Challenge: a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks .
Approach: They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively .
Outcome: The proposed method outperforms existing models and achieves a 3.3% improvement on average.

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Challenge: Goal-oriented conversations often have sub-dialogue structure, but it can be domain-dependent . Increasingly, language understanding applications involve conversational speech and text .
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Challenge: Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks.
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Challenge: Existing models for limited-domain RNNs are difficult to scale due to the complexity of the inputs.
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Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension (P19-1)

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Challenge: Existing research on multi-turn spoken conversations focuses on reading comprehension of passages . interactivity of spoken content can cause lower information density and topic diffusion .
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Challenge: Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots.
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Challenge: Existing models for dialog generation are challenging to train using the standard Seq2Seq models.
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A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging (2022.coling-1)

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Challenge: Dialogue Act tagging with ISO 24617-2 standard is a difficult task that requires multiple labels covering semantic, syntactic and pragmatic aspects of dialogue.
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Dialog Intent Structure: A Hierarchical Schema of Linked Dialog Acts (L18-1)

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Challenge: a schema for dialog representation captures the pragmatic intents of the conversation independently from any semantic representation.
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Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing (2023.findings-emnlp)

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Challenge: Existing discourse parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks.
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Deep Learning for Dialogue Systems (C18-3)

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Challenge: Using deep learning to build robust and scalable spoken dialogue systems is still a challenging task.
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