PRHLT ’s Submission to WLAC 2023 (2023)

Abstract: This paper describes our submission to the Word-Level AutoCompletion shared task of WMT23. We participated in the English–German and German–English categories. We extended our last year segment-based interactive machine translation approach to address its weakness when no context is available. Additionally, we fine-tune the pre-trained mT5 large language model to be used for autocompletion.

## introduction despite its improvement in recent years with the emergence of neural machine translation (nmt), machine translation (mt) still cannot assure highquality translations for all tasks @xcite . as a consequence, it is critical for professional translators to manually validate the translations generated by the nmt system for those scenarios with rigorous translation quality requirements. computeraided translation (cat) tools emerged to improve the validation and editing process carried out by translators. with the aim of reducing the human effort of correcting the automatic translations, researchers approached cat tools from many directions. among cat tools such as translation memory @xcite , augmented translation (lommel, 2018) and terminology management (verplaetse and lambrechts, 2019); we can find autocompletion tools, which help professional translators by providing new partial translations according to the validated parts they have supplied to the system. word level autocompletion (wlac) @xcite was introduced as a shared task in wmt22 @xcite . its aim is to complete a target word given a source sentence, a sequence of characters typed by the human translator and a translation context. four types of context are possible: zero-contex: no context is given. suffix: a sequence of translated words located after the word to autocomplete. prefix: a sequence of translated words located prior to the word to autocomplete. bi-contex: a combination of the suffix and the prefix type. that is, there is a sequence of translated words located after the word to autocomplete, and a sequence of translated words located prior to the word to autocomplete. note that, in all cases, the word to autocomplete is not necessarily consecutive to these contexts. approaches to wlac include modeling the task as a structured prediction (generation) task @xcite , modeling it as a segment-based interactive machine translation (imt) task @xcite , using pre-trained nmt models and available libraries @xcite , and using a generator-reranker framework @xcite . in this work, we extended the segment-based imt approach from @xcite by adding a module based on a statistical dictionary that tackles zero-context completions-which are the cases in which, not having any feedback, the imt system performs at its worst. additionally, since this year edition allowed the use of pre-trained large language model (llm), we experimented using the mt5 model @xcite . ## segment-based interactive machine translation segment-based imt establishes a framework in which a human translator works together with the mt system to produce the final translation. this collaboration starts with the system proposing an initial translation hypothesis y i 1 of length i. then, the user reviews this hypothesis and validates those sequence of words which they consider to be correct ( f1 , . . . , fn ; where n is the number of non-overlapping validated segments). after that, they are able to merge two consecutive segments fi , fi+1 into a new one. finally, they correct a word-which introduces a new one-word validated segment, fi , which is inserted in f n 1 . this correction can also consist in a partially typed word f i , in which case the system would complete it as part of its prediction. the system's reacts to this user feedback by generating a sequence of new translation segments g n 1 = g 1 , . . . , g n ; where each g n is a subsequence of words in the target language. this sequence complements the user's feedback to conform the new hypothesis: in this work, we extended navarro et al. (2022)'s segment-based imt approach by adding a new module that handles zero-context completions, which are harder for the imt system to deal with (since there is no user feedback). additionally, we designed a new approach based on the mt5 llm @xcite . ## experimental setup in this section, we present the details of our experimental session. ## results regarding the mt5 approach, its main advantage is that we can adapt an already pre-trained mt5 model by simply performing fine-tuning with ## conclusions in this work, we have presented our submission to wlac shared task from wmt23. our first proposal extended @xcite 's segment-based imt approach by adding a zerocontext-based on a statistical dictionary-that handles separately the cases in which no context is given. this approach yielded satisfactory results for all cases except when the given context consists in a suffix. our second proposal consisted in leverage the pre-trained llm model mt5 by performing a simple fine-tuning that enables the model to be used for wlac, achieving satisfactory results for all type of contexts. as a future work, we would like to study the behavior of the segment-based imt approach when dealing with suffixes. additionally we would like to consider the use of other llm, as well as different versions of the mt5 model.