Improving Dialogue Act Recognition with Augmented Data

Khyati Mahajan, Soham Parikh, Quaizar Vohra, Mitul Tiwari, Samira Shaikh


Abstract
We present our work on augmenting dialog act recognition capabilities utilizing synthetically generated data. Our work is motivated by the limitations of current dialog act datasets, and the need to adapt for new domains as well as ambiguity in utterances written by humans. We list our observations and findings towards how synthetically generated data can contribute meaningfully towards more robust dialogue act recognition models extending to new domains. Our major finding shows that synthetic data, which is linguistically varied, can be very useful towards this goal and increase the performance from (0.39, 0.16) to (0.85, 0.88) for AFFIRM and NEGATE dialog acts respectively.
Anthology ID:
2022.gem-1.44
Volume:
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Antoine Bosselut, Khyathi Chandu, Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, Yacine Jernite, Jekaterina Novikova, Laura Perez-Beltrachini
Venue:
GEM
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
471–479
Language:
URL:
https://aclanthology.org/2022.gem-1.44
DOI:
10.18653/v1/2022.gem-1.44
Bibkey:
Cite (ACL):
Khyati Mahajan, Soham Parikh, Quaizar Vohra, Mitul Tiwari, and Samira Shaikh. 2022. Improving Dialogue Act Recognition with Augmented Data. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 471–479, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Improving Dialogue Act Recognition with Augmented Data (Mahajan et al., GEM 2022)
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