论文标题
一个多任务学习框架，用于三重提取
A Multi-task Learning Framework for Opinion Triplet Extraction
论文作者
论文摘要
最先进的基于方面的情感分析（ABSA）方法主要基于检测方面术语及其相应的情感极性，或者是共同提取方面和意见术语。但是，提取方面态度对缺乏意见术语作为参考，而在不确定他们的情感依赖性的情况下，对方面和意见术语的共摘要不会导致有意义的对。为了解决这个问题，我们将ABSA作为一项意见三重提取任务的新颖观点，并提出了一个多任务学习框架，以共同提取方面术语和意见术语，同时与Biaffine得分手之间的情感依赖性。在推论阶段，三胞胎的提取是通过基于上述输出的三重态解码方法来促进的。我们评估了ASBA的四个半eval基准测试的拟议框架。结果表明，我们的方法显着优于一系列强大的基准和最先进的方法。
The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.
