Dialogue Understanding

18 papers with code • 0 benchmarks • 5 datasets

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Most implemented papers

A Repository of Conversational Datasets

PolyAI-LDN/conversational-datasets 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

dascim/acl2018_abssumm 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.

Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning

wangtianyiftd/dialogue_pretrain 27 Feb 2020

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 29 Sep 2020

Most of these approaches account for the context for effective understanding.

DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension

nlpdata/dream 1 Feb 2019

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

Alab-NII/onecommon 8 Jul 2019

Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground.

Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

xcfcode/DHGN 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 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

apple/ml-cread NAACL 2021

In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.