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        <title>Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification</title>
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        <namePart type="given">Ayesha</namePart>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
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    <abstract>Utterances do not occur in isolation in dialogues; it is essential to have the information of who the speaker of an utterance is to be able to recover the speaker’s intention with respect to the surrounding context. Beyond simply capturing speaker switches, identifying how speakers interact with each other in a dialogue is crucial to understanding conversational flow. This becomes increasingly important and simultaneously difficult to model when more than two interlocutors take part in a conversation. To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation. To that end, we use a graph neural network to model how each speaker is behaving within the local context of a conversation. The speaker representations learned this way are then used to update their respective utterance representations. We experiment with both multiparticipant and dyadic conversations on the MRDA and SwDA datasets and show the effectiveness of our approach.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.678</identifier>
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%0 Conference Proceedings
%T Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification
%A Qamar, Ayesha
%A Pyarelal, Adarsh
%A Huang, Ruihong
%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 qamar-etal-2023-speaking
%X Utterances do not occur in isolation in dialogues; it is essential to have the information of who the speaker of an utterance is to be able to recover the speaker’s intention with respect to the surrounding context. Beyond simply capturing speaker switches, identifying how speakers interact with each other in a dialogue is crucial to understanding conversational flow. This becomes increasingly important and simultaneously difficult to model when more than two interlocutors take part in a conversation. To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation. To that end, we use a graph neural network to model how each speaker is behaving within the local context of a conversation. The speaker representations learned this way are then used to update their respective utterance representations. We experiment with both multiparticipant and dyadic conversations on the MRDA and SwDA datasets and show the effectiveness of our approach.
%R 10.18653/v1/2023.findings-emnlp.678
%U https://aclanthology.org/2023.findings-emnlp.678
%U https://doi.org/10.18653/v1/2023.findings-emnlp.678
%P 10122-10135

Markdown (Informal)

[Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification](https://aclanthology.org/2023.findings-emnlp.678) (Qamar et al., Findings 2023)

• Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification (Qamar et al., Findings 2023)

ACL

• Ayesha Qamar, Adarsh Pyarelal, and Ruihong Huang. 2023. Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10122–10135, Singapore. Association for Computational Linguistics.