MARRS: Multimodal Reference Resolution System
Abstrak
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.
Penulis (18)
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
Akses Cepat
- Tahun Terbit
- 2023
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓