ICM : Intent and Conversational Mining from Conversation Logs

Sayantan Mitra; Roshni Ramnani; Sumit Ranjan; Shubhashis Sengupta

doi:10.18653/v1/2022.sigdial-1.39

ICM : Intent and Conversational Mining from Conversation Logs

Sayantan Mitra, Roshni Ramnani, Sumit Ranjan, Shubhashis Sengupta

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Abstract

Building conversation agents requires a large amount of manual effort in creating training data for intents / entities as well as mapping out extensive conversation flows. In this demonstration, we present ICM (Intent and conversation Mining), a tool which can be used to analyze existing conversation logs and help a bot designer analyze customer intents, train a custom intent model as well as map and optimize conversation flows. The tool can be used for first time deployment or subsequent deployments of chatbots.

Anthology ID:: 2022.sigdial-1.39
Volume:: Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:: September
Year:: 2022
Address:: Edinburgh, UK
Editors:: Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
Venue:: SIGDIAL
SIG:: SIGDIAL
Publisher:: Association for Computational Linguistics
Note:
Pages:: 403–406
Language:
URL:: https://aclanthology.org/2022.sigdial-1.39/
DOI:: 10.18653/v1/2022.sigdial-1.39
Bibkey:
Cite (ACL):: Sayantan Mitra, Roshni Ramnani, Sumit Ranjan, and Shubhashis Sengupta. 2022. ICM : Intent and Conversational Mining from Conversation Logs. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 403–406, Edinburgh, UK. Association for Computational Linguistics.
Cite (Informal):: ICM : Intent and Conversational Mining from Conversation Logs (Mitra et al., SIGDIAL 2022)
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PDF:: https://aclanthology.org/2022.sigdial-1.39.pdf
Video:: https://youtu.be/xnFqxU_8c8A

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@inproceedings{mitra-etal-2022-icm,
    title = "{ICM} : Intent and Conversational Mining from Conversation Logs",
    author = "Mitra, Sayantan  and
      Ramnani, Roshni  and
      Ranjan, Sumit  and
      Sengupta, Shubhashis",
    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-1.39/",
    doi = "10.18653/v1/2022.sigdial-1.39",
    pages = "403--406",
    abstract = "Building conversation agents requires a large amount of manual effort in creating training data for intents / entities as well as mapping out extensive conversation flows. In this demonstration, we present ICM (Intent and conversation Mining), a tool which can be used to analyze existing conversation logs and help a bot designer analyze customer intents, train a custom intent model as well as map and optimize conversation flows. The tool can be used for first time deployment or subsequent deployments of chatbots."
}

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%0 Conference Proceedings
%T ICM : Intent and Conversational Mining from Conversation Logs
%A Mitra, Sayantan
%A Ramnani, Roshni
%A Ranjan, Sumit
%A Sengupta, Shubhashis
%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 mitra-etal-2022-icm
%X Building conversation agents requires a large amount of manual effort in creating training data for intents / entities as well as mapping out extensive conversation flows. In this demonstration, we present ICM (Intent and conversation Mining), a tool which can be used to analyze existing conversation logs and help a bot designer analyze customer intents, train a custom intent model as well as map and optimize conversation flows. The tool can be used for first time deployment or subsequent deployments of chatbots.
%R 10.18653/v1/2022.sigdial-1.39
%U https://aclanthology.org/2022.sigdial-1.39/
%U https://doi.org/10.18653/v1/2022.sigdial-1.39
%P 403-406

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Markdown (Informal)

[ICM : Intent and Conversational Mining from Conversation Logs](https://aclanthology.org/2022.sigdial-1.39/) (Mitra et al., SIGDIAL 2022)

ICM : Intent and Conversational Mining from Conversation Logs (Mitra et al., SIGDIAL 2022)

ACL

Sayantan Mitra, Roshni Ramnani, Sumit Ranjan, and Shubhashis Sengupta. 2022. ICM : Intent and Conversational Mining from Conversation Logs. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 403–406, Edinburgh, UK. Association for Computational Linguistics.