SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction
He Zhao, Longtao Huang, Rong Zhang, Quan Lu, Hui Xue · 2020
Aspect terms extraction and opinion terms extraction are two key problems of fine-grained Aspect Based Sentiment Analysis (ABSA).The aspect-opinion pairs can provide a global profile about a product or service for consumers and opinion mining systems.However, traditional methods can not directly output aspect-opinion pairs without given aspect terms or opinion terms.Although some recent co-extraction methods have been proposed to extract both terms jointly, they fail to extract them as pairs.To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE).Furthermore, this paper treats the problem from a perspective of joint term and relation extraction rather than under the sequence tagging formulation performed in most prior works.We propose a multi-task learning framework based on shared spans, where the terms are extracted under the supervision of span boundaries.Meanwhile, the pair-wise relations are jointly identified using the span representations.Extensive experiments show that our model consistently outperforms stateof-the-art methods.