English-Portuguese Biomedical Translation Task Using a Genuine
            Phrase-Based Statistical Machine Translation Approach

Aires, José; Lopes, Gabriel Pereira; Gomes, Luís

doi:10.18653/v1/w16-2335

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…Aires et al, 2016) IXA University of the Basque Country UPV/EHU, Spain (Perez-de Viñaspre and Labaka, 2016) LIMSI-TLP LIMSI, France (Ive et al, 2016) TALP-UPC Universitat Politècnica de Catalunya, Spain (Costa-jussà et al, 2016) uedin University of Edinburgh, UK(Williams et al, 2016) …”

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confidence: 99%

Findings of the 2016 Conference on Machine Translation

Bojar¹,

Chatterjee²,

Federmann³

et al. 2016

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

420

358

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Findings of the 2016 Conference on Machine Translation (WMT16) Bojar, O.; Chatterjee, R.; Federmann, C.; Graham, Y.; Haddow, B.; Huck, M.; Jimeno Yepes, A.; Koehn, P.; Logacheva, V.; Monz, C.; Negri, M.; Névéol, A.; Neves, M.; Popel, M.; Post, M.; Rubino, R.; Scarton, C.; Specia, L.; Turchi, M.; Verspoor, K.; Zampieri, M.Abstract This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments).The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.1 http://statmt.org/wmt16/results.html 2

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mentioning

confidence: 99%

Findings of the 2016 Conference on Machine Translation

Bojar¹,

Chatterjee²,

Federmann³

et al. 2016

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

420

358

View full text Add to dashboard Cite

show abstract

“…However, their goal was to assess the benefits of multilingual modeling by clustering languages and is different from that of a multi-output translation system. For English-Portuguese, Aires et al (2016) build a phrasebased machine translation system to translate biomedical texts. For multilingual parahrasing, Ganitkevitch and Callison-Burch (2014) release a database consisting of paraphrases for several languages, including Hungarian and Portuguese, at lexical, phrasal and syntactic level.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous paraphrasing and translation by fine-tuning Transformer models

Chada¹

2020

Proceedings of the Fourth Workshop on Neural Generation and Translation

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This paper describes the third place submission to the shared task on simultaneous translation and paraphrasing for language education at the 4th workshop on Neural Generation and Translation (WNGT) for ACL 2020. The final system leverages pre-trained translation models and uses a Transformer architecture combined with an oversampling strategy to achieve a competitive performance. This system significantly outperforms the baseline on Hungarian (27% absolute improvement in Weighted Macro F1 score) and Portuguese (33% absolute improvement) languages.

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“…However, their goal was to assess the benefits of multilingual modeling by clustering languages and is different from that of a multi-output translation system. For English-Portuguese, Aires et al (2016) build a phrasebased machine translation system to translate biomedical texts. For multilingual parahrasing, release a database consisting of paraphrases for several languages, including Hungarian and Portuguese, at lexical, phrasal and syntactic level.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Fourth Workshop on Neural Generation and Translation

2020

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We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.1 BLEU+case.mixed+lang.en-de+numrefs.1+s mooth.exp+test.wmt * +tok.13a+version.1.4.8 for various WMT test sets 2 Participants are likely to have used these test sets in development. The WMT 2020 test set was not yet available and others were out of the domain the systems were trained for.

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English-Portuguese Biomedical Translation Task Using a Genuine Phrase-Based Statistical Machine Translation Approach

Abstract: Our approach to produce translations for the ACL-2016 Biomedical Translation Task on the English-Portuguese language pair, in both directions, is described. Own preliminary tests results and final results, measured by the shared task organizers, are also presented.

Cited by 7 publications

References 10 publications

Findings of the 2016 Conference on Machine Translation

Findings of the 2016 Conference on Machine Translation

Simultaneous paraphrasing and translation by fine-tuning Transformer models

Proceedings of the Fourth Workshop on Neural Generation and Translation

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