@inproceedings{konigari-etal-2021-topic,
title = "Topic Shift Detection for Mixed Initiative Response",
author = "Konigari, Rachna and
Ramola, Saurabh and
Alluri, Vijay Vardhan and
Shrivastava, Manish",
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-1.17",
doi = "10.18653/v1/2021.sigdial-1.17",
pages = "161--166",
abstract = "Topic diversion occurs frequently with engaging open-domain dialogue systems like virtual assistants. The balance between staying on topic and rectifying the topic drift is important for a good collaborative system. In this paper, we present a model which uses a fine-tuned XLNet-base to classify the utterances pertaining to the major topic of conversation and those which are not, with a precision of 84{\%}. We propose a preliminary study, classifying utterances into major, minor and off-topics, which further extends into a system initiative for diversion rectification. A case study was conducted where a system initiative is emulated as a response to the user going off-topic, mimicking a common occurrence of mixed initiative present in natural human-human conversation. This task of classifying utterances into those which belong to the major theme or not, would also help us in identification of relevant sentences for tasks like dialogue summarization and information extraction from conversations.",
}
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<abstract>Topic diversion occurs frequently with engaging open-domain dialogue systems like virtual assistants. The balance between staying on topic and rectifying the topic drift is important for a good collaborative system. In this paper, we present a model which uses a fine-tuned XLNet-base to classify the utterances pertaining to the major topic of conversation and those which are not, with a precision of 84%. We propose a preliminary study, classifying utterances into major, minor and off-topics, which further extends into a system initiative for diversion rectification. A case study was conducted where a system initiative is emulated as a response to the user going off-topic, mimicking a common occurrence of mixed initiative present in natural human-human conversation. This task of classifying utterances into those which belong to the major theme or not, would also help us in identification of relevant sentences for tasks like dialogue summarization and information extraction from conversations.</abstract>
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%0 Conference Proceedings
%T Topic Shift Detection for Mixed Initiative Response
%A Konigari, Rachna
%A Ramola, Saurabh
%A Alluri, Vijay Vardhan
%A Shrivastava, Manish
%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 konigari-etal-2021-topic
%X Topic diversion occurs frequently with engaging open-domain dialogue systems like virtual assistants. The balance between staying on topic and rectifying the topic drift is important for a good collaborative system. In this paper, we present a model which uses a fine-tuned XLNet-base to classify the utterances pertaining to the major topic of conversation and those which are not, with a precision of 84%. We propose a preliminary study, classifying utterances into major, minor and off-topics, which further extends into a system initiative for diversion rectification. A case study was conducted where a system initiative is emulated as a response to the user going off-topic, mimicking a common occurrence of mixed initiative present in natural human-human conversation. This task of classifying utterances into those which belong to the major theme or not, would also help us in identification of relevant sentences for tasks like dialogue summarization and information extraction from conversations.
%R 10.18653/v1/2021.sigdial-1.17
%U https://aclanthology.org/2021.sigdial-1.17
%U https://doi.org/10.18653/v1/2021.sigdial-1.17
%P 161-166
Markdown (Informal)
[Topic Shift Detection for Mixed Initiative Response](https://aclanthology.org/2021.sigdial-1.17) (Konigari et al., SIGDIAL 2021)
ACL
- Rachna Konigari, Saurabh Ramola, Vijay Vardhan Alluri, and Manish Shrivastava. 2021. Topic Shift Detection for Mixed Initiative Response. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 161–166, Singapore and Online. Association for Computational Linguistics.