text: 103,45,974255416875147264,185910194,Graham Neubig,"Posted ""Neural Lattice Language Models"", our new paper (TACL) on LMs that calculate probability of a sentence by marginalizing over a lattice! <LINK> It's a nice and flexible framework for LMs that lets you consider ambiguity such as word sense, segmentation, etc <LINK>",https://arxiv.org/abs/1803.05071,"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. ",Neural Lattice Language Models,1,"['Posted ""Neural Lattice Language Models"", our new paper (TACL) on LMs that calculate probability of a sentence by marginalizing over a lattice! <LINK>\nIt\'s a nice and flexible framework for LMs that lets you consider ambiguity such as word sense, segmentation, etc <LINK>']",18,03,270