Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks
| Autor: | Sparsh Gupta, Vitor R. Carvalho, Anush Kamath |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Matching (graph theory)
Computer science business.industry 05 social sciences 010501 environmental sciences computer.software_genre Base (topology) 01 natural sciences Domain (software engineering) Adversarial system 0502 economics and business Data deduplication Reversing Artificial intelligence 050207 economics business Textual entailment Adaptation (computer science) computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | ACL (1) |
| DOI: | 10.18653/v1/p19-1556 |
| Popis: | Adversarial domain adaptation has been recently proposed as an effective technique for textual matching tasks, such as question deduplication. Here we investigate the use of gradient reversal on adversarial domain adaptation to explicitly learn both shared and unshared (domain specific) representations between two textual domains. In doing so, gradient reversal learns features that explicitly compensate for domain mismatch, while still distilling domain specific knowledge that can improve target domain accuracy. We evaluate reversing gradients for adversarial adaptation on multiple domains, and demonstrate that it significantly outperforms other methods on question deduplication as well as on recognizing textual entailment (RTE) tasks, achieving up to 7% absolute boost in base model accuracy on some datasets. |
| Databáze: | OpenAIRE |
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