Mimicking Word Embeddings using Subword RNNs

Yuval Pinter; Robert Guthrie; Jacob Eisenstein

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arXiv · 1707.06961

Mimicking Word Embeddings using Subword RNNs

Yuval Pinter·Robert Guthrie·Jacob Eisenstein

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Abstract

Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised character-based model in low-resource settings.

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BibTeX RIS

Yuval Pinter, Robert Guthrie, Jacob Eisenstein. 2017-07-21. Mimicking Word Embeddings using Subword RNNs. https://arxiv.org/abs/1707.06961

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