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Cross-Domain Review Generation for Aspect-Based Sentiment Analysis ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.421 Abstract: Supervised learning methods have proven to be effective for Aspect-Based Sentiment Analysis (ABSA). However, the lack of fine-grained labeled data hinders their effectiveness in many domains. To address this issue, unsupervised domain adaptation methods are desired to transfer knowledge from a labeled source domain to any unlabeled target domain. In this paper, we propose a new domain adaptation paradigm called cross-domain review generation (CDRG), which aims to generate target-domain reviews with fine-grained annotation based on the source-domain labeled reviews. To achieve this goal, we propose a two-step approach as a concrete realization of CDRG. It first converts a source-domain review to a domain-independent review by masking its source-specific attributes, and then converts the domain-independent review to a target-domain review with a masked language model pre-trained in the target domain. We further propose two ways to ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Neural Network; Semantics
URL: https://underline.io/lecture/26512-cross-domain-review-generation-for-aspect-based-sentiment-analysis
https://dx.doi.org/10.48448/na2g-dx08
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Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions ...
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