论文标题
ETMS@IITKGP在Semeval-2022任务10：使用生成方法的结构化情感分析
ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach
论文作者
论文摘要
结构化的情感分析（SSA）涉及文本中提取意见元素，其中每个元组（H，E，T，P）由H，Holder H组成，H holder，他们通过情感表达式向目标t表达情感极性P向目标t。虽然先前的作品探索了基于图的或基于序列标记的任务方法，但我们在本文中提出了一种新型的统一生成方法来求解SSA，这是SEMEVAL2022共享任务。我们利用基于巴特的编码器架构体系结构，并适当地修改它以生成句子，即一系列意见元组。每个生成的元组分别由七个整数组成，分别代表与持有人，目标和表达式跨度的开始和终端位置相对应的索引，然后是目标和情感表达之间相关的情感极性类别。我们对单语和跨语义子任务进行了严格的实验，并在两种情况下都在排行榜上实现竞争性情感F1分数。
Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment expression e. While prior works explore graph-based or sequence labeling-based approaches for the task, we in this paper present a novel unified generative method to solve SSA, a SemEval2022 shared task. We leverage a BART-based encoder-decoder architecture and suitably modify it to generate, given a sentence, a sequence of opinion tuples. Each generated tuple consists of seven integers respectively representing the indices corresponding to the start and end positions of the holder, target, and expression spans, followed by the sentiment polarity class associated between the target and the sentiment expression. We perform rigorous experiments for both Monolingual and Cross-lingual subtasks, and achieve competitive Sentiment F1 scores on the leaderboard in both settings.
