@inproceedings{sastre-martinez-etal-2022-generating,
title = "Generating Meaningful Topic Descriptions with Sentence Embeddings and {LDA}",
author = "Sastre Martinez, Javier Miguel and
Gorman, Sean and
Nugent, Aisling and
Pal, Anandita",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.26",
doi = "10.18653/v1/2022.sigdial-1.26",
pages = "244--254",
abstract = "A major part of business operations is interacting with customers. Traditionally this was done by human agents, face to face or over telephone calls within customer support centers. There is now a move towards automation in this field using chatbots and virtual assistants, as well as an increased focus on analyzing recorded conversations to gather insights. Determining the different services that a human agent provides and estimating the incurred call handling costs per service are key to prioritizing service automation. We propose a new technique, ELDA (Embedding based LDA), based on a combination of LDA topic modeling and sentence embeddings, that can take a dataset of customer-agent dialogs and extract key utterances instead of key words. The aim is to provide more meaningful and contextual topic descriptions required for interpreting and labeling the topics, reducing the need for manually reviewing dialog transcripts.",
}
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%0 Conference Proceedings
%T Generating Meaningful Topic Descriptions with Sentence Embeddings and LDA
%A Sastre Martinez, Javier Miguel
%A Gorman, Sean
%A Nugent, Aisling
%A Pal, Anandita
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F sastre-martinez-etal-2022-generating
%X A major part of business operations is interacting with customers. Traditionally this was done by human agents, face to face or over telephone calls within customer support centers. There is now a move towards automation in this field using chatbots and virtual assistants, as well as an increased focus on analyzing recorded conversations to gather insights. Determining the different services that a human agent provides and estimating the incurred call handling costs per service are key to prioritizing service automation. We propose a new technique, ELDA (Embedding based LDA), based on a combination of LDA topic modeling and sentence embeddings, that can take a dataset of customer-agent dialogs and extract key utterances instead of key words. The aim is to provide more meaningful and contextual topic descriptions required for interpreting and labeling the topics, reducing the need for manually reviewing dialog transcripts.
%R 10.18653/v1/2022.sigdial-1.26
%U https://aclanthology.org/2022.sigdial-1.26
%U https://doi.org/10.18653/v1/2022.sigdial-1.26
%P 244-254
Markdown (Informal)
[Generating Meaningful Topic Descriptions with Sentence Embeddings and LDA](https://aclanthology.org/2022.sigdial-1.26) (Sastre Martinez et al., SIGDIAL 2022)
ACL