Improving Downstream Task Performance by Treating Numbers as Entities
Computation and Language
2022-09-20 v2 Machine Learning
Abstract
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not accounted for distinctly in most NLP tasks, there is still an underlying amount of numeracy already exhibited by NLP models. In this work, we attempt to tap this potential of state-of-the-art NLP models and transfer their ability to boost performance in related tasks. Our proposed classification of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering using joint embeddings, outperforming the BERT and RoBERTa baseline classification.
Cite
@article{arxiv.2205.03559,
title = {Improving Downstream Task Performance by Treating Numbers as Entities},
author = {Dhanasekar Sundararaman and Vivek Subramanian and Guoyin Wang and Liyan Xu and Lawrence Carin},
journal= {arXiv preprint arXiv:2205.03559},
year = {2022}
}
Comments
Accepted to CIKM 2022