Dialogue Understanding
18 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Dialogue Understanding
Doc2Dial
Emotional Dialogue Acts
WDC-Dialogue
Most implemented papers
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.
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
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.
Utterance-level Dialogue Understanding: An Empirical Study
declare-lab/dialogue-understanding
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Most of these approaches account for the context for effective understanding.
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.
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.
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.
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.
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.
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.
