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    <titleInfo>
        <title>Intent Detection and Zero-shot Intent Classification for Chatbots</title>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Pawar</namePart>
            <namePart type="family">Jyoti</namePart>
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    <abstract>In this paper we give in detail how seen and unseen intent is detected and classified. User intent detection has a critical role in dialogue systems. While analysing the intents it has been found that intents are diversely expressed and new variety of intents emerge continuously. Here we propose a capsule-based approach that classifies the intent and a zero-shot learning to identify the unseen intent. There are recently proposed methods on zero-shot classification which are implemented differently from ours. We have also developed an annotated corpus of free conversations in Tamil, the language we have used for intent classification and for our chatbot. Our proposed method on intent classification performs well.</abstract>
    <identifier type="citekey">sobha-pattabhi-2023-intent</identifier>
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        <url>https://aclanthology.org/2023.icon-1.61</url>
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        <date>2023-12</date>
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%0 Conference Proceedings
%T Intent Detection and Zero-shot Intent Classification for Chatbots
%A Sobha, Lalitha Devi
%A Pattabhi, RK. Rao
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F sobha-pattabhi-2023-intent
%X In this paper we give in detail how seen and unseen intent is detected and classified. User intent detection has a critical role in dialogue systems. While analysing the intents it has been found that intents are diversely expressed and new variety of intents emerge continuously. Here we propose a capsule-based approach that classifies the intent and a zero-shot learning to identify the unseen intent. There are recently proposed methods on zero-shot classification which are implemented differently from ours. We have also developed an annotated corpus of free conversations in Tamil, the language we have used for intent classification and for our chatbot. Our proposed method on intent classification performs well.
%U https://aclanthology.org/2023.icon-1.61
%P 636-640

Markdown (Informal)

[Intent Detection and Zero-shot Intent Classification for Chatbots](https://aclanthology.org/2023.icon-1.61) (Sobha & Pattabhi, ICON 2023)

• Intent Detection and Zero-shot Intent Classification for Chatbots (Sobha & Pattabhi, ICON 2023)

ACL

• Lalitha Devi Sobha and RK. Rao Pattabhi. 2023. Intent Detection and Zero-shot Intent Classification for Chatbots. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 636–640, Goa University, Goa, India. NLP Association of India (NLPAI).