LIMSI $@$ WMT’14 Medical Translation Task

Pécheux, Nicolas; Gong, Li; Khanh, Quoc; Marie, Benjamin; Ivanishcheva, Yulia; Allauzen, Alexander; Lee, Thomas; Niehues, Jan; Max, Aurélien; Yvon, François

doi:10.3115/v1/w14-3330

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…• SOUL models: SOUL models are structured output layer neural network language models (LMs) which have been shown to be useful in reranking tasks, for instance for WMT evaluations (Allauzen et al, 2013;Pécheux et al, 2014). SOUL scoring being too costly to be integrated during decoding, it fits perfectly the reranker scenario, which furthermore enables to use larger contexts for n-grams.…”

Section: Reranking and Featuresmentioning

confidence: 99%

Confidence-based Rewriting of Machine Translation Output

Marie

Max

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Numerous works in Statistical Machine Translation (SMT) have attempted to identify better translation hypotheses obtained by an initial decoding using an improved, but more costly scoring function. In this work, we introduce an approach that takes the hypotheses produced by a state-ofthe-art, reranked phrase-based SMT system, and explores new parts of the search space by applying rewriting rules selected on the basis of posterior phraselevel confidence. In the medical domain, we obtain a 1.9 BLEU improvement over a reranked baseline exploiting the same scoring function, corresponding to a 5.4 BLEU improvement over the original Moses baseline. We show that if an indication of which phrases require rewriting is provided, our automatic rewriting procedure yields an additional improvement of 1.5 BLEU. Various analyses, including a manual error analysis, further illustrate the good performance and potential for improvement of our approach in spite of its simplicity.

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Section: Reranking and Featuresmentioning

confidence: 99%

Confidence-based Rewriting of Machine Translation Output

Marie

Max

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Enabling Medical Translation for Low-Resource Languages

Musleh¹,

Durrani²,

Temnikova³

et al. 2018

Computational Linguistics and Intelligent Text Processing

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We present research towards bridging the language gap between migrant workers in Qatar and medical staff. In particular, we present the first steps towards the development of a real-world Hindi-English machine translation system for doctor-patient communication. As this is a low-resource language pair, especially for speech and for the medical domain, our initial focus has been on gathering suitable training data from various sources. We applied a variety of methods ranging from fully automatic extraction from the Web to manual annotation of test data. Moreover, we developed a method for automatically augmenting the training data with synthetically generated variants, which yielded a very sizable improvement of more than 3 BLEU points absolute.

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Findings of the 2014 Workshop on Statistical Machine Translation

Bojar

Buck

Federmann

et al. 2014

Proceedings of the Ninth Workshop on Statistical Machine Translation

555

543

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This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries.

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LIMSI $@$ WMT’14 Medical Translation Task

Cited by 3 publications

References 20 publications

Confidence-based Rewriting of Machine Translation Output

Confidence-based Rewriting of Machine Translation Output

Enabling Medical Translation for Low-Resource Languages

Findings of the 2014 Workshop on Statistical Machine Translation

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