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arXiv · 2311.01650

MARRS: Multimodal Reference Resolution System

Halim Cagri Ates·Shruti Bhargava·Site Li·Jiarui Lu·Siddhardha Maddula·Joel Ruben Antony Moniz·Anil Kumar Nalamalapu·Roman Hoang Nguyen·Melis Ozyildirim·Alkesh Patel·Dhivya Piraviperumal·Vincent Renkens·Ankit Samal·Thy Tran·Bo-Hsiang Tseng·Hong Yu·Yuan Zhang·Rong Zou
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Abstract

Successfully handling context is essential for any dialog understanding task. This context maybe be conversational (relying on previous user queries or system responses), visual (relying on what the user sees, for example, on their screen), or background (based on signals such as a ringing alarm or playing music). In this work, we present an overview of MARRS, or Multimodal Reference Resolution System, an on-device framework within a Natural Language Understanding system, responsible for handling conversational, visual and background context. In particular, we present different machine learning models to enable handing contextual queries; specifically, one to enable reference resolution, and one to handle context via query rewriting. We also describe how these models complement each other to form a unified, coherent, lightweight system that can understand context while preserving user privacy.

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Halim Cagri Ates, Shruti Bhargava, Site Li, Jiarui Lu, Siddhardha Maddula, Joel Ruben Antony Moniz, Anil Kumar Nalamalapu, Roman Hoang Nguyen, Melis Ozyildirim, Alkesh Patel, Dhivya Piraviperumal, Vincent Renkens, Ankit Samal, Thy Tran, Bo-Hsiang Tseng, Hong Yu, Yuan Zhang, Rong Zou. 2023-11-03. MARRS: Multimodal Reference Resolution System. https://doi.org/10.18653/v1/2023.crac-main.7

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