@inproceedings{hayashi2020latent,
author = {Hayashi, Hiroaki and Hu, Zecong and Xiong, Chenyan and Neubig, Graham},
title = {Latent Relation Language Models},
organization = {AAAI},
booktitle = {AAAI},
year = {2020},
month = {February},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/latent-relation-language-models/},
}