Enhancing Review Comprehension with Domain-Specific Commonsense

TL;DR

This paper introduces xSense, a system that leverages inexpensive, domain-specific commonsense knowledge bases to significantly improve review comprehension tasks such as aspect extraction, sentiment classification, and question answering.

Contribution

The paper presents a novel approach to constructing domain-specific commonsense knowledge bases and demonstrates their effectiveness in enhancing review comprehension models.

Findings

xSense outperforms state-of-the-art models in aspect extraction and sentiment classification.

xSense significantly improves question answering performance over baseline BERT models.

Publicly released knowledge bases and benchmarks facilitate future research.

Abstract

Review comprehension has played an increasingly important role in improving the quality of online services and products and commonsense knowledge can further enhance review comprehension. However, existing general-purpose commonsense knowledge bases lack sufficient coverage and precision to meaningfully improve the comprehension of domain-specific reviews. In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs). We show that xSense KBs can be constructed inexpensively and present a knowledge distillation method that enables us to use xSense KBs along with BERT to boost the performance of various review comprehension tasks. We evaluate xSense over three review comprehension tasks: aspect extraction, aspect sentiment classification, and question answering. We find that xSense outperforms the state-of-the-art models for the first two tasks and improves the baseline BERT QA model significantly, demonstrating the usefulness of incorporating commonsense into review comprehension pipelines. To facilitate future research and applications, we publicly release three domain-specific knowledge bases and a domain-specific question answering benchmark along with this paper.