The SPECTRANS System Description for the WMT22 Biomedical Task

Proceedings of the Seventh Conference on Machine Translation (WMT), pages 895 - 900 December 7-8, 2022 ©2022 Association for Computational Linguistics The SPECTRANS System Description for the WMT22 Biomedical Task Nicolas Ballier1, Jean-Baptiste Yunès2, Guillaume Wisniewski3, Lichao Zhu3, Maria Zimina-Poirot1 1CLILLAC-ARP, 2IRIF, 3LLF Université Paris Cité, F-75013 Paris, France {nicolas.ballier, guillaume.wisniewski, jean-baptiste.yunes, lichao.zhu, maria.zimina-poirot}@u-paris.fr Abstract This paper describes the SPECTRANS sub- mission for the WMT 2022 biomedical shared task. We present the results of our experiments using the training corpora and the JoeyNMT (Kreutzer et al., 2019) and SYSTRAN Pure Neural Server/ Advanced Model Studio toolk- its for the language directions English to French and French to English. We compare the pre- dictions of the different toolkits. We also use JoeyNMT to fine-tune the model with a selec- tion of texts from WMT, Khresmoi and UFAL data sets. We report our results and assess the respective merits of the different translated texts. 1 Introduction For this WMT22 Biomedical workshop, we fo- cused on the selection of texts used for fine-tuning. We selected what we believe to be the two best models we produced for the EN-FR track with two different neural toolkits but we mostly took the opportunity to discuss the translated texts. The rest of the paper is organised as follows: Section 2 summarises our approaches to the task, Section 3 details the training data of our experiments, Section 4 presents the results. Section 5 discusses them. 2 Our Approaches to the Task This section presents our various strategies for this task and our four submissions. We compared the predictions of two toolkits but our comparison is very partial as the training data differs. We trained several systems with JoeyNMT (Kreutzer et al., 2019) training and fine-tuning with UFAL, WMT and Khresmoi data. We used the SYSTRAN Pure Neural® Server generic system and tried to fine- tune with specialised terminology. We used SYS- TRAN Advanced Model Studio® to fine-tune a generic model with in-house data based on 2,700 aligned segments collected during the translation of the French federation for diabetes.1 Table 1 sum- marises our submissions. With JoeyNMT, we selected the training data, comparing the performance with and without the added data and applied fine-tuning to the model based on UFAL medical corpora The following section details the model selection and fine-tuning. 3 Data and Tools Used In this section, we present different approaches that we adopted to train baseline models and proceed to fine-tuning. We have built two baseline mod- els : one trained with generic data set fine-tuned with in-domain data, and the other trained directly with in-domain data, in order to compare their per- formances and to better understand functioning of in-domain NMT training. 3.1 Data for baseline models training We used two baseline models : the first one is built based on our model submitted for WMT 2021. It took the Europarl 7 parallel corpus as data set trained with 341,554 sentences in two directions (EN⇔FR)(Ballier et al., 2021); the second one has been built …

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