Improving Downstream Task Performance by Treating Numbers as Entities
This addresses the issue of numeracy in NLP for tasks requiring number understanding, but it is incremental as it builds on existing models.
The paper tackled the problem of improving NLP model performance on downstream tasks by classifying numbers as entities, resulting in outperforming BERT and RoBERTa baselines on tasks like Fill-In-The-Blank and question answering.
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.