Dialogue Understanding
29 papers with code • 0 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Dialogue Understanding
Datasets
Doc2Dial
ProsocialDialog
DiaASQ
WDC-Dialogue
Emotional Dialogue Acts
The Mafia Dataset
Latest papers
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
lisk123/uccr
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In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS.
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience.
TEACh: Task-driven Embodied Agents that Chat
alexa/teach
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Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding.
DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization
For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation.
M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations.
A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues
DeepLearnXMU/Structure-Self-Aware
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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence 2021
Conversational discourse structures aim to describe how a dialogue is organised, thus they are helpful for dialogue understanding and response generation.
Semantic Representation for Dialogue Modeling
muyeby/AMR-Dialogue
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ACL 2021
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
