China no later than 2024. Wang Siyi

[22]

studied her own self-revision process and the improvement of translation quality under Juliane House’s Translation Quality Assessment Model. Xu Ni

[23]

released a revision report on the translation and localization (from Chinese to English) of Chinese online literature (web novels) released on NovelCat, a popular reading app on both Android and iOS. In her report, Xu listed bilingual inter-disciplinary ability, inter-cultural awareness, and the willingness to observe acknowledged standards as essential qualities to become a qualified translator/reviser and better perform the localization task, so that English-speaking readers would be more likely to read those Chinese web novels.

When we narrow down the field to the phenomenon of preferential changes, we find the number of publications on it is limited. One of the pre-translation instructions in the study by Aikawa et al.

[1]

explicitly states: “Avoid over-editing: don’t try to over-edit if the existing translations are grammatical and readable.” De Almeida

[4]

analyzes the edits made by 20 participants post-editing IT texts generated by machine translation (ten translated into French and ten into Brazilian Portuguese). Her study categorized modifications into essential changes, preferential changes, unimplemented essential changes, and introduction of errors. On average, participants made 45.16 preferential changes in a text containing 1008 words; this equates to an unnecessary change every 22.32 words. Oster

[17]

explains the reasons behind some preferential changes. Translators often develop their own mental concept of the source unit with their unique translation ideas, which can lead to discrepancies when confronted with another person’s translation. These translations are not necessarily flawed but may not align perfectly with the translator’s internal representation. Nitzke and Gros

[16]

think that it can be challenging for translators without specialized revision training to lower their quality standards and focus solely on necessary corrections while disregarding personal style and habits.

Through the literature review, we can see that while translation revision and post-editing have garnered considerable attention within the field of translation studies, preferential changes have not received enough scholarly attention they deserve, despite being a common occurrence in the revision and post-editing process. Our study builds upon this foundation by applying Nitzke and Gros’s classification of preferential changes to the English-Chinese translation context, while also examining how academic training level influences revisers’ tendency to make such changes. This addresses a significant gap in the literature, as most studies on preferential changes have focused on European language pairs.

3. Research Design

To ensure a comprehensive understanding of the revision process, several key considerations were taken into account during the design phase. The current study selects a non-literary English text excerpted from a think tank handbook originally published in the United States as the source text and collects its unrevised Chinese translation (generated by a native Chinese-speaking, in-house translator rather than machine output) alongside 12 versions of revision conducted independently by 8 undergraduates with different academic backgrounds, 2 postgraduate translation students, 1 doctoral translation student, and their advisor.

The choice of a non-literary text is intentional. It allows the study to mainly focus on the practical aspects of translation, such as factual accuracy and clarity, rather than the more subjective elements of style and literary interpretation. This approach mirrors the reality of many professional translation tasks, where the primary goal is to convey information accurately and effectively, often seen in technical or informational contexts rather than literary works.

The participants in this study are all native speakers of Mandarin Chinese at the Shanghai International Studies University, and they have studied English as either their first or second foreign language. Some participants had prior experience in revision and/or post-editing, but this was not uniformly distributed among them. The advisor, who specializes in translation studies, is a seasoned academic and senior translator with decades of experience in translation teaching, research, and practice.

This linguistic background ensures that all participants have a strong foundation in both English and Chinese languages, which is crucial for a study focused on the translation revision of this language pair. The inclusion of participants with varying levels of experience—ranging from undergraduates with no formal training in translation and revision to their advisor, who is a seasoned academic and senior translator—provides a rich dataset for analyzing how different levels of translation competence and expertise influence the revision process. The advisor’s extensive experience in translation teaching and research adds a layer of depth to the study, as his revision can serve as a benchmark for evaluating the work of less experienced participants. The diversity of participants’ academic backgrounds and levels helps us explore how different levels of training and expertise influence the tendency towards preferential changes. For example, it is possible that more experienced revisers, such as the doctoral translation student and the advisor, will make fewer preferential changes and more necessary changes which are more impactful in terms of improving the quality of the translation. Conversely, less experienced revisers may make more preferential changes, which may be less focused on improving accuracy and clarity and more influenced by personal preferences or stylistic considerations.

The selected source text is approximately 600 words long in English, while the unrevised Chinese translation spans about 1000 Chinese characters. This length is ideal for a study of this nature, as it is long enough to provide substantial material and data for analysis but short enough to allow for detailed examination of each revision.

The 12 revisers were asked to check and correct the unrevised Chinese translation text, with the option to do so either bilingually (examining both the source text and target text) or monolingually (checking the target text only). There were neither strict time restrictions nor specific revision guidelines imposed on any of the participants, and they had free access to dictionaries and Internet web search, except that they were not allowed to consult each other or use AI-empowered large language models for assistance. The absence of strict time restrictions or specific revision guidelines ensures that the participants’ revisions reflect their natural tendencies and decision-making processes, rather than being influenced by external constraints. After completing the revisions, their work was collected for subsequent data analysis.

In summary, the design of this study is carefully crafted to provide a nuanced understanding and observation of the revision process in translation. By selecting a non-literary text and including participants with a wide range of experience and expertise, the study aims to shed light on how different factors—such as the reviser’s level of training and their abilities to keep focusing on necessary changes—influence the quality and nature of the revisions. This approach not only contributes to the academic understanding of translation revision but also has practical implications for translation training and professional practice, as it may highlight the importance of specialized training.

4. Data Analysis and Categorization

The first step of the analysis, after the collection of the 12 versions of revision, is to label those revisions. The 8 revisions obtained from the undergraduate students with different academic backgrounds are labeled as UG1 to UG8, those from the postgraduate translation students PG1 and PG2, the revisions from the doctoral translation student and the advisor DR1 and AD1. This labeling approach facilitates clear differentiation between the sources of the collected data based on the academic level of the revisers.

The second step is to determine and count the number of changes made by revisers. Depending on the type of change, a text unit can consist of at least one word and at most one sentence. Lexical changes, for example, can be made by changing only one or two Chinese characters. Reformulating the structure of a sentence, on the other hand, can take up to an entire sentence and is seen as one editing instance. However, if a reviser restructures a sentence and makes a lexical change within the sentence at the same time, we will take it as two changes rather than one.

The third step is to analyze and assess which changes are necessary and which are preferential (unnecessary). To diminish subjectivity, which is inevitable due to the nature of this study, in the process of analysis and assessment as much as possible, we have internal discussions before making judgments. For those controversial ones which we cannot agree on, we don’t label them as preferential changes. The Table 1 below lists the total number of changes made by revisers respectively, the number of preferential changes, and the percentage of them.

Table 1. Total Number of Changes, Preferential Changes, and Their Percentage.

	Total Number of Changes	Number of Preferential Changes	Percentage
UG1	27	20	74.07%
UG2	26	9	34.62%
UG3	31	18	58.06%
UG4	29	17	58.62%
UG5	30	14	46.67%
UG6	23	12	52.17%
UG7	25	18	72.00%
UG8	22	12	54.55%
PG1	58	24	41.38%
PG2	46	16	34.78%
DR1	53	12	22.64%
AD1	61	8	13.11%

Several insights regarding the revision practices emerge after we analyze the data presented in Table 1.

The total number of changes made by undergraduates ranges from 22 to 31, whereas for postgraduates, it spans from 46 to 58. For the doctoral translation student and the advisor, the number is 53 and 61 respectively. This suggests that on average, postgraduate translation students make more revisions than undergraduate students with different academic backgrounds. And the number of changes goes even higher for the most experienced advisor. This could indicate a higher level of scrutiny or possibly more confidence in making extensive modifications with the growth of translation training and experience.

Despite making fewer changes in total, the percentage of preferential changes among undergraduates is generally among the highest. For instance, UG1 has a high percentage of 74.07%, while the advisor, despite having the highest number of total changes (61), only has 13.11% preferential changes. This observation means that although postgraduates, the doctoral student and the advisor revise more extensively, undergraduates have a higher proportion of edits based on personal preference rather than necessity. It may also imply that postgraduates, the doctoral student and the advisor, with specialized and intensive training of translation and revision, are more competent, and thus more likely, to focus on necessary corrections rather than subjective improvements.

There is an exceptional case among those participating undergraduates which comes to our attention. Notably, UG2 stands out with only 9 preferential changes out of 26 total changes (34.62%), indicating UG2, compared with his or her peers, has a more focused approach towards necessary revisions rather than personal preference. The distinguished performance appears to be an outlier rather than an indicative measure of the collective proficiency among the group of undergraduates. We believe that UG2’s good performance can be attributed predominantly to his or her superior English proficiency and Chinese attainments. Therefore, while this undergraduate’s achievements are commendable and suggest a high degree of personal skill and dedication, they should not be considered representative of the broader sample’s capabilities in English-Chinese translation and revision tasks and do not reflect the overall English-to-Chinese translation and revision competency level of the participating undergraduates.

Having listed the overall numbers, finally, we shall now classify the instances of preferential changes. As we mentioned above, we evaluate whether the changes made by revisers have improved the target text or not. For example, if a change is categorized by both researchers as a lexical preference, it means that both researchers think that the original word is accurate and adequate enough and the target unit has not been improved or become better after the change of the wording. If only one researcher categorizes some change as “preferential” while the other researcher thinks the change is necessary, we do not count that change as “preferential” and thus do not categorize it.

We categorize and quantify those preferential changes following the classification proposed by Jean Nitzke and Anne-Kathrin Gros

[17]

in their research on English-to-German translation. Though adopting this classification, this study adapts it slightly so that it can better suit the scenario of English-to-Chinese translation. For example, we omit areas like grammar (changing tense or switching between definite and indefinite articles) and spelling (choosing another spelling variant), which is rarely seen in English-to-Chinese translation revision due to the unique characteristics of Chinese language, and insecurity (deleting a target text unit and inserting the same unit without any changes), which cannot be detected and analyzed without the technical support of Translog-like keystroke logging software.

Therefore, six areas are defined for which instances of preferential changes can be observed in our study:

1) lexicon, for example, using synonyms or different terminology;

2) syntax, for example, reordering parts of a sentence;

3) style, for example, rephrasing a target text unit and register preferences;

4) addition, for example, inserting words or information into the target text;

5) deletion, for example, deleting words or information from the target text;

6) punctuation, for example, insertion or deletion of commas.

According to the different areas identified above, the distribution of the preferential change instances by area is shown in Table 2 respectively.

Table 2. Distribution of Preferential Change Instances by Area.

	Lexicon	Syntax	Style	Addition	Deletion	Punctuation	Totality
UG1	12	0	0	6	2	0	20
UG2	4	0	0	0	1	2	7
UG3	11	1	0	1	3	2	18
UG4	9	3	1	3	0	1	17
UG5	3	4	2	3	1	1	14
UG6	5	3	0	3	1	0	12
UG7	6	2	0	7	2	1	18
UG8	3	1	1	0	3	4	12
PG1	9	5	1	4	3	2	24
PG2	7	3	1	3	2	0	16
DR1	8	1	0	1	2	0	12
AD1	6	0	0	1	1	0	8
Totality	83	23	6	32	21	13

Several observations can be made as well after we analyze the data presented in Table 2. Most notably, lexical modifications (e.g., using bingqie (并且) or yiji (以及) to replace the synonymous tongshi (同时) in the original translation text, which occurs repetitively in several revisions) are the most frequent type of preferential change across almost all revisers, totaling 83 instances out of 178, which means lexical changes account for almost half (46.63%) of all preferential changes observed. This suggests that changing words or terms might indeed be perceived as the easiest or the most straightforward way of editing.

Syntactic changes occur much less frequently than lexical ones, with a total of 23 instances. It may indicate that altering sentence structure is seen as more impactful or risky, potentially requiring a greater understanding and confidence of both languages involved.

Style-related changes (e.g., rephrasing for register preferences) are even rarer, with only 6 instances recorded. This scarcity may suggest that style adjustments require a nuanced understanding of the context and the target readership. Meanwhile, this scarcity could be attributed to the nature of the source and target texts chosen for this study, which were non-literary and not so nuanced. Given that the original Chinese translation is done by a think tank in-house translator specializing in the domain, there may have been limited room for stylistic improvements from those revisers, resulting in fewer modifications in this category.

Addition instances, where words or information are added into the target text, amount to 32 cases. Deletions from the target text occurred 21 times. These two categories show that enriching and streamlining content is a significant aspect of the revision process, possibly aiming to improve clarity and completeness and remove redundancies or unnecessary details.

With 13 instances, punctuation changes are the least frequent, except for the style-related changes due to the nature of the texts, among the categories listed. This may reflect that punctuation adjustments are often subtle and may not always be considered important unless those revisers think that some punctuation significantly affects readability or meaning.

Besides the linguistic reasons and the revisers’ translation competence we’ve discussed above, sociological reasons may also be considered as another important but not so visible factor. Some revisers, especially those undergraduate students, who are novice translators and revisers, may prefer to actively look for mistakes in the target text in order to demonstrate that they’ve taken the task seriously, which is assigned by their advisor. This behavior persists even in the absence of actual mistakes, leading students to make alterations wherever possible, such as substituting synonyms, with the primary intention of demonstrating their thorough engagement with the task at hand. By leaving visible traces of modifications, students aim to convey a strong sense of diligence and meticulousness in fulfilling their advisor’s assignments. Their underlying motivation stems from a desire to create a positive impression on their mentors, showcasing not only their commitment but also their capability to meticulously review and refine a text, regardless of its initial quality. This approach reflects both a strategy to gain recognition and an effort to ensure they are perceived as conscientious and detail-oriented students.

Driven by this mentality, novice revisers show their lexical preferences, add and delete some Chinese characters, which is obviously easier and more straightforward than other changes, to visibly demonstrate their involvement and diligence, regardless of whether these changes are necessary or not. We believe that this mindset is not unique to the cross-language translation and revision; it is also a common phenomenon widely existing in the same-language revision and editing context. Whether working across languages or within a single language, some revisers or editors may feel compelled to demonstrate their thoroughness and dedication by making changes, even when such changes do not necessarily enhance the text. This behavior underscores a universal tendency in revision and editing practices, reflecting the desire to leave an imprint of effort and diligence on the text they are asked to revise or edit.

5. Conclusion, Limitation and Future Work

This study set out to investigate preferential changes in English-to-Chinese translation revision through three research questions. Our findings provide the following answers:

First, regarding how the rate of preferential changes varies across revisers with different levels of academic training, our data clearly demonstrates that the percentage of preferential changes declines notably with advancement in academic levels and specialized translation training. Undergraduate revisers showed the highest rates of preferential changes (ranging from 34.62% to 74.07%), while the advisor, as the most experienced professional, exhibited the lowest rate (13.11%). This progression suggests that specialized training in translation and revision helps revisers develop the discernment to focus on necessary corrections rather than subjective preferences.

Second, concerning the types of preferential changes most prevalent in English-to-Chinese translation revision, lexical modifications accounted for nearly half (46.63%) of all preferential changes observed. This predominance of lexical changes over syntactic, stylistic, addition, deletion, and punctuation changes indicates that word-level substitutions represent the most common form of preferential editing in this language pair.

Third, on the factors contributing to preferential changes, our analysis suggests that both competence-based and sociological factors play important roles. The declining rate of preferential changes with increasing expertise supports the competence explanation, where specialized training enables revisers to better distinguish necessary from preferential edits. Additionally, the pattern of changes, particularly among undergraduate revisers, suggests possible sociological motivations, where less experienced revisers may make unnecessary changes to demonstrate diligence and thoroughness to their advisor.

The phenomenon of preferential changes is inevitable because the revisers must have their own version of translation in their mind and they have to face a target text proposal made by another person. In their mind, the already-existing translation output and their own potential translation are competing. But the quality issue is so subjective that different people probably judge differently. This competition in many cases leads to preferential changes.

Though it will not disappear completely, we can try to diminish its influence since quality and efficiency are equally important in most translation and revision processes. The final text simply needs to be acceptable under the quality requirements, not perfect. To achieve this, we need to suppress personal preferences on lexicon, syntax, style, punctuation, and even quality standards. This may be a difficult challenge for revisers to cut through those noises in their mind but it looks like the only way we can make most revision tasks worthwhile.

In fact, this study has presented an initial insight into the behavior and results of different participants in a revision task, and it reveals that specialized training on translation and revision is helpful to avoid preferential changes, since the rate of preferential changes shows a notable decline with the advancement in academic levels and the increase in specialized training of translation and revision. For example, those two postgraduate translation students perform apparently better than those undergraduates with different academic backgrounds, except for UG2, which is an outlier. Our results suggest that revision training should be included in university curricula for foreign language majors in order to train students for these tasks and raise their awareness of preferential changes, which could easily become components of their professional lives in the future.

No matter whether in university courses or training courses for professionals, perhaps the focuses of the translation and revision training should be placed on 1) adhering to guidelines and predefined quality requirements, 2) suppressing personal preferences, especially regarding lexical choices. Trainees may need to learn to explain how they decide that an edit is or is not necessary. They need to learn how to make reasonable decisions. Further, it seems plausible to train translators to work with what the human or machine has previously produced and improve the text with the least possible effort, so that translators who are also trained as revisers will be able to rapidly assess which changes are necessary.

This study has several limitations that we have to acknowledge. First, the small sample size (12 participants) from a single university limits the generalizability of our findings. Future research should include larger and more diverse samples, including professional translators from various institutional contexts. Second, while we observed patterns suggesting sociological motivations behind preferential changes, we lacked direct evidence through interviews or surveys to substantiate these interpretations. Future studies could incorporate mixed methods to better understand revisers’ decision-making processes. Third, using a single advisor as the expert benchmark introduces potential subjectivity; future research would benefit from multiple expert evaluators.

When it comes to machine translation, here is another term “post-editing”. Post-editing entered the industrial world of translation decades of years ago, when Machine Translation (MT) systems started to produce in target languages content that was deemed to be of good enough quality to be edited and improved by translators. [A paragraph deleted here.] With the development of large language models in recent years, post-editing became more widespread.

Therefore, we plan to conduct a case study of preferential changes in the post-editing of machine translation, or MTPE, as a follow-up. We will select the same source text and collect its machine-generated Chinese translation, then ask the same, or if possible, a bigger and more heterogeneous group of participants to post-edit it. We will then compare the results of the post-editing task with the revision task to see if there are any significant differences in the number and types of preferential changes made by the participants. We expect that the post-editing task will elicit more preferential changes than the revision task, as the machine-generated translation may be perceived as less authoritative or less human-like, thus inviting more interventions from the post-editors. We also expect that the distribution of preferential changes by area may differ between the two tasks, reflecting the different nature of the source texts (human-generated vs. machine-generated).

Abbreviations

AI	Artificial Intelligence
EFL	English as a Foreign Language
LLM	Large Language Model
MT	Machine Translation
MTPE	Machine Translation Post-Editing
NMT	Neural Machine Translation

Author Contributions

Xiean Huang: Conceptualization, Data curation, Methodology, Writing – review & editing

Caixi Liu: Formal Analysis, Investigation, Writing – original draft

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Aikawa, Takako et al. (2012) The impact of crowdsourcing post-editing with the collaborative translation framework. Advances in Natural Language Processing, 1-10. https://doi.org/10.1007/978-3-642-33983-7_1
[2]	Asrifan, Andi (2025). Navigating ethical dilemmas in AI-powered translation: Challenges and solutions. Role of AI in Translation and Interpretation, pp. 327-356. https://doi.org/10.4018/979-8-3373-0060-3.ch012
[3]	Cui, Qiliang (2014). On the Post-editing of Machine Translation. Chinese Translators Journal. (6): 68-73.
[4]	De Almeida, Giselle (2013) Translating the post-editor: an investigation of post-editing changes and correlations with professional experience across two Romance languages. PhD dissertation, Dublin City University. http://doras.dcu.ie/17732/
[5]	Do Carmo, Félix and Koponen, Maarit (2024). Revisers and post-editors: The guardians of quality. Handbook of the Language Industry: Contexts, Resources and Profiles, pp. 203-224. https://doi.org/10.1515/9783110716047-010
[6]	Garcia, Ignacio (2008) Translating and revising for localisation: what do we know? What do we need to know?. Perspectives 16: 1, 49-60. https://doi.org/10.1080/09076760802517630
[7]	International Organization for Standardization (2015) ISO 17100 Translation Services— Requirements for Translation Services.
[8]	Jaccomard, Hélène (2020). “Cheerful or merry?” Investigating literary translation revision. Australian Journal of French Studies, 57 (1), pp. 49-65. https://doi.org/10.3828/ajfs.2020.07
[9]	Jakobsen, Arnt Lykke (2019) Moving translation, revision, and post-editing boundaries. Moving Boundaries in Translation Studies (Helle V. Dam et al., eds.), 64-80. https://doi.org/10.4324/9781315121871-5
[10]	Koponen, M., Mossop B., Robert I. S., and Scocchera G. eds (2021) Translation Revision and Post-editing: Industry Practices and Cognitive Processes, 1-17.
[11]	Künzli, Alexander (2014) Die Übersetzungsrevision—Begrifsklärungen, Forschungsstand, Forschungsdesiderate’. Trans-kom 7: 1, 1-29.
[12]	Liang, Haiyan (2021). Initial translation interference to reviser trainees in English-LOTE translation revision tasks. Translation and Interpreting, 13 (2), pp. 92-108. https://doi.org/10.12807/ti.113202.2021.a06
[13]	Mohammed, Tawffeek A. S. (2025). From Google translate to ChatGPT: The use of large language models in translating, editing, and revising. Role of AI in Translation and Interpretation, pp. 1-31. https://doi.org/10.4018/979-8-3373-0060-3.ch001
[14]	Mossop, Brian (2007) Empirical studies of revision: what we know and need to know. Journal of Specialised Translation 8, 5-20. https://doi.org/10.26034/cm.jostrans.2007.695
[15]	Mossop, Brian (2011) Revision. Handbook of Translation Studies (Vol. 2) (Gambier and Van Doorslaer, eds.), 135-39. https://doi.org/10.1075/hts.2.rev1
[16]	Nitzke, Jean and Gros, Anne-Kathrin. (2021) Preferential Changes in Revision and Post-editing. Translation Revision and Post-editing: Industry Practices and Cognitive Processes (Maarit Koponen et al., eds.), 21-34. https://doi.org/10.4324/9781003096962-3
[17]	Oster, Katharina (2017) The influence of self-monitoring on the translation of cognates. In Empirical Modelling of Translation and Interpreting (Hansen-Schirra et al., eds.), 23-39.
[18]	Parra-Galiano, Silvia (2021). Translators’ and revisers’ competences in legal translation: Revision foci in prototypical scenarios. Target, 33 (2), pp. 228-253. https://doi.org/10.1075/target.21065.gal
[19]	Riondel, Aurélien (2024). How to teach revision: tips from an interview study. Interpreter and Translator Trainer, 18 (3), pp. 507-522. https://doi.org/10.1080/1750399x.2024.2372916
[20]	Robert, Isabelle S. et al. (2018) Conceptualizing translation revision competence: a pilot study on the “fairness and tolerance” attitudinal component. Perspectives: Studies in Translatology 26: 1, 2-23. https://doi.org/10.1080/0907676x.2017.1330894
[21]	Scocchera, Giovanna (2017) La revisione della traduzione editoriale dall’inglese all’italiano. Ricerca, professione formazione.
[22]	Wang, Siyi (2023). Translator’s Self-revision Process in Post Editing under House’s Translation Quality Assessment Model on the Improvement of Translation Quality. Thesis for the Degree of Master of Translation and Interpreting. Shandong Normal University, Jinan, Shandong, P. R. China.
[23]	Xu, Ni (2023). A Revision Report on Localization Translation of Chinese Online Literature Released on NovelCat. Thesis for the Degree of Master of Translation and Interpreting. Central South University, Changsha, Hunan, P. R. China.
[24]	Yeh, Chun-Chun (2021). Revision in the process-oriented translation classroom: Student perspectives. Compilation and Translation Review, 14 (2), pp. 87-124.

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APA Style

Huang, X., Liu, C. (2025). A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation. English Language, Literature & Culture, 10(4), 137-145. https://doi.org/10.11648/j.ellc.20251004.12

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ACS Style

Huang, X.; Liu, C. A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation. Engl. Lang. Lit. Cult. 2025, 10(4), 137-145. doi: 10.11648/j.ellc.20251004.12

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AMA Style

Huang X, Liu C. A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation. Engl Lang Lit Cult. 2025;10(4):137-145. doi: 10.11648/j.ellc.20251004.12

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@article{10.11648/j.ellc.20251004.12,
  author = {Xiean Huang and Caixi Liu},
  title = {A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation
},
  journal = {English Language, Literature & Culture},
  volume = {10},
  number = {4},
  pages = {137-145},
  doi = {10.11648/j.ellc.20251004.12},
  url = {https://doi.org/10.11648/j.ellc.20251004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ellc.20251004.12},
  abstract = {Preferential changes in revision are a phenomenon commonly observed in other-revision contexts (one translator revises another translator’s work). Revisers tend to over-revise the translations rendered by others even though the translations are accurate and adequate enough. Despite the ongoing debate within translation studies on preferential changes for years, detailed case studies of that phenomenon in the scenario of English-to-Chinese translation remain scarce. The current study selects an English text excerpted from a think tank handbook originally published in the United States and collects its unrevised Chinese translation alongside 12 versions of revision conducted independently by 8 undergraduates with different academic backgrounds, 2 postgraduate translation students, 1 doctoral translation student, and their advisor. All the 12 revisers are native speakers of Mandarin Chinese. This study counts the number of changes made by revisers, analyzes and assesses which changes are necessary and which are preferential (unnecessary), and categorizes and quantifies those preferential changes following the classification proposed by Jean Nitzke and Anne-Kathrin Gros in their research on English-to-German translation. Though adopting this classification, this study adapts it slightly so that it can better suit the scenario of English-to-Chinese translation. The research result reveals that the rate of preferential changes declines notably with the revisers’ advancement in academic levels and improvement in specialized training of translation and revision. This study then explores the reasons behind the phenomenon of preferential changes based on the case study. Besides the linguistic reasons and the revisers’ translation competence, sociological reasons are also considered. Some revisers may prefer to actively look for mistakes in the target text in order to demonstrate that they’ve taken the task seriously and performed their duty well, even though the target text does not need so many changes. Regarding future research, a case study of preferential changes in the post-editing of machine translation, or MTPE, will be conducted as a follow-up. With the rapid development and wide application of artificial intelligence-empowered large language models, it is more and more common to post-edit a machine translation rather than translate a script manually from scratch. Post-editing, like revision, is still conducted by humans, at least in the present and for the several years to come.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation

AU  - Xiean Huang
AU  - Caixi Liu
Y1  - 2025/10/30
PY  - 2025
N1  - https://doi.org/10.11648/j.ellc.20251004.12
DO  - 10.11648/j.ellc.20251004.12
T2  - English Language, Literature & Culture
JF  - English Language, Literature & Culture
JO  - English Language, Literature & Culture
SP  - 137
EP  - 145
PB  - Science Publishing Group
SN  - 2575-2413
UR  - https://doi.org/10.11648/j.ellc.20251004.12
AB  - Preferential changes in revision are a phenomenon commonly observed in other-revision contexts (one translator revises another translator’s work). Revisers tend to over-revise the translations rendered by others even though the translations are accurate and adequate enough. Despite the ongoing debate within translation studies on preferential changes for years, detailed case studies of that phenomenon in the scenario of English-to-Chinese translation remain scarce. The current study selects an English text excerpted from a think tank handbook originally published in the United States and collects its unrevised Chinese translation alongside 12 versions of revision conducted independently by 8 undergraduates with different academic backgrounds, 2 postgraduate translation students, 1 doctoral translation student, and their advisor. All the 12 revisers are native speakers of Mandarin Chinese. This study counts the number of changes made by revisers, analyzes and assesses which changes are necessary and which are preferential (unnecessary), and categorizes and quantifies those preferential changes following the classification proposed by Jean Nitzke and Anne-Kathrin Gros in their research on English-to-German translation. Though adopting this classification, this study adapts it slightly so that it can better suit the scenario of English-to-Chinese translation. The research result reveals that the rate of preferential changes declines notably with the revisers’ advancement in academic levels and improvement in specialized training of translation and revision. This study then explores the reasons behind the phenomenon of preferential changes based on the case study. Besides the linguistic reasons and the revisers’ translation competence, sociological reasons are also considered. Some revisers may prefer to actively look for mistakes in the target text in order to demonstrate that they’ve taken the task seriously and performed their duty well, even though the target text does not need so many changes. Regarding future research, a case study of preferential changes in the post-editing of machine translation, or MTPE, will be conducted as a follow-up. With the rapid development and wide application of artificial intelligence-empowered large language models, it is more and more common to post-edit a machine translation rather than translate a script manually from scratch. Post-editing, like revision, is still conducted by humans, at least in the present and for the several years to come.

VL  - 10
IS  - 4
ER  -

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Author Information

Xiean Huang

Graduate Institute of Interpretation and Translation, Shanghai International Studies University, Shanghai, P. R. China

Contact Email

http://orcid.org/0009-0002-3112-0235
Caixi Liu

Graduate Institute of Interpretation and Translation, Shanghai International Studies University, Shanghai, P. R. China

Contact Email

http://orcid.org/0009-0006-9657-3410

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Plain Text BibTeX RIS

APA Style

Huang, X., Liu, C. (2025). A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation. English Language, Literature & Culture, 10(4), 137-145. https://doi.org/10.11648/j.ellc.20251004.12

Copy | Download

ACS Style

Huang, X.; Liu, C. A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation. Engl. Lang. Lit. Cult. 2025, 10(4), 137-145. doi: 10.11648/j.ellc.20251004.12

Copy | Download

AMA Style

Huang X, Liu C. A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation. Engl Lang Lit Cult. 2025;10(4):137-145. doi: 10.11648/j.ellc.20251004.12

Copy | Download

@article{10.11648/j.ellc.20251004.12,
  author = {Xiean Huang and Caixi Liu},
  title = {A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation
},
  journal = {English Language, Literature & Culture},
  volume = {10},
  number = {4},
  pages = {137-145},
  doi = {10.11648/j.ellc.20251004.12},
  url = {https://doi.org/10.11648/j.ellc.20251004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ellc.20251004.12},
  abstract = {Preferential changes in revision are a phenomenon commonly observed in other-revision contexts (one translator revises another translator’s work). Revisers tend to over-revise the translations rendered by others even though the translations are accurate and adequate enough. Despite the ongoing debate within translation studies on preferential changes for years, detailed case studies of that phenomenon in the scenario of English-to-Chinese translation remain scarce. The current study selects an English text excerpted from a think tank handbook originally published in the United States and collects its unrevised Chinese translation alongside 12 versions of revision conducted independently by 8 undergraduates with different academic backgrounds, 2 postgraduate translation students, 1 doctoral translation student, and their advisor. All the 12 revisers are native speakers of Mandarin Chinese. This study counts the number of changes made by revisers, analyzes and assesses which changes are necessary and which are preferential (unnecessary), and categorizes and quantifies those preferential changes following the classification proposed by Jean Nitzke and Anne-Kathrin Gros in their research on English-to-German translation. Though adopting this classification, this study adapts it slightly so that it can better suit the scenario of English-to-Chinese translation. The research result reveals that the rate of preferential changes declines notably with the revisers’ advancement in academic levels and improvement in specialized training of translation and revision. This study then explores the reasons behind the phenomenon of preferential changes based on the case study. Besides the linguistic reasons and the revisers’ translation competence, sociological reasons are also considered. Some revisers may prefer to actively look for mistakes in the target text in order to demonstrate that they’ve taken the task seriously and performed their duty well, even though the target text does not need so many changes. Regarding future research, a case study of preferential changes in the post-editing of machine translation, or MTPE, will be conducted as a follow-up. With the rapid development and wide application of artificial intelligence-empowered large language models, it is more and more common to post-edit a machine translation rather than translate a script manually from scratch. Post-editing, like revision, is still conducted by humans, at least in the present and for the several years to come.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation

AU  - Xiean Huang
AU  - Caixi Liu
Y1  - 2025/10/30
PY  - 2025
N1  - https://doi.org/10.11648/j.ellc.20251004.12
DO  - 10.11648/j.ellc.20251004.12
T2  - English Language, Literature & Culture
JF  - English Language, Literature & Culture
JO  - English Language, Literature & Culture
SP  - 137
EP  - 145
PB  - Science Publishing Group
SN  - 2575-2413
UR  - https://doi.org/10.11648/j.ellc.20251004.12
AB  - Preferential changes in revision are a phenomenon commonly observed in other-revision contexts (one translator revises another translator’s work). Revisers tend to over-revise the translations rendered by others even though the translations are accurate and adequate enough. Despite the ongoing debate within translation studies on preferential changes for years, detailed case studies of that phenomenon in the scenario of English-to-Chinese translation remain scarce. The current study selects an English text excerpted from a think tank handbook originally published in the United States and collects its unrevised Chinese translation alongside 12 versions of revision conducted independently by 8 undergraduates with different academic backgrounds, 2 postgraduate translation students, 1 doctoral translation student, and their advisor. All the 12 revisers are native speakers of Mandarin Chinese. This study counts the number of changes made by revisers, analyzes and assesses which changes are necessary and which are preferential (unnecessary), and categorizes and quantifies those preferential changes following the classification proposed by Jean Nitzke and Anne-Kathrin Gros in their research on English-to-German translation. Though adopting this classification, this study adapts it slightly so that it can better suit the scenario of English-to-Chinese translation. The research result reveals that the rate of preferential changes declines notably with the revisers’ advancement in academic levels and improvement in specialized training of translation and revision. This study then explores the reasons behind the phenomenon of preferential changes based on the case study. Besides the linguistic reasons and the revisers’ translation competence, sociological reasons are also considered. Some revisers may prefer to actively look for mistakes in the target text in order to demonstrate that they’ve taken the task seriously and performed their duty well, even though the target text does not need so many changes. Regarding future research, a case study of preferential changes in the post-editing of machine translation, or MTPE, will be conducted as a follow-up. With the rapid development and wide application of artificial intelligence-empowered large language models, it is more and more common to post-edit a machine translation rather than translate a script manually from scratch. Post-editing, like revision, is still conducted by humans, at least in the present and for the several years to come.

VL  - 10
IS  - 4
ER  -

Copy | Download