The SPECTRANS System Description for the WMT 22 Biomedical Task (2022)

Abstract: This paper describes the SPECTRANS submission 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 toolkits 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 selection of texts from WMT, Khresmoi and UFAL data sets. We report our results and assess the respective merits of the different translated texts.

## introduction for this wmt22 biomedical workshop, we focused 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. 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. ## 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 models : 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 performances and to better understand functioning of in-domain nmt training. ## experiments and results in our experiments, we aimed to compare the different joeynmt models (baseline and finetuning) that we have trained with systran model. joeynmt, which is based on trans-former @xcite , requires lighter implementations than opennmt @xcite . ## conclusion this paper presents the spectrans system description for the wmt 2022 biomedical shared task. we participated in the english-to-french and french-to-english tasks. we only used the data provided by the organisers but also analysed the translations produced with mbart. we obviously concur with previous research that training data is key. for the mt system, we applied a variety of strategies, toolkit comparison and fine-tuning to compare outcomes of different nmt systems in biomedical translation. our contribution mostly lies in the textometric analysis of the output. this allowed us to raise the issue of the role of the variability observed for the gender of covid in french or for technical terms like "keloids".