Melis Ozyildirim
2024
ReALM: Reference Resolution as Language Modeling
Joel Ruben Antony Moniz
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Soundarya Krishnan
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Melis Ozyildirim
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Prathamesh Saraf
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Halim Cagri Ates
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Yuan Zhang
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Hong Yu
Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Reference resolution is an important problem, one that is essential to understand and successfully handle contexts of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user’s screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.
2023
MARRS: Multimodal Reference Resolution System
Halim Cagri Ates
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Shruti Bhargava
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Site Li
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Jiarui Lu
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Siddhardha Maddula
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Joel Ruben Antony Moniz
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Anil Kumar Nalamalapu
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Roman Hoang Nguyen
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Melis Ozyildirim
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Alkesh Patel
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Dhivya Piraviperumal
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Vincent Renkens
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Ankit Samal
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Thy Tran
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Bo-Hsiang Tseng
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Hong Yu
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Yuan Zhang
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Shirley Zou
Proceedings of The Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023)
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Co-authors
- Joel Ruben Antony Moniz 2
- Halim Cagri Ates 2
- Yuan Zhang 2
- Hong Yu 2
- Soundarya Krishnan 1
- show all...