@inproceedings{song-etal-2023-pcmid,
title = "{PCMID}: Multi-Intent Detection through Supervised Prototypical Contrastive Learning",
author = "Song, Yurun and
Zhao, Junchen and
Koehler, Spencer and
Abdullah, Amir and
Harris, Ian",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.636",
doi = "10.18653/v1/2023.findings-emnlp.636",
pages = "9481--9495",
abstract = "Intent detection is a major task in Natural Language Understanding (NLU) and is the component of dialogue systems for interpreting users{'} intentions based on their utterances. Many works have explored detecting intents by assuming that each utterance represents only a single intent. Such systems have achieved very good results; however, intent detection is a far more challenging task in typical real-world scenarios, where each user utterance can be highly complex and express multiple intents. Therefore, in this paper, we propose PCMID, a novel Multi-Intent Detection framework enabled by Prototypical Contrastive Learning under a supervised setting. The PCMID model can learn multiple semantic representations of a given user utterance under the context of different intent labels in an optimized semantic space. Our experiments show that PCMID achieves the current state-of-the-art performance on both multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="song-etal-2023-pcmid">
<titleInfo>
<title>PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yurun</namePart>
<namePart type="family">Song</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junchen</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Spencer</namePart>
<namePart type="family">Koehler</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amir</namePart>
<namePart type="family">Abdullah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ian</namePart>
<namePart type="family">Harris</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
</titleInfo>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Juan</namePart>
<namePart type="family">Pino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kalika</namePart>
<namePart type="family">Bali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Singapore</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Intent detection is a major task in Natural Language Understanding (NLU) and is the component of dialogue systems for interpreting users’ intentions based on their utterances. Many works have explored detecting intents by assuming that each utterance represents only a single intent. Such systems have achieved very good results; however, intent detection is a far more challenging task in typical real-world scenarios, where each user utterance can be highly complex and express multiple intents. Therefore, in this paper, we propose PCMID, a novel Multi-Intent Detection framework enabled by Prototypical Contrastive Learning under a supervised setting. The PCMID model can learn multiple semantic representations of a given user utterance under the context of different intent labels in an optimized semantic space. Our experiments show that PCMID achieves the current state-of-the-art performance on both multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task.</abstract>
<identifier type="citekey">song-etal-2023-pcmid</identifier>
<identifier type="doi">10.18653/v1/2023.findings-emnlp.636</identifier>
<location>
<url>https://aclanthology.org/2023.findings-emnlp.636</url>
</location>
<part>
<date>2023-12</date>
<extent unit="page">
<start>9481</start>
<end>9495</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning
%A Song, Yurun
%A Zhao, Junchen
%A Koehler, Spencer
%A Abdullah, Amir
%A Harris, Ian
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F song-etal-2023-pcmid
%X Intent detection is a major task in Natural Language Understanding (NLU) and is the component of dialogue systems for interpreting users’ intentions based on their utterances. Many works have explored detecting intents by assuming that each utterance represents only a single intent. Such systems have achieved very good results; however, intent detection is a far more challenging task in typical real-world scenarios, where each user utterance can be highly complex and express multiple intents. Therefore, in this paper, we propose PCMID, a novel Multi-Intent Detection framework enabled by Prototypical Contrastive Learning under a supervised setting. The PCMID model can learn multiple semantic representations of a given user utterance under the context of different intent labels in an optimized semantic space. Our experiments show that PCMID achieves the current state-of-the-art performance on both multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task.
%R 10.18653/v1/2023.findings-emnlp.636
%U https://aclanthology.org/2023.findings-emnlp.636
%U https://doi.org/10.18653/v1/2023.findings-emnlp.636
%P 9481-9495
Markdown (Informal)
[PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning](https://aclanthology.org/2023.findings-emnlp.636) (Song et al., Findings 2023)
ACL