Xiao Pu


Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
Xiao Pu | Nikolaos Pappas | James Henderson | Andrei Popescu-Belis
Transactions of the Association for Computational Linguistics, Volume 6

This paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words. We first introduce three adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant processes, and random walks, which are then applied to large word contexts represented in a low-rank space and evaluated on SemEval shared-task data. We then learn word vectors jointly with sense vectors defined by our best WSD method, within a state-of-the-art NMT system. We show that the concatenation of these vectors, and the use of a sense selection mechanism based on the weighted average of sense vectors, outperforms several baselines including sense-aware ones. This is demonstrated by translation on five language pairs. The improvements are more than 1 BLEU point over strong NMT baselines, +4% accuracy over all ambiguous nouns and verbs, or +20% when scored manually over several challenging words.


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Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering
Xiao Pu | Nikolaos Pappas | Andrei Popescu-Belis
Proceedings of the Second Conference on Machine Translation

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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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