Papers by Jixiang Luo

1 papers
Efficient Hybrid Generation Framework for Aspect-Based Sentiment Analysis (2023.eacl-main)

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Challenge: Aspect-based sentiment analysis (ABSA) has attracted broad commercial attention due to its commercial value.
Approach: They propose a framework that generates location and semantic information in parallel and a global hybrid loss function in combination with bipartite matching to achieve end-to-end model training.
Outcome: The proposed framework outperforms state-of-the-art methods in almost all cases and outperfies existing methods in terms of inference efficiency.

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