AAAI Conference 2020 Conference Paper
Latent Relation Language Models
- Hiroaki Hayashi
- Zecong Hu
- Chenyan Xiong
- Graham Neubig
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 word-based language models 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. †