Neural Machine Translation without Embeddings

Uri Shaham; Omer Levy

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arXiv · 2008.09396

Neural Machine Translation without Embeddings

Uri Shaham·Omer Levy

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Abstract

Many NLP models operate over sequences of subword tokens produced by hand-crafted tokenization rules and heuristic subword induction algorithms. A simple universal alternative is to represent every computerized text as a sequence of bytes via UTF-8, obviating the need for an embedding layer since there are fewer token types (256) than dimensions. Surprisingly, replacing the ubiquitous embedding layer with one-hot representations of each byte does not hurt performance; experiments on byte-to-byte machine translation from English to 10 different languages show a consistent improvement in BLEU, rivaling character-level and even standard subword-level models. A deeper investigation reveals that the combination of embeddingless models with decoder-input dropout amounts to token dropout, which benefits byte-to-byte models in particular.

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Uri Shaham, Omer Levy. 2020-08-21. Neural Machine Translation without Embeddings. https://arxiv.org/abs/2008.09396

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