An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
This work addresses aspect-based sentiment analysis for natural language processing applications, presenting an incremental improvement over pipeline methods.
The paper tackled the problem of aspect-based sentiment analysis by proposing an interactive multi-task learning network that jointly learns aspect term extraction and sentiment prediction, achieving superior performance on three benchmark datasets.
Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus. In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level. Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.