natural-language-processing-7

Dialogue Understanding

3260 papers • 126 benchmarks • 313 datasets

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Spoken Language Understanding Dialogue Safety Prediction

Most implemented papers

Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization

A. Tixier, M. Vazirgiannis, Guokan Shang, Wensi Ding, Zekun Zhang, Polykarpos Meladianos, J. Lorré•Sun May 13 2018

A novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations to take exterior semantic knowledge into account, and to design custom diversity and informativeness measures.

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Content

Emotional Dialogue Acts

Harry Potter Dialogue Dataset

The Mafia Dataset

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A Repository of Conversational Datasets

N. Mrksic, Tsung-Hsien Wen, Ivan Vulic, Paweł Budzianowski, I. Casanueva, D. Gerz, Matthew Henderson, Sam Coope, Georgios P. Spithourakis, Pei-hao Su•Fri Apr 12 2019

A repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy' is presented.

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TEACh: Task-driven Embodied Agents that Chat

Dilek Z. Hakkani-Tür, Jesse Thomason, Spandana Gella, Anjali Narayan-Chen, Gokhan Tur, Aishwarya Padmakumar, Ayush Shrivastava, P. Lange, Robinson Piramithu•Thu Sep 30 2021

TEACh, a dataset of over 3,000 human-human, interactive dialogues to complete household tasks in simulation, is introduced and initial models' abilities in dialogue understanding, language grounding, and task execution are evaluated.

235 0

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ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

M. Kocour, Karel Veselý, Juan Pablo Zuluaga, Igor Szöke, J. Černocký, P. Motlícek, Allan Tart, Amrutha Prasad, Seyyed Saeed Sarfjoo, Pavel Kolcárek, Claudia Cevenini, K. Choukri, M. Rigault, Iuliia Nigmatulina•Mon Nov 07 2022

The ATCO2 corpus is introduced, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data, and will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community.

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

Yating Zhang, Tianyi Wang, Xiaozhong Liu, Changlong Sun, Qiong Zhang•Wed Feb 26 2020

This work investigates dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation.

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Utterance-level Dialogue Understanding: An Empirical Study

Soujanya Poria, Navonil Majumder, Deepanway Ghosal, Rada Mihalcea•Mon Sep 28 2020

This paper explores and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act identification, using state-of-the-art dialog understanding methods as baselines and employs various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines.

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A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context

Takuma Udagawa, Akiko Aizawa•Sun Jul 07 2019

This paper proposes a minimal dialogue task which requires advanced skills of common grounding under continuous and partially-observable context, and collects a largescale dataset of 6,760 dialogues which fulfills essential requirements of natural language corpora.

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Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure

Ting Liu, Bing Qin, Jiaqi Li, Ming Liu, Min-Yen Kan, Zihao Zheng, Zekun Wang, Wenqiang Lei•Thu Apr 09 2020

The Molweni dataset is presented, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog with large-scale annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style.

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Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

Ting Liu, Bing Qin, Xiaocheng Feng, Xiachong Feng•Mon Oct 19 2020

A novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue un-derstanding and summary generation and a Dialogue Heterogeneous Graph Network for modeling both information.

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