@inproceedings{mahajan-etal-2022-improving,
title = "Improving Dialogue Act Recognition with Augmented Data",
author = "Mahajan, Khyati and
Parikh, Soham and
Vohra, Quaizar and
Tiwari, Mitul and
Shaikh, Samira",
editor = "Bosselut, Antoine and
Chandu, Khyathi and
Dhole, Kaustubh and
Gangal, Varun and
Gehrmann, Sebastian and
Jernite, Yacine and
Novikova, Jekaterina and
Perez-Beltrachini, Laura",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gem-1.44",
doi = "10.18653/v1/2022.gem-1.44",
pages = "471--479",
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.",
}
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%0 Conference Proceedings
%T Improving Dialogue Act Recognition with Augmented Data
%A Mahajan, Khyati
%A Parikh, Soham
%A Vohra, Quaizar
%A Tiwari, Mitul
%A Shaikh, Samira
%Y Bosselut, Antoine
%Y Chandu, Khyathi
%Y Dhole, Kaustubh
%Y Gangal, Varun
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Novikova, Jekaterina
%Y Perez-Beltrachini, Laura
%S Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F mahajan-etal-2022-improving
%X 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.
%R 10.18653/v1/2022.gem-1.44
%U https://aclanthology.org/2022.gem-1.44
%U https://doi.org/10.18653/v1/2022.gem-1.44
%P 471-479
Markdown (Informal)
[Improving Dialogue Act Recognition with Augmented Data](https://aclanthology.org/2022.gem-1.44) (Mahajan et al., GEM 2022)
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.