MD3: The Multi-Dialect Dataset of Dialogues

• URL: http://arxiv.org/abs/2305.11355v1
• Date: Fri, 19 May 2023 00:14:10 GMT
• Title: MD3: The Multi-Dialect Dataset of Dialogues
• Authors: Jacob Eisenstein, Vinodkumar Prabhakaran, Clara Rivera, Dorottya Demszky, Devyani Sharma
• Abstract summary: We introduce a new dataset of conversational speech representing English from India, Nigeria, and the United States. The dataset includes more than 20 hours of audio and more than 200,000 orthographically-transcribed tokens.
• Score: 20.144004030947507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new dataset of conversational speech representing English from India, Nigeria, and the United States. The Multi-Dialect Dataset of Dialogues (MD3) strikes a new balance between open-ended conversational speech and task-oriented dialogue by prompting participants to perform a series of short information-sharing tasks. This facilitates quantitative cross-dialectal comparison, while avoiding the imposition of a restrictive task structure that might inhibit the expression of dialect features. Preliminary analysis of the dataset reveals significant differences in syntax and in the use of discourse markers. The dataset, which will be made publicly available with the publication of this paper, includes more than 20 hours of audio and more than 200,000 orthographically-transcribed tokens.

Related papers

• The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings [0.0]
  We present our approach to building large and open speech-and-text-aligned datasets of less-resourced languages. We focus on three Slavic languages, namely Croatian, Polish, and Serbian. The results of this pilot run are three high-quality datasets that span more than 5,000 hours of speech and accompanying text transcripts.
  arXiv  Detail & Related papers  (2024-09-23T10:12:18Z)
• Multi-turn Dialogue Comprehension from a Topic-aware Perspective [70.37126956655985]
  This paper proposes to model multi-turn dialogues from a topic-aware perspective. We use a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. We also present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements.
  arXiv  Detail & Related papers  (2023-09-18T11:03:55Z)
• Multi3WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems [64.40789703661987]
  Multi3WOZ is a novel multilingual, multi-domain, multi-parallel ToD dataset. It is large-scale and offers culturally adapted dialogs in 4 languages. We describe a complex bottom-up data collection process that yielded the final dataset.
  arXiv  Detail & Related papers  (2023-07-26T08:29:42Z)
• SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents [72.42049370297849]
  SpokenWOZ is a large-scale speech-text dataset for spoken TOD. Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
  arXiv  Detail & Related papers  (2023-05-22T13:47:51Z)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)
• Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension [46.69961067676279]
  Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue. Previous models focus on how to incorporate speaker information flows using complex graph-based modules. In this paper, we design two labour-free self- and pseudo-self-supervised prediction tasks on speaker and key-utterance to implicitly model the speaker information flows.
  arXiv  Detail & Related papers  (2021-09-08T16:51:41Z)
• RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling [35.75880078666584]
  RiSAWOZ is a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic s. It contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains.
  arXiv  Detail & Related papers  (2020-10-17T08:18:59Z)
• Interview: A Large-Scale Open-Source Corpus of Media Dialog [11.28504775964698]
  We introduce 'Interview': a large-scale (105K conversations) media dialog dataset collected from news interview transcripts. Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance. 'Interview' contains speaker role annotations for each turn, facilitating the development of engaging, responsive dialog systems.
  arXiv  Detail & Related papers  (2020-04-07T02:44:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.