Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages (original) (raw)

Elhuyar submission to the Biomedical Translation Task 2020 on terminology and abstracts translation

2020

This article describes the systems submitted by Elhuyar to the 2020 Biomedical Translation Shared Task, specifically the systems presented in the subtasks of terminology translation for English-Basque and abstract translation for English-Basque and English-Spanish. In all cases a Transformer architecture was chosen and we studied different strategies to combine open domain data with biomedical domain data for building the training corpora. For the English-Basque pair, given the scarcity of parallel corpora in the biomedical domain, we set out to create domain training data in a synthetic way. The systems presented in the terminology and abstract translation subtasks for the English-Basque language pair ranked first in their respective tasks among four participants, achieving 0.78 accuracy for terminology translation and a BLEU of 0.1279 for the translation of abstracts. In the abstract translation task for the English-Spanish pair our team ranked second (BLEU=0.4498) in the case of ...

UoS Participation in the WMT20 Translation of Biomedical Abstracts

2020

This paper describes the machine translation systems developed by the University of Sheffield (UoS) team for the biomedical translation shared task of WMT20. Our system is based on a Transformer model with TensorFlow Model Garden toolkit. We participated in ten translation directions for the English/Spanish, English/Portuguese, English/Russian, English/Italian, and English/French language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources.

Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set

2021

In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque. Further, our tests were composed of three types of textual test sets. New to this year, we released a test set of summaries of animal experiments, in addition to the test sets of scientific abstracts and terminologies. We received a total of 107 submissions from 15 teams from 6 countries.

BSC Participation in the WMT Translation of Biomedical Abstracts

Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

This paper describes the machine translation systems developed by the Barcelona Supercomputing (BSC) team for the biomedical translation shared task of WMT19. Our system is based on Neural Machine Translation unsing the OpenNMT-py toolkit and Transformer architecture. We participated in four translation directions for the English/Spanish and English/Portuguese language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources, as well as terminological resources from UMLS.

Ixamed’s submission description for WMT20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation

2020

In this paper we describe the systems developed at Ixa for our participation in WMT20 Biomedical shared task in three language pairs, en-eu, en-es and es-en. When defining our approach, we have put the focus on making an efficient use of corpora recently compiled for training Machine Translation (MT) systems to translate Covid-19 related text, as well as reusing previously compiled corpora and developed systems for biomedical or clinical domain. Regarding the techniques used, we base on the findings from our previous works for translating clinical texts into Basque, making use of clinical terminology for adapting the MT systems to the clinical domain. However, after manually inspecting some of the outputs generated by our systems, for most of the submissions we end up using the system trained only with the basic corpus, since the systems including the clinical terminologies generated outputs shorter in length than the corresponding references. Thus, we present simple baselines for t...

FJWU participation for the WMT20 Biomedical Translation Task

2020

This paper reports system descriptions for FJWU-NRPU team for participation in the WMT20 Biomedical shared translation task. We focused our submission on exploring the effects of adding in-domain corpora extracted from various out-of-domain sources. Systems were built for French to English using in-domain corpora through fine tuning and selective data training. We further explored BERT based models specifically with focus on effect of domain adaptive subword units.

Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT

PloS one, 2024

Background Non-English speaking researchers may find it difficult to write articles in English and may be tempted to use machine translators (MTs) to facilitate their task. We compared the performance of DeepL, Google Translate, and CUBBITT for the translation of abstracts from French to English. Methods We selected ten abstracts published in 2021 in two high-impact bilingual medical journals (CMAJ and Canadian Family Physician) and used nine metrics of Recall-Oriented Understudy for Gisting Evaluation (ROUGE-1 recall/precision/F1-score, ROUGE-2 recall/precision/F1-score, and ROUGE-L recall/precision/F1-score) to evaluate the accuracy of the translation (scores ranging from zero to one [= maximum]). We also used the fluency score assigned by ten raters to evaluate the stylistic quality of the translation (ranging from ten [= incomprehensible] to fifty [= flawless English]). We used Kruskal-Wallis tests to compare the medians between the three MTs. For the human evaluation, we also examined the original English text. Results Differences in medians were not statistically significant for the nine metrics of ROUGE (medians: min-max = 0.5246-0.7392 for DeepL, 0.4634-0.7200 for Google Translate, 0.4815-0.7316 for CUBBITT, all p-values > 0.10). For the human evaluation, CUBBITT tended to score higher than DeepL, Google Translate, and the original English text (median = 43 for CUBBITT, vs. 39, 38, and 40, respectively, p-value = 0.003). Conclusion The three MTs performed similarly when tested with ROUGE, but CUBBITT was slightly better than the other two using human evaluation. Although we only included abstracts and did

IXA Biomedical Translation System at WMT16 Biomedical Translation Task

Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers, 2016

In this paper we present the system developed at the IXA NLP Group of the University of the Basque Country for the Biomedical Translation Task in the First Conference on Machine Translation (WMT16). For the adaptation of a statistical machine translation system to the biomedical domain, we developed three approaches based on a baseline system for English-Spanish and Spanish-English language pairs. The lack of terminology and the variation of the prominent sense of the words are the issues we have addressed on these approaches. The best of our systems reached the average of all the systems submitted in the challenge in most of the evaluation sets.

FJWU Participation for the WMT21 Biomedical Translation Task

2021

In this paper we present the FJWU’s system submitted to the biomedical shared task at WMT21. We prepared state-of-the-art multilingual neural machine translation systems for three languages (i.e. German, Spanish and French) with English as target language. Our NMT systems based on Transformer architecture, were trained on combination of in-domain and out-domain parallel corpora developed using Information Retrieval (IR) and domain adaptation techniques.