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arXiv · 1908.07690

Latent Relation Language Models

Hiroaki Hayashi·Zecong Hu·Chenyan Xiong·Graham Neubig
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Abstract

In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entities that occur therein via knowledge graph relations. This model has a number of attractive properties: it not only improves language modeling performance, but is also able to annotate the posterior probability of entity spans for a given text through relations. Experiments demonstrate empirical improvements over both a word-based baseline language model and a previous approach that incorporates knowledge graph information. Qualitative analysis further demonstrates the proposed model's ability to learn to predict appropriate relations in context.

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Hiroaki Hayashi, Zecong Hu, Chenyan Xiong, Graham Neubig. 2019-08-21. Latent Relation Language Models. https://arxiv.org/abs/1908.07690

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