Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the
triplets of target entities, their associated sentiment, and opinion spans
explaining the reason for the sentiment. Existing research efforts mostly solve
this problem using pipeline approaches, which break the triplet extraction
process into several stages. Our observation is that the three elements within
a triplet are highly related to each other, and this motivates us to build a
joint model to extract such triplets using a sequence tagging approach.
However, how to effectively design a tagging approach to extract the triplets
that can capture the rich interactions among the elements is a challenging
research question. In this work, we propose the first end-to-end model with a
novel position-aware tagging scheme that is capable of jointly extracting the
triplets. Our experimental results on several existing datasets show that
jointly capturing elements in the triplet using our approach leads to improved
performance over the existing approaches. We also conducted extensive
experiments to investigate the model effectiveness and robustness.

本文提出了一种新型的定位感知标注方案，旨在通过序列标注方法达到同时提取目标实体、与其相关的情感和原因的任务，实验证明了这种方法可以改善现有方法的性能。