Paraphrases as Foreign Languages in Multilingual Neural Machine Translation
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Using paraphrases, the expression of the same semantic meaning in different words, to improve generalization and translation performance is often useful. However, prior works only explore the use of paraphrases at the word or phrase level, not at the sentence or document level. Unlike previous works, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. Our corpus contains parallel paraphrases in multiple languages from various sources. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train in the style of multilingual Neural Machine Translation (NMT). Experimental results indicate that adding paraphrases improves the rare word translation, increases entropy and diversity in lexical choice. Moreover, adding the source paraphrases improves translation performance more effectively than adding the target paraphrases. Combining both the source and the target paraphrases boosts performance further; combining paraphrases with multilingual data also helps but has mixed performance. We achieve a BLEU score of 57.2 for French-to-English translation, training on 24 paraphrases of the Bible, which is +27 above the WMT'14 baseline.
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