Semantic and syntactic information for neural machine translation: Injecting features to the transformer

Cita com:
hdl:2117/347441
Document typeArticle
Defense date2021-05-18
Rights accessOpen Access
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Attribution 4.0 International
Abstract
Introducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored machine translation has proven to still be useful in the case of sequence-to-sequence systems. In this work, we investigate whether this gains hold in the case of the state-of-the-art architecture in neural machine translation, the Transformer, instead of recurrent architectures. We propose a new model, the Factored Transformer, to introduce an arbitrary number of word features in the source sequence in an attentional system. Specifically, we suggest two variants depending on the level at which the features are injected. Moreover, we suggest two combination mechanisms for the word features and words themselves. We experiment both with classical linguistic features and semantic features extracted from a linked data database, and with two low-resource datasets. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both low-resource and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information.
CitationArmengol, J.; Costa-jussà, M.R. Semantic and syntactic information for neural machine translation: Injecting features to the transformer. "Machine translation", 18 Maig 2021, vol. 35, p. 3-17.
ISSN0922-6567
Publisher versionhttps://link.springer.com/article/10.1007/s10590-021-09264-2
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