Papers by Daniell Wang

1 papers
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (2022.findings-acl)

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Challenge: Existing text semantic matching models do not provide granularity for text comparison.
Approach: They propose a simple yet effective training strategy for text semantic matching by disentangling keywords from intents.
Outcome: The proposed approach achieves stable performance improvements against a wide range of models on three benchmarks.

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