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arXiv · 1804.08313

Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

Diego Marcheggiani·Jasmijn Bastings·Ivan Titov
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Abstract

Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English--German language pair.

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Diego Marcheggiani, Jasmijn Bastings, Ivan Titov. 2018-04-23. Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. https://arxiv.org/abs/1804.08313

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