Neural Lattice Language Models (1803.05071v1)

Abstract: In this work, we propose a new LLMing paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice LLMs. 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 LLM. Experiments on multiple LLMing tasks show that English neural lattice LLMs 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.