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
Adding Chit-Chat to Enhance Task-Oriented Dialogues
facebookresearch/accentor
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NAACL 2021
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
xcfcode/DHGN
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CCL 2021
In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.
Utterance-level Dialogue Understanding: An Empirical Study
Most of these approaches account for the context for effective understanding.
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Research into the area of multiparty dialog has grown considerably over recent years.
Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning
Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc.
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground.
A Repository of Conversational Datasets
PolyAI-LDN/conversational-datasets
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WS 2019
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
nlpdata/dream
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DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge.
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
xcfcode/Summarization-Papers
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ACL 2018
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
