Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

TL;DR

This paper introduces SAECON, a sentiment analysis enhanced network that improves comparative preference classification by incorporating sentiment and semantic information through domain adaptive knowledge transfer, outperforming existing methods.

Contribution

SAECON is the first to integrate sentiment analysis with domain adaptive knowledge transfer for CPC, significantly boosting accuracy over prior models.

Findings

SAECON achieves higher F1 scores on CompSent-19 dataset.

Sentiment integration improves CPC performance.

Domain adaptive transfer enhances model robustness.

Abstract

We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendations. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We proposed sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC ac-curacy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.