Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking

• URL: http://arxiv.org/abs/2605.30107v1
• Date: Thu, 28 May 2026 15:47:09 GMT
• Title: Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking
• Abstract summary: This work introduces HEALTHDIAL, a large-scale, multilingual, and multi-parallel dataset for developing and evaluating retrieval-augmented generation (RAG)-based spoken dialogue systems.<n>The dataset comprises 6,000 information-seeking dialogues (1,500 per language) grounded in trusted content from the World Health Organization (WHO) and 163 hours of user speech recorded from native speakers of diverse dialects.
• Score: 72.66384771245389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-parallel datasets at scale. This work introduces HEALTHDIAL, a large-scale, multilingual, and multi-parallel dataset for developing and evaluating retrieval-augmented generation (RAG)-based spoken dialogue systems. The dataset comprises 6,000 information-seeking dialogues (1,500 per language) grounded in trusted content from the World Health Organization (WHO) and 163 hours of user speech recorded from native speakers of diverse dialects across four official WHO languages: Arabic, Chinese, English, and Spanish. Each speaker is annotated with demographic (e.g., gender, age) and sociolinguistic (e.g., primary language, region of origin) variables. We report benchmark results across key dialogue tasks, which reveal consistent performance disparities across languages, even among high-resource ones. To support future research, we release the dataset, a prototype system, and a toolkit for data collection and system evaluation.

Related papers

• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• 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)
• ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For Low-resource Language [0.0]
  ViWOZ is the first multi-turn, multi-domain tasked oriented dataset in Vietnamese. The dataset consists of a total of 5,000 dialogues, including 60,946 fully annotated utterances. With those characteristics, the ViWOZ dataset enables future studies on creating a multilingual task-oriented dialogue system.
  arXiv  Detail & Related papers  (2022-03-15T09:22:04Z)
• 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)
• GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems [66.92182084456809]
  We introduce a novel data curation method that generates GlobalWoZ -- a large-scale multilingual ToD dataset from an English ToD dataset. Our method is based on translating dialogue templates and filling them with local entities in the target-language countries. We release our dataset as well as a set of strong baselines to encourage research on learning multilingual ToD systems for real use cases.
  arXiv  Detail & Related papers  (2021-10-14T19:33:04Z)
• Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking [84.50302759362698]
  We enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models. We use parallel and conversational movie subtitles datasets to design cross-lingual intermediate tasks. We achieve impressive improvements (> 20% on goal accuracy) on the parallel MultiWoZ dataset and Multilingual WoZ dataset.
  arXiv  Detail & Related papers  (2021-09-28T11:22:38Z)
• 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)

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.