Challenge: Traditionally, anaphora resolution and ellipses resolution are limited in dialogues . despite rapid progress in dialogue systems, several difficulties remain .
Approach: They propose a joint learning framework for modeling coreference resolution and query rewriting for complex, multi-turn dialogues.
Outcome: The proposed model outperforms the state-of-the-art model on a rewritten dialogue dataset.

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GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue (D19-1)

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Challenge: Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues.
Approach: They propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model . the model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user .
Outcome: The proposed model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user . intrinsic evaluations on the resolution of ellipsis and co-reference show that the model outperforms the baseline model in terms of EM, BLEU and F1 .
End-to-End Neural Discourse Deixis Resolution in Dialogue (2022.emnlp-main)

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Challenge: Lexical overlap is a strong indicator of entity coreference, both among names and in the resolution of nominals.
Approach: They propose to extend their span-based entity coreference model to exploit task-specific characteristics of discourse deixis resolution in dialogue.
Outcome: The proposed model achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.
What Did You Refer to? Evaluating Co-References in Dialogue (2021.findings-acl)

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Challenge: Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context.
Approach: They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering.
Outcome: The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset.
Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition (2023.emnlp-main)

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Challenge: Existing approaches to learn dialogue discourse parsing with related tasks require additional annotation, thus limiting their generality.
Approach: They propose a multitasking framework that integrates dialogue discourse parsing with addressee recognition to reflect relation-based structure of dialogue.
Outcome: The proposed framework outperforms baselines on the Molweni and STAC datasets.
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)

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Challenge: Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem.
Approach: They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content .
Outcome: The proposed model outperforms baseline models on both SAMSum and DialSum datasets.
Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)

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Challenge: Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues.
Approach: They propose a method to reconstruct utterance with omitted information and pronouns to be standalone and complete based on context.
Outcome: The proposed method improves existing models and achieves state-of-the-art on three benchmarks.
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 .
Approach: They propose a framework that unifies textual and multimodal reference resolution by mapping mention embeddings to object embeddements and selecting mentions or objects based on their similarity.
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Ellipsis Resolution as Question Answering: An Evaluation (2021.eacl-main)

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Challenge: Existing models for ellipsis resolution in English are expensive and cumbersome . ellipas are hard, open problems in NLP, and can cause errors in translation, question answering, and dialogue understanding.
Approach: They propose an alternative approach to ellipsis resolution based on question answering architectures.
Outcome: The proposed model outperforms the current state of the art for ellipsis resolution in English . it shows that annotations can be useful for a subset of the known ellipas .
CoQAR: Question Rewriting on CoQA (2022.lrec-1)

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Challenge: Existing systems that ask questions in a conversational context may have contextual dependencies that make the understanding difficult.
Approach: They propose to rewrite questions into an out-of-context form to facilitate understanding . they propose to use this form to train and evaluate conversational question answering models .
Outcome: The proposed model can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering.
Coreference Resolution through a seq2seq Transition-Based System (2023.tacl-1)

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Challenge: Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference.
Approach: They propose a text-to-text coreference resolution system that uses a semantic paradigm to predict mentions and links jointly.
Outcome: The proposed system achieves state-of-the-art accuracy on CoNLL-2012 datasets with 83.3 F1-score for English, 68.5 F1 score for Arabic, and 74.3 F1 scores for Chinese.

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