@inproceedings{karan-etal-2021-mitigating,
title = "Mitigating Topic Bias when Detecting Decisions in Dialogue",
author = "Karan, Mladen and
Khare, Prashant and
Healey, Patrick and
Purver, Matthew",
editor = "Li, Haizhou and
Levow, Gina-Anne and
Yu, Zhou and
Gupta, Chitralekha and
Sisman, Berrak and
Cai, Siqi and
Vandyke, David and
Dethlefs, Nina and
Wu, Yan and
Li, Junyi Jessy",
booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2021",
address = "Singapore and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigdial-1.56",
doi = "10.18653/v1/2021.sigdial-1.56",
pages = "542--547",
abstract = "This work revisits the task of detecting decision-related utterances in multi-party dialogue. We explore performance of a traditional approach and a deep learning-based approach based on transformer language models, with the latter providing modest improvements. We then analyze topic bias in the models using topic information obtained by manual annotation. Our finding is that when detecting some types of decisions in our data, models rely more on topic specific words that decisions are about rather than on words that more generally indicate decision making. We further explore this by removing topic information from the train data. We show that this resolves the bias issues to an extent and, surprisingly, sometimes even boosts performance.",
}
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<abstract>This work revisits the task of detecting decision-related utterances in multi-party dialogue. We explore performance of a traditional approach and a deep learning-based approach based on transformer language models, with the latter providing modest improvements. We then analyze topic bias in the models using topic information obtained by manual annotation. Our finding is that when detecting some types of decisions in our data, models rely more on topic specific words that decisions are about rather than on words that more generally indicate decision making. We further explore this by removing topic information from the train data. We show that this resolves the bias issues to an extent and, surprisingly, sometimes even boosts performance.</abstract>
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%0 Conference Proceedings
%T Mitigating Topic Bias when Detecting Decisions in Dialogue
%A Karan, Mladen
%A Khare, Prashant
%A Healey, Patrick
%A Purver, Matthew
%Y Li, Haizhou
%Y Levow, Gina-Anne
%Y Yu, Zhou
%Y Gupta, Chitralekha
%Y Sisman, Berrak
%Y Cai, Siqi
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Wu, Yan
%Y Li, Junyi Jessy
%S Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2021
%8 July
%I Association for Computational Linguistics
%C Singapore and Online
%F karan-etal-2021-mitigating
%X This work revisits the task of detecting decision-related utterances in multi-party dialogue. We explore performance of a traditional approach and a deep learning-based approach based on transformer language models, with the latter providing modest improvements. We then analyze topic bias in the models using topic information obtained by manual annotation. Our finding is that when detecting some types of decisions in our data, models rely more on topic specific words that decisions are about rather than on words that more generally indicate decision making. We further explore this by removing topic information from the train data. We show that this resolves the bias issues to an extent and, surprisingly, sometimes even boosts performance.
%R 10.18653/v1/2021.sigdial-1.56
%U https://aclanthology.org/2021.sigdial-1.56
%U https://doi.org/10.18653/v1/2021.sigdial-1.56
%P 542-547
Markdown (Informal)
[Mitigating Topic Bias when Detecting Decisions in Dialogue](https://aclanthology.org/2021.sigdial-1.56) (Karan et al., SIGDIAL 2021)
ACL
- Mladen Karan, Prashant Khare, Patrick Healey, and Matthew Purver. 2021. Mitigating Topic Bias when Detecting Decisions in Dialogue. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 542–547, Singapore and Online. Association for Computational Linguistics.