The ADAPT ’s Submissions to the WMT 20 Biomedical Translation Task (2020)

Abstract: This paper describes the ADAPT Centre’s submissions to the WMT20 Biomedical Translation Shared Task for English-to-Basque. We present the machine translation (MT) systems that were built to translate scientific abstracts and terms from biomedical terminologies, and using the state-of-the-art neural MT (NMT) model: Transformer. In order to improve our baseline NMT system, we employ a number of methods, e.g. “pseudo” parallel data selection, monolingual data selection for synthetic corpus creation, mining monolingual sentences for adapting our NMT systems to this task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that systematic addition of the aforementioned techniques to the baseline yields an excellent performance in the English-to-Basque translation task.

## introduction the adapt centre participated in the biomedical translation shared task of the fifth conference of machine translation (wmt20). this task is about evaluating systems on the translation of documents from the biomedical domain. the test data consists of biomedical abstracts and terminologies. the task addresses a number of language pairs, and we participated in the english-to-basque translation task. to make the readers familiar with the biomedical translation task and to understand the challenges of this task, we show a couple of examples from the blind test set and two terminological expressions from terminology test set in for building our mt systems we used the transformer model @xcite . our strategies to build the competitive mt systems for the task roughly include (i) pseudo in-domain parallel and monolingual data selection, (ii) augmenting training data @xcite @xcite @xcite , (iii) mining (1) no cardiovascular risk factor differences were found in terms of age. (2) congenital tumors show a different pattern than tumors in other pediatric ages. (3) open bite of thyroid gland, sequela (4) poisoning by oxytocic drugs, undetermined, subsequent encounter ## data used this section presents the data sets which were used for system building. we used the bilingual data provided by the wmt20 biomedical shared task organisers only. as for english monolingual corpus, we used all in-domain texts released by the organisers including the english side of the bilingual corpora of the language-pairs. as for basque monolingual data, organisers provided us with a tiny set of in-domain sentences. since the participants are allowed to use external data, we used the commoncrawl foot_0 corpus for basque. since the size of english in-domain monolingual corpus is reasonably big, we did not use any english out-of-domain data for system building. in order to perform tokenisation for english and basque texts, we used the standard tool of the moses toolkit. the development data released by the task organisers contains 2,000 sentences (cf. fourth row of ## experiments and results this section presents the performance of our mt systems in terms of the automatic evaluation metric bleu @xcite . additionally, we performed statistical significance tests using bootstrap resampling methods @xcite . ## conclusion this paper presents the adapt system description for the wmt20 biomedical translation shared task. we participated in the english-to-basque translation task. the task is to translate scientific abstracts and terms from biomedical terminologies. we aimed to build a competitive translation system for this task. for this, we applied various strategies, e.g. selecting monolingual and bilingual texts that are similar to the in-domain data, mining monolingual sentences, applying adaptation technique for adapting the neural mt models to the task, hyperparameters search. we found that our strategies to improve the baseline mt system were effective and yields excellent performance. this paper demonstrated a novel adaptation approach for translating domain texts. this method is found to be effective in this translation task. in the future, we aim to test the on-the-fly adaptation method @xcite to translate domain texts.