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.
Outcome: The proposed dataset contains 8,429 dialogs with an average of 5.36 turns per dialog . the users exhibit complex task switching behavior such as re-initiating a previous task .

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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.
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The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2020.acl-main)

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Challenge: a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, and perceive and converse about images.
Approach: They propose a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy . they use large dialogue datasets to multi-task and obtain state-of-the-art results .
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CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)

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Challenge: Document-grounded dialogs need smooth transitions between knowledge selected for generating responses.
Approach: They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic.
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RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling (2020.emnlp-main)

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Challenge: RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues spanning over 12 domains . despite of substantial progress made, there are challenges in creating challenging datasets in terms of size, multiple domains, semantic annotations and complexity.
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CorefUD 1.0: Coreference Meets Universal Dependencies (2022.lrec-1)

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Challenge: Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotized data.
Approach: They propose a multilingual collection of corpora and a standardized format for coreference resolution compatible with morphosyntactic annotations in the UD framework.
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Reference production in human-computer interaction: Issues for Corpus-based Referring Expression Generation (L18-1)

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Challenge: Referring Expression Generation studies often use web-based data collection tasks without a particular addressee in mind.
Approach: They developed a parallel corpus of monologue and dialogue referring expressions and an annotated corpus to compare instances produced in both modes of communication.
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They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking (C18-1)

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Challenge: Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities.
Approach: They propose a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions and a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning.
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Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)

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Challenge: Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity)
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Scaling Multi-Domain Dialogue State Tracking via Query Reformulation (N19-2)

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Challenge: Using a pointer-generator network, we model the reference resolution task as a dialogue context-aware user query reformulation task.
Approach: They propose a pointer-generator network and a novel multi-task learning setup to model dialogue state tracking and referring expression resolution tasks using a dialogue context-aware user query reformulation task.
Outcome: The proposed model improves absolute F1 on internal and public benchmarks.
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (2020.emnlp-main)

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Challenge: despite significant progress on entity coreference resolution, there is a general lack of understanding of what has been improved.
Approach: They present an empirical analysis of entity coreference resolvers to provide an understanding of what has been improved.
Outcome: The proposed model improves the performance of entity coreference resolvers.

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