Laura Mascarell


2017

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Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues
Xiao Pu | Laura Mascarell | Andrei Popescu-Belis
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently, i.e. using the same noun in the target text as well. We train and test classifiers that predict consistent translations based on lexical, syntactic, and semantic features. We first evaluate the accuracy of our classifiers intrinsically, in terms of the accuracy of consistency predictions, over a subset of the UN Corpus. Then, we also evaluate them in combination with phrase-based statistical MT systems for Chinese-to-English and German-to-English. We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers’ output, and also use these methods in combination. This improves over the baseline and closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier.

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Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings
Annette Rios Gonzales | Laura Mascarell | Rico Sennrich
Proceedings of the Second Conference on Machine Translation

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Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation
Laura Mascarell
Proceedings of the Third Workshop on Discourse in Machine Translation

Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German-to-English show that our method produces correct translations in up to 88% of the changes, improving the translation in 36%-48% of them over the baseline.

2015

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Detecting Document-level Context Triggers to Resolve Translation Ambiguity
Laura Mascarell | Mark Fishel | Martin Volk
Proceedings of the Second Workshop on Discourse in Machine Translation

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Leveraging Compounds to Improve Noun Phrase Translation from Chinese and German
Xiao Pu | Laura Mascarell | Andrei Popescu-Belis | Mark Fishel | Ngoc-Quang Luong | Martin Volk
Proceedings of the ACL-IJCNLP 2015 Student Research Workshop

2013

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tSEARCH: Flexible and Fast Search over Automatic Translations for Improved Quality/Error Analysis
Meritxell Gonzàlez | Laura Mascarell | Lluís Màrquez
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations