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Predicting Target Language CCG Supertags Improves Neural Machine Translation

Authors: Nadejde, Maria; Reddy, Siva; Sennrich, Rico; Dwojak, Tomasz; Junczys-Dowmunt, Marcin; Koehn, Philipp; Birch, Alexandra;

Predicting Target Language CCG Supertags Improves Neural Machine Translation

Abstract

Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling target language syntax help NMT? 2) Is tight integration of words and syntax better than multitask training? We introduce syntactic information in the form of CCG supertags in the decoder, by interleaving the target supertags with the word sequence. Our results on WMT data show that explicitly modeling target-syntax improves machine translation quality for German->English, a high-resource pair, and for Romanian->English, a low-resource pair and also several syntactic phenomena including prepositional phrase attachment. Furthermore, a tight coupling of words and syntax improves translation quality more than multitask training. By combining target-syntax with adding source-side dependency labels in the embedding layer, we obtain a total improvement of 0.9 BLEU for German->English and 1.2 BLEU for Romanian->English.

Accepted at the Second Conference on Machine Translation (WMT17). This version includes more results regarding target syntax for Romanian->English and reports fewer results regarding source syntax

Country
United Kingdom
Subjects by Vocabulary

Microsoft Academic Graph classification: Sequence Dependency (UML) Interleaving Machine translation Computer science business.industry Speech recognition Translation (geometry) computer.software_genre Syntax Artificial intelligence Layer (object-oriented design) business computer Natural language processing Word (computer architecture)

Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)

26 references, page 1 of 3

Roee Aharoni and Yoav Goldberg. 2017. Towards string-to-tree neural machine translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vancouver, Canada. Association for Computational Linguistics.

Bharat Ram Ambati, Tejaswini Deoskar, and Mark Steedman. 2016. Hindi CCGbank: CCG Treebank from the Hindi Dependency Treebank. In Language Resources and Evaluation.

Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016. Globally normalized transition-based neural networks. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2442-2452, Berlin, Germany. Association for Computational Linguistics.

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations (ICLR).

Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016. Neural versus phrasebased machine translation quality: a case study. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pages 257-267. [OpenAIRE]

Alexandra Birch, Miles Osborne, and Philipp Koehn. 2007. Ccg supertags in factored statistical machine translation. In Proceedings of the Second Workshop on Statistical Machine Translation, StatMT '07, pages 9-16, Stroudsburg, PA, USA. Association for Computational Linguistics.

Ondˇrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016. Findings of the 2016 conference on machine translation. In Proceedings of the First Conference on Machine Translation, pages 131- 198, Berlin, Germany. Association for Computational Linguistics. [OpenAIRE]

David Chiang. 2007. Hierarchical phrase-based translation. Comput. Linguist., 33(2):201-228.

Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014a. On the properties of neural machine translation: Encoder-decoder approaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 103-111, Doha, Qatar. Association for Computational Linguistics.

Kyunghyun Cho, Bart van Merrie¨nboer, C¸a g˘lar Gu¨lc¸ehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014b. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724- 1734, Doha, Qatar. Association for Computational Linguistics.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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