Introducing linguistic transformation to improve translation memory retrieval. Results of a professional translators’ survey for S panish, F rench and A rabic (2021)

Abstract: Translation memory systems (TMS) are the main component of computer-assisted translation (CAT) tools. They store translations allowing to save time by presenting translations on the database through matching of several types such as fuzzy matches, which are calculated by algorithms like the edit distance. However, studies have demonstrated the linguistic deficiencies of these systems and the difficulties in data retrieval or obtaining a high percentage of matching, especially after the application of syntactic and semantic transformations as the active/passive voice change, change of word order, substitution by a synonym or a personal pronoun, for instance. This paper presents the results of a pilot study where we analyze the qualitative and quantitative data of questionnaires conducted with professional translators of Spanish, French and Arabic in order to improve the effectiveness of TMS and explore all possibilities to integrate further linguistic processing from ten transformation types. The results are encouraging, and they allowed us to find out about the translation process itself; from which we propose a pre-editing processing tool to improve the matching and retrieving processes.

## introduction computer-assisted translation tools are expanding by offering translators increasingly useful solutions; they are composed of several tools such as terminology databases, the integration of machine translation engines and translation memories (tm). translation memories are the most relevant; their function is to store previous translations so that when translating a new segment, the user can automatically retrieve from a database its equivalent in the target language, avoiding having to translate a segment already recorded in the database @xcite . previously, the system automatically splits the source text into segments or units and searches for a similar translation by matching of several levels. the segments are usually sentences beginning with a capital letter and ending with a full stop through the segmentation by punctuation @xcite . among these matching, the fuzzy matches retrieve segments from the tm that are almost similar to their equivalents in the target language and allow a translation with less post-editing. the percentage of these matches is usually calculated using an algorithm based on edit distance or levenshtein distance @xcite . in addition @xcite reported that fuzzy matching techniques use different approaches to estimate the degree of similarity between two sentences by calculating: the percentage of tokens (or characters) that appear in both segments potentially allowing for synonyms and paraphrase, the length of the longest matching sequence of tokens, or n-gram matching, the edit distance between segments, the most commonly used metric in cat tools, automated mt evaluation metrics such as translation edit rate (ter), the amount of overlap in syntactic parse trees, or a more recently proposed method, the distance between continuous sentence representations. however, different authors consider different percentages for the fuzzy matches: 70% and 95% @xcite or between 1% and 99% @xcite . fuzzy matches do not have an exact definition since they depend on personal settings of each user and tms. the most important aspect is to have a high percentage of matching. to this end, several studies are being carried out in order to increase the matching percentage and improve the data retrieval. the rest of the article is structured as follows: section 2 presents previous related work, section 3 describes the methodology of this research, the survey design and data collection, section 4 presents, analyses and discusses the qualitative and quantitative data divided in three subsections: participants profile (4.1), use of tms (4.2), and human evaluations of semantic and syntactic transformations (4.3). finally, the findings, the conclusions and future work will be presented in section 5. ## related work recent research conducted by @xcite , @xcite propose a new approach of new generation translation memories using deep learning techniques. the study conducted by members of the research group in computational linguistics at the university wolverhampton showed how to improve the performance of these systems. the authors introduced sentence encoders to improve tms matching and retrieving processes as an alternative to conventional algorithms. other related work by @xcite presents a comparative study for spanish, french and arabic applying ten semantic and syntactic transformations to calculate and analyze 1500 original and transformed segments in three pair languages: spanish-french (es-fr), french-spanish (fr-es), and arabic-spanish (ar-es) with two tms: sdl trados and memoq in order to identify the tms deficiencies and propose solutions to improve the systems. the analysis of empirical data indicates that the matching scores decrease between language pairs of the same language family such as es-fr/fr-es because of the transformations, but it also shows that arabic as a source language faces other difficulties when it comes to translating into spanish, where several segments have no matching. arabic also registered lower matching percentage with memoq, which seems to have more difficulties with the word order transformation than sdl trados despite that modern standard arabic has a rich and flexible morphology in terms of word order @xcite and there are several possibilities of syntactic typology: verb, subject, object (vso), subject, verb, object (svo) and verb, object, subject (vos). for spanish, the first results show that memoq has considerable difficulties when it comes to transform the active/passive voice and the substitution of a word by a synonym. sdl trados faces difficulties with the substitution of two words by their synonyms and the change of the word order, sentence and/or clause order. as for french, these systems record lower matching when transforming the active/passive voice and replacing a word by a personal pronoun despite the nature of the french language with a frequent use of the passive voice unlike the spanish language where the use of the passive voice is limited @xcite . these results indicate that tms need to integrate more linguistic processing to improve the data retrieval. similarly, others researches (silvestre @xcite demonstrated the importance of integrating more language processing in tms after the calculation and analysis of fuzzy matches. the authors suggested lexical, semantical and syntactical transformations for english-spanish/spanish-english and it was observed that spanish as a target language has more lexical and syntactical difficulties due to the syntactic complexity of spanish. consequently, the research demonstrated the shortcomings and the linguistics limitations of tms. however, improving tms retrieving processes still needs human assessment by professional translators, not only from a linguistic and computational point of view. this paper, which presents the second phase of our research on different possibilities to improve the tms matching process, discuss the results of questionnaires conducted with native speakers and professional translators of spanish, french and arabic. the objective is to continue to evaluate the empirical data obtained on the first phase and analyze all the qualitative and quantitative data in order to draw conclusions about different aspects, in particular on how to apply these linguistic transformations to improve the tms efficiency. ## survey design and data collection the objective of this research is to explore the possibilities to improve the tms matching process through the evaluation of ten linguistic transformations for spanish, french and arabic: 1) change active to passive voice, 2) change passive to active voice, 3) change the word, sentence or clause order, 4) replace one word with its synonym, 5) replace two words with their synonyms, 6) replace two words with their synonyms and change the word, phrase or clause order, 7) replace one word into a personal pronoun, 8) replace one word into a personal pronoun and change the word, sentence or clause order, 9) change active to passive voice and replace one word with its synonym, and 10) change active to passive voice and replace one word into a personal pronoun. for the survey foot_0 , we selected ten original segments with their corresponding transformation as mentioned previously for each language pair: es-fr, fr-es, and ar-es. we collected and built multilingual corpora from the united nations general assembly, internal regulation and share it with the participants in excel files. 2 the questionnaire was designed with google forms in three languages (spanish, french and arabic) according to the participants' profile. the choice of these three languages is based on two main reasons i) addressing the participants with their native language and/or working language allows us to have clear assessments and avoid communication ambiguities and ii) the three languages are the language pairs analyzed in the first phase of our research. thus, it is a logical continuation. regarding the data collection, the participants were contacted in early january 2021 and the questionnaires were shared online by emailing the participants and explaining the stages of the research with a guideline 3 in three languages so every participant had to choose their language. once accepted, the participants completed the questionnaires between 17 and 27 january 2021. furthermore, all participants are graduated, post graduated or professional translators in different fields, including two participants who are phd students in translation. the participants come from i) algeria and egypt for the arabic language, ii) spain for the spanish language, and finally iii) from france for french language (see 4.1). ## analysis, discussion of quantitative and qualitative data in this section, we present and analyze the results of the questionnaires. first, we present the collected data with tables, graphs and/or descriptive statistics depending on the nature of the questions: open and closed ones (saldanha & o'brien, 2014); then, we analyze the participants answers, question by question according to the questionnaire order. we will present and discuss the human assessment covering three subsections i) participants profile, ii) use of tms, and finally iii) human evaluation of the linguistic transformation. ## conclusions and future research in this paper, we have presented the results of a questionnaire survey conducted among professional translators specializing in different fields for the spanish, french and arabic languages. the objective of this survey, which is the second phase of our research, is to find out how to improve the matching and data retrieval process by applying semantical and syntactical transformations as well as to study the different ways of integrating these transformations and other related proposals. the findings related to the participants profile as well as the use of tms indicate that 60% of participants use tms to translate mainly legal and/or administrative texts with 67% of the calculated specialties. the analysis of the quantitative and qualitative data related to the third part of the questionnaire, i.e. the semantical and syntactical transformations, showed that translators from spanish apply transformations by synonymy, by personal pronoun, change active/passive voice and prefer transformations with one or two modifications without having to change the words order, sentences and/or clauses order. as for the translators of french, they also apply transformation by synonymy, change active/passive, passive/active voice and substitution by a personal pronoun. all translators from arabic indicate that they prefer substitution with a synonym without changing the word order; they only make one change in the original segment. similarly, our ten initial transformations receive a favorable evaluation from professional translators, which supports our approach to the possibility of integrating them through linguistic processing adapted to each language. all these transformations and other proposals can be integrated during the translation process with 60% of the answers for spanish, french and arabic. thus, it is important to reflect that in the translation process itself the participants prefer editing at the same time of translating, instead of the process of pre-editing the source text before it is uploaded into the system and post-editing which only represent 20% of the participants. in addition, the participants propose to integrate other types of transformations that we consider to be techniques or strategies used by translators to improve their work, such as reformulation, addition, omission, as well as the change of grammatical gender, or relative pronoun. these proposals will be studied exhaustively in order to consider how we can integrate translation strategies or techniques to improve the efficiency of the tms providing ideally an editing tool or an automatic paraphrasing process integrated in the tms. finally, these results will be further developed and improved as we plan to continue this research and propose new approaches, especially regarding the translation process itself and how to use translation techniques and strategies in that process.