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        <title>Novel Feature Discovery for Task-Oriented Dialog Systems</title>
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        <namePart type="given">Vinh</namePart>
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    <name type="personal">
        <namePart type="given">Abdalghani</namePart>
        <namePart type="family">Abujabal</namePart>
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    <abstract>A novel feature represents a cluster of semantically equivalent novel user requests e.g., requests to play a song on a service or read user’s messages. Detecting and supporting novel features is crucial towards wider adoption of dialog systems by end users. Intuitively, features are represented by a combination of intents, slot types and/or their values. For example, while playing a song is a feature represented by a single intent (PlayMusic) only, playing a song on a service is another feature represented by the combination of PlayMusic intent and ServiceName slot type. Prior work on novelty detection limits the scope of features to those represented by novel single intents, leading to (1) giant clusters spanning several user-perceived fine-grained features belonging to the same intent, (2) incoherent interpretation of clusters from users’ perspective (no direct connection to some user-perceived feature), and (3) missing those features spanning several intents. In this work, we introduce feature discovery as opposed to single intent discovery, which aims at discovering novel features spanning a combination of intents and slots, and present a technique for discovering novel features from user utterances. Experiments on two datasets demonstrate the effectiveness of our approach and consistently show its ability to detect novel features.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.59</identifier>
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        <date>2023-05</date>
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            <start>782</start>
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%0 Conference Proceedings
%T Novel Feature Discovery for Task-Oriented Dialog Systems
%A Ho, Vinh Thinh
%A Soliman, Mohamed
%A Abujabal, Abdalghani
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F ho-etal-2023-novel
%X A novel feature represents a cluster of semantically equivalent novel user requests e.g., requests to play a song on a service or read user’s messages. Detecting and supporting novel features is crucial towards wider adoption of dialog systems by end users. Intuitively, features are represented by a combination of intents, slot types and/or their values. For example, while playing a song is a feature represented by a single intent (PlayMusic) only, playing a song on a service is another feature represented by the combination of PlayMusic intent and ServiceName slot type. Prior work on novelty detection limits the scope of features to those represented by novel single intents, leading to (1) giant clusters spanning several user-perceived fine-grained features belonging to the same intent, (2) incoherent interpretation of clusters from users’ perspective (no direct connection to some user-perceived feature), and (3) missing those features spanning several intents. In this work, we introduce feature discovery as opposed to single intent discovery, which aims at discovering novel features spanning a combination of intents and slots, and present a technique for discovering novel features from user utterances. Experiments on two datasets demonstrate the effectiveness of our approach and consistently show its ability to detect novel features.
%R 10.18653/v1/2023.findings-eacl.59
%U https://aclanthology.org/2023.findings-eacl.59
%U https://doi.org/10.18653/v1/2023.findings-eacl.59
%P 782-792

Markdown (Informal)

[Novel Feature Discovery for Task-Oriented Dialog Systems](https://aclanthology.org/2023.findings-eacl.59) (Ho et al., Findings 2023)

• Novel Feature Discovery for Task-Oriented Dialog Systems (Ho et al., Findings 2023)

ACL

• Vinh Thinh Ho, Mohamed Soliman, and Abdalghani Abujabal. 2023. Novel Feature Discovery for Task-Oriented Dialog Systems. In Findings of the Association for Computational Linguistics: EACL 2023, pages 782–792, Dubrovnik, Croatia. Association for Computational Linguistics.