mlsanityData to 14 Oct 2026

Latent Relation Language Models

arXiv 1908.07690 ↗First posted 21 Aug 2019cs.CLcited by 14

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Aug 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 7; 2022: 3; 2023: 3; 2024: 1.

14 papers.

Sort: