How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?

Shuoyang Ding; Kevin Duh

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arXiv · 1806.01515

How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?

Shuoyang Ding·Kevin Duh

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Abstract

Using pre-trained word embeddings as input layer is a common practice in many natural language processing (NLP) tasks, but it is largely neglected for neural machine translation (NMT). In this paper, we conducted a systematic analysis on the effect of using pre-trained source-side monolingual word embedding in NMT. We compared several strategies, such as fixing or updating the embeddings during NMT training on varying amounts of data, and we also proposed a novel strategy called dual-embedding that blends the fixing and updating strategies. Our results suggest that pre-trained embeddings can be helpful if properly incorporated into NMT, especially when parallel data is limited or additional in-domain monolingual data is readily available.

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Shuoyang Ding, Kevin Duh. 2018-06-05. How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?. https://arxiv.org/abs/1806.01515

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