Challenge: Existing work on exophoric reference resolution for situated dialogs is limited to a literary model . et al., 2010) showed that it is possible to improve dialogic reference resolving by incrementally adapting word semantic model parameters to idiosyncratic language use by dyad partners.
Approach: They propose to use a logistic regression model to adapt a model to idiosyncratic language . they first train a log regression model and then use it to learn the general referring ability of each word .
Outcome: The proposed methods improve dialogic reference resolution without annotation of referring expressions even with little background data.

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Challenge: Recent work on incorporating external knowledge into the response generation models has attracted great interest.
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Challenge: Existing studies on pronoun coreference resolution focus on anaphora and cataphores . exophoric pronounos are common in daily communications, but can be disambiguated by general topics of the dialogue.
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Challenge: Automatic Speech Recognition (ASR) errors in voice-based dialog systems pose significant impediments to downstream tasks.
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Grounding Language in Multi-Perspective Referential Communication (2024.emnlp-main)

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Challenge: Using a dataset of 2,970 human-written referring expressions, we find that the performance of automated models in both reference generation and comprehension lags behind that of pairs of human agents.
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Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts (2020.emnlp-main)

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Challenge: Subsequent references exploit the common ground accumulated by the interlocutors and tend to be shorter and reuse expressions that were effective in previous mentions.
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What kinds of errors do reference resolution models make and what can we learn from them? (2022.findings-naacl)

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Challenge: Referring resolution is the task of identifying the referent of a natural language expression.
Approach: They propose a model that restores weakening of the spatial natural constraints on referring expressions by evaluating their performance on different datasets.
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Disambiguating Reference in Visually Grounded Dialogues through Joint Modeling of Textual and Multimodal Semantic Structures (2025.acl-long)

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Challenge: incorporating textual and multimodal reference resolution improves performance in visual-based reference resolution . Phrase grounding is a well-established task for understanding semantic relations between mentions and objects . ambiguities caused by pronouns and ellipses can arise in visually grounded dialogues .
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Bridging Resolution: A Survey of the State of the Art (2020.coling-main)

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Challenge: bridging resolution is an anaphora resolution task that is less studied than entity coreference resolution.
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Toward Implicit Reference in Dialog: A Survey of Methods and Data (2022.aacl-main)

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Challenge: In natural language, speakers often leave out information that is understood by the other party through the shared context.
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MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation (2020.lrec-1)

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Challenge: a new dataset, MuDoCo, is composed of authored dialogs between a fictional user and a system . the dialogs cross domains and users exhibit complex task switching behavior .
Approach: They propose a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system . they propose two baseline models for the downstream tasks: coreference resolution and referring expression generation.
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