Data and Model Distillation as a Solution for Domain-transferable Fact Verification

Mitch Paul Mithun, Sandeep Suntwal, Mihai Surdeanu


Abstract
While neural networks produce state-of-the-art performance in several NLP tasks, they generally depend heavily on lexicalized information, which transfer poorly between domains. We present a combination of two strategies to mitigate this dependence on lexicalized information in fact verification tasks. We present a data distillation technique for delexicalization, which we then combine with a model distillation method to prevent aggressive data distillation. We show that by using our solution, not only does the performance of an existing state-of-the-art model remain at par with that of the model trained on a fully lexicalized data, but it also performs better than it when tested out of domain. We show that the technique we present encourages models to extract transferable facts from a given fact verification dataset.
Anthology ID:
2021.naacl-main.360
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4546–4552
Language:
URL:
https://aclanthology.org/2021.naacl-main.360
DOI:
10.18653/v1/2021.naacl-main.360
Bibkey:
Cite (ACL):
Mitch Paul Mithun, Sandeep Suntwal, and Mihai Surdeanu. 2021. Data and Model Distillation as a Solution for Domain-transferable Fact Verification. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4546–4552, Online. Association for Computational Linguistics.
Cite (Informal):
Data and Model Distillation as a Solution for Domain-transferable Fact Verification (Mithun et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.360.pdf
Video:
 https://aclanthology.org/2021.naacl-main.360.mp4
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