Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.

翻译：摘要：角度情感三元组提取（ASTE）已成为情感分析研究中的新兴任务，旨在从给定的句子中提取方面术语、其相应的观点术语和其相关情感极性的三元组。最近，许多基于神经网络的模型采用了不同的标记方案，但它们几乎都有其局限性：严重依赖1）每个单词仅与一个角色（例如，方面术语或观点术语等）相关的先验假设和2）单词级交互并将每个观点/方面视为独立单词集。因此，在复杂的ASTE任务中（如一个单词与多个角色相关或一个包含多个单词的方面/观点术语），它们的表现较差。因此，我们提出了一种新方法，跨度标记和贪婪推理（STAGE），以在跨度级别提取情感三元组，其中每个跨度可能由多个单词组成，同时起多种角色。为此，本文将ASTE任务形式化为多类跨度分类问题。具体而言，STAGE通过探索跨度级别的信息和约束来生成更准确的角度情感三元组提取，由跨度标记方案和贪心推理策略两个组成。前者基于新定义的标记集对所有可能的候选跨度进行标记。后者从候选情感语块中检索具有最大长度的方面/观点术语以输出情感三元组。此外，我们提出了一个基于STAGE的简单而有效的模型，它在四个广泛使用的数据集上的表现明显优于现有技术。此外，我们的STAGE可以轻松推广到其他配对/三元组提取任务，这也展示了所提出的方案STAGE的优越性。