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arXiv · 1803.05071

Neural Lattice Language Models

Jacob Buckman·Graham Neubig
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Abstract

In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions - including polysemy and existence of multi-word lexical items - into our language model. Experiments on multiple language modeling tasks show that English neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95% relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94% relative to a character-level baseline.

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BibTeXRIS

Jacob Buckman, Graham Neubig. 2018-03-13. Neural Lattice Language Models. https://arxiv.org/abs/1803.05071

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