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        <title>Multimodal Contextualized Semantic Parsing from Speech</title>
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            <namePart type="given">Andre</namePart>
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    <abstract>We introduce Semantic Parsing in Contextual Environments (SPICE), a task designed to enhance artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts. SPICE goes beyond traditional semantic parsing by offering a structured, interpretable framework for dynamically updating an agent‘s knowledge with new information, mirroring the complexity of human communication. We develop the VG-SPICE dataset, crafted to challenge agents with visual scene graph construction from spoken conversational exchanges, highlighting speech and visual data integration. We also present the Audio-Vision Dialogue Scene Parser (AViD-SP) developed for use on VG-SPICE. These innovations aim to improve multimodal information processing and integration. Both the VG-SPICE dataset and the AViD-SP model are publicly available.</abstract>
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%0 Conference Proceedings
%T Multimodal Contextualized Semantic Parsing from Speech
%A Voas, Jordan
%A Harwath, David
%A Mooney, Ray
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F voas-etal-2024-multimodal
%X We introduce Semantic Parsing in Contextual Environments (SPICE), a task designed to enhance artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts. SPICE goes beyond traditional semantic parsing by offering a structured, interpretable framework for dynamically updating an agent‘s knowledge with new information, mirroring the complexity of human communication. We develop the VG-SPICE dataset, crafted to challenge agents with visual scene graph construction from spoken conversational exchanges, highlighting speech and visual data integration. We also present the Audio-Vision Dialogue Scene Parser (AViD-SP) developed for use on VG-SPICE. These innovations aim to improve multimodal information processing and integration. Both the VG-SPICE dataset and the AViD-SP model are publicly available.
%R 10.18653/v1/2024.acl-long.398
%U https://aclanthology.org/2024.luhme-long.398/
%U https://doi.org/10.18653/v1/2024.acl-long.398
%P 7354-7369

Markdown (Informal)

[Multimodal Contextualized Semantic Parsing from Speech](https://aclanthology.org/2024.luhme-long.398/) (Voas et al., ACL 2024)

• Multimodal Contextualized Semantic Parsing from Speech (Voas et al., ACL 2024)

ACL

• Jordan Voas, David Harwath, and Ray Mooney. 2024. Multimodal Contextualized Semantic Parsing from Speech. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7354–7369, Bangkok, Thailand. Association for Computational Linguistics.