Papers with ABSA) task

2 papers
Unsupervised Aspect-Level Sentiment Controllable Style Transfer (2020.aacl-main)

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Challenge: Unsupervised style transfer has been explored in text.
Approach: They propose a system where aspect-level sentiments can be controlled at the output . they propose to use unsupervised techniques such as ABSA masked-language-modelling .
Outcome: The proposed system is successful in controlling aspect-level sentiments.
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)

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Challenge: Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks.
Approach: They propose a framework that allows model parameter transfer, not data transfer, between different domains.
Outcome: The proposed framework performs competitively with traditional unsupervised domain adaptation methods under privacy conditions.

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