Pre-training Language Models for Comparative Reasoning
Submission Type: Regular Long Paper
Submission Track: Natural Language Generation
Submission Track 2: NLP Applications
Keywords: language model pre-training, question answering, question generation, summarization, comparative reasoning
TL;DR: This paper introduces a novel framework to pre-train language models for enhancing the ability of comparison reasoning over texts, and the proposed method shows significant improvement on tasks including comparative QA, QG and summarization.
Abstract: Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability. In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts. While there have been approaches for NLP tasks that require comparative reasoning, they suffer from costly manual data labeling and limited generalizability to different tasks. Our approach introduces a novel method of collecting scalable data for text-based entity comparison, which leverages both structured and unstructured data. Moreover, we present a framework of pre-training language models via three novel objectives on comparative reasoning. Evaluation on downstream tasks including comparative question answering, question generation, and summarization shows that our pre-training framework significantly improves the comparative reasoning abilities of language models, especially under low-resource conditions. This work also releases the first integrated benchmark for comparative reasoning.
Submission Number: 4089
Loading