Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction

TL;DR

This paper introduces a transfer learning approach that leverages abundant review sentiment data to improve target-oriented opinion words extraction, addressing data scarcity issues in TOWE.

Contribution

It proposes a novel transfer learning model that effectively transfers latent opinion knowledge from sentiment classification datasets to TOWE, enhancing performance.

Findings

Outperforms state-of-the-art methods in TOWE

Significantly improves results over baseline models without transfer

Validates effectiveness through extensive experiments

Abstract

Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.