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：方面情感三元组抽取（ASTE）已成为情感分析研究中的新兴任务，旨在从给定句子中抽取由方面词、其对应观点词及相关情感极性构成的三元组。近年来，基于不同标记方案且采用多种神经网络的模型被相继提出，但几乎所有模型均存在局限性：严重依赖于1）每个词仅关联单一角色（如方面词或观点词等）的先验假设；2）词级交互，并将每个观点/方面视为独立词的集合。因此，这些模型在处理复杂ASTE任务时表现不佳，例如涉及多角色关联词或多词构成方面/观点词的情况。为此，我们提出一种名为“跨度标记与贪婪推理（STAGE）”的新型方法，在跨度级别上抽取情感三元组——每个跨度可能由多个词构成并同时扮演不同角色。基于此，本文将ASTE任务形式化为多类别跨度分类问题。具体而言，STAGE通过探索跨度级信息与约束实现更精准的方面情感三元组抽取，其包含两个核心组件：跨度标记方案与贪婪推理策略。前者基于新定义的标记集对所有可能候选跨度进行标注，后者则从候选情感片段中选取最大长度的方面/观点词以输出情感三元组。此外，我们提出一种基于STAGE的简洁有效模型，在四个广泛使用的数据集上以显著优势超越现有最优方法。同时，我们的STAGE可轻松泛化至其他配对/三元组抽取任务，进一步证明了所提方案STAGE的优越性。