ASTE-Data-V2

Aspect Sentiment Triplet Extraction 的基准数据集，ASTE-Data-V1 的更新版本。Aspect Sentiment Triplet Extraction (ASTE)
是提取目标实体的三元组、它们相关联的情绪和解释情绪原因的观点跨度的任务。现有的研究工作主要使用管道方法解决这个问题，将三元组提取过程分为几个阶段。我们的观察是三元组中的三个元素彼此高度相关，这促使我们建立一个联合模型来使用序列标记方法提取这样的三元组。然而，如何有效地设计一种标记方法来提取能够捕捉元素之间丰富交互的三元组是一个具有挑战性的研究问题。在这项工作中，我们提出了第一个具有新颖位置感知标记方案的端到端模型，该模型能够联合
提取三元组。我们对几个现有数据集的实验结果表明，使用我们的方法联合捕获三元组中的元素可以提高现有方法的性能。我们还进行了广泛的实验来研究
模型的有效性和鲁棒性。

This is an updated version of ASTE-Data-V1, the benchmark dataset for Aspect Sentiment Triplet Extraction (ASTE). Aspect Sentiment Triplet Extraction (ASTE) refers to the task of extracting triplets consisting of target entities, their associated sentiments, and opinion spans that explain the causes of the sentiments. Existing research works mainly address this task via pipeline approaches, which split the triplet extraction process into multiple separate stages. Our observation that the three elements in a triplet are highly correlated with each other motivates us to develop a joint model that uses sequence labeling methods to extract such triplets. However, how to effectively design a labeling method to extract triplets that can capture the rich interactions between elements remains a challenging research problem. In this work, we propose the first end-to-end model equipped with a novel position-aware labeling scheme, which enables the joint extraction of triplets. Our experimental results on several existing datasets demonstrate that jointly capturing the elements within a triplet via our proposed method can outperform existing approaches. We also conduct extensive experiments to investigate the effectiveness and robustness of the proposed model.

搜集汇总

背景与挑战

背景概述

ASTE-Data-V2是一个用于情感三元组提取（ASTE）任务的基准数据集，是ASTE-Data-V1的更新版本，专注于从文本中联合提取目标实体、相关情绪和观点跨度的三元组。该数据集支持端到端模型和位置感知标记方案的研究，旨在提高三元组提取的准确性和鲁棒性，由新加坡科技设计大学、字节跳动和阿里巴巴达摩学院于2021年发布。