Position-Aware Tagging for Aspect Sentiment Triplet Extraction

Lu Xu, Hao Li, Wei Lu, Lidong Bing


Abstract
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.
Anthology ID:
2020.emnlp-main.183
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2339–2349
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.183
DOI:
10.18653/v1/2020.emnlp-main.183
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.183.pdf
Optional supplementary material:
 2020.emnlp-main.183.OptionalSupplementaryMaterial.zip