Character-Word LSTM Language Models

Lyan Verwimp; Joris Pelemans; Hugo Van hamme; Patrick Wambacq

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arXiv · 1704.02813

Character-Word LSTM Language Models

Lyan Verwimp·Joris Pelemans·Hugo Van hamme·Patrick Wambacq

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Abstract

We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77% relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters.

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Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq. 2017-04-10. Character-Word LSTM Language Models. https://arxiv.org/abs/1704.02813

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