PRHLT ’s Submission to WLAC 2022 (2022)

Abstract: This paper describes our submission to the Word-Level AutoCompletion shared task of WMT22. We participated in the English–German and German–English categories. We proposed a segment-based interactive machine translation approach whose central core is a machine translation (MT) model which generates a complete translation from the context provided by the task. From there, we obtain the word which corresponds to the autocompletion. With this approach, we aim to show that it is possible to use the MT models in the autocompletion task by simply performing minor changes at the decoding step, obtaining satisfactory results.

## introduction machine translation (mt) has significantly improved in recent years with the emergence of neural machine translation (nmt), but it still cannot assure high-quality translations for all tasks @xcite . for those scenarios with rigorous translation quality requirements, it is critical for professional translators to manually validate the translations generated by the nmt system. the computer-aided translation (cat) tools show up to improve the validation and editing process carried out by translators. researchers approached cat tools from many directions with the aim of reducing the human effort of correcting the automatic translations. among cat tools such as translation memory @xcite , augmented translation @xcite and terminology management @xcite ; 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 is a new shared task introduced in wmt22. 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. we have experimented with a similar cat tool from the interactive machine translation (imt) framework. in this field of research, the translation is generated in a collaborative process between the human translator and the mt model. among the different approaches, the segment-based imt @xcite protocol presents certain similarities with wlac: at each step, the user validates sequences of translated words-the context-and makes a correction-the word to autocomplete. therefore, in this work we have approached wlac as a simplification of segment-based imt, using the context as the validated segments and the typed characters as the word correction; and limiting the process to the first iteration. this has allowed us to tackle wlac by training a conventional nmt model and adapting it at the decoding step. source (x): una versión traducida de un texto. reference (y): a translated version of a text. iter-0 ( f ) ( ) (ŷ) a written version of a story. ( f ) (a) (version of a) iter-1 (s) t (ŷ) a translated version of a document. ( f ) (a translated version of a) iter-2 (s) t (ŷ) a translated version of a text. final (ŷ ≡ y) a translated version of a text. figure 1 : examples of a segment-based imt session to translate a sentence from spanish to english; and a wlac session for predicting a word for a source sentence, a translation context, and a human-typed character sequence. 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: given a source sentence x j 1 , a sequence of typed characters s with that in mind, we can rewrite eq. ( 1 ) as: therefore, we can obtain the autocompleted word (w w 1 = s k 1 w w k+1 ) by performing a single step of the segment-based imt protocol, discarding the rest of the translation prediction. figure 1 illustrates an example of segment-based imt compared to the wlac task for the same case. in the segment-based imt (fig. 1a ) example, at iteration 0, the system generates an incorrect first hypothesis. the, at iteration 1, the user validates a sequence of segments and types the first character of the word 'translated' to help the system fulfill the sequence of words between the first two segments. after that, the system generates a new translation with all the validated segments and the human-typed character sequence. the system repeats this process at the second iteration, ending with a correct translation. the wlac (fig. 1b ) example simplifies the case that happens at iteration 1. although we have the same source sentence, validated segments (left and right context) and human-typed character sequence, in this case, the system only has to find one word between the two segments instead of generating the whole sentence. ## experimental setup in this section, we present the details of our experimental session. ## results in this section we present our experimental results. we trained four different models, alternating between the rnn and transformer architectures and the use of the bpe algorithm on the english-german language pairs. prior to submitting our systems, we used the synthetic validation dataset created from the provided data (see section 4.2). as reflected in all in all, these results show that the segmentbased imt methodology is a promising approach to adapting an mt model to the wlac task. moreover, due to the shared task constrains, we trained our systems using only the data provided by the organizers. however, one of the benefits of our approach is that any mt system can be easily adapted to be used for wlac. ## conclusions in this work, we have presented our submission to wlac shared task from wmt22. our approach consisted in adapting the segment-based imt methodology to the wlac task, which allows us to use a conventional nmt model to tackle this task by simply adapting it at the decoding step. we tested some of the most used nmt architectures, achieving very encouraging results. as a future work, we would like to test our ap-proach using a more robust nmt system, adapted to the domain of the task to perform-instead of training an ad hoc system, as we did in this work due to the task restrictions.