Md Rizwan Parvez, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang

ACL 2018

Links

Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2\% better perplexity in recipe generation and 40.3\% on code generation than state-of-the-art language models.

Bib entry

@inproceedings{PCRC18,
author = {Md Rizwan Parvez and Saikat Chakraborty and Baishakhi Ray and Kai-Wei Chang},
title = {Building Language Models for Text with Named Entities},
booktitle = {ACL},
year = {2018},
}