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        <title>Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk</title>
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            <namePart type="given">Samuel</namePart>
            <namePart type="family">Louvan</namePart>
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        <name type="personal">
            <namePart type="given">Andrea</namePart>
            <namePart type="family">Madotto</namePart>
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        <name type="personal">
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    <abstract>The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by the current popular open-domain dialog systems and chatbots. Heavily relying on corpus-based machine learning techniques to encode and decode context-sensitive meanings, these systems focus on fitting a particular training dataset, but not tracking what is actually happening in a conversation, and therefore easily derail in a new context. This work sketches out a more linguistically-informed architecture to handle social talk in English, in which corpus-based methods form the backbone of the relatively context-insensitive components (e.g. part-of-speech tagging, approximation of lexical meaning and constituent chunking), while symbolic modeling is used for reasoning out the context-sensitive components, which do not have any consistent mapping to linguistic forms. All components are fitted into a Bayesian game-theoretic model to address the interactive and rational aspects of conversation.</abstract>
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%0 Conference Proceedings
%T Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk
%A Lưu, Alex
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F luu-2022-sketching
%X The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by the current popular open-domain dialog systems and chatbots. Heavily relying on corpus-based machine learning techniques to encode and decode context-sensitive meanings, these systems focus on fitting a particular training dataset, but not tracking what is actually happening in a conversation, and therefore easily derail in a new context. This work sketches out a more linguistically-informed architecture to handle social talk in English, in which corpus-based methods form the backbone of the relatively context-insensitive components (e.g. part-of-speech tagging, approximation of lexical meaning and constituent chunking), while symbolic modeling is used for reasoning out the context-sensitive components, which do not have any consistent mapping to linguistic forms. All components are fitted into a Bayesian game-theoretic model to address the interactive and rational aspects of conversation.
%R 10.18653/v1/2022.acl-srw.14
%U https://aclanthology.org/2022.acl-srw.14
%U https://doi.org/10.18653/v1/2022.acl-srw.14
%P 153-170

Markdown (Informal)

[Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk](https://aclanthology.org/2022.acl-srw.14) (Lưu, ACL 2022)

• Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (Lưu, ACL 2022)

ACL

• Alex Lưu. 2022. Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 153–170, Dublin, Ireland. Association for Computational Linguistics.