Papers with feature-based adaptation

2 papers
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)

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Challenge: Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task.
Approach: They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features .
Outcome: The proposed method improves on the state-of-the-art in two aspects of the ABSA task.
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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