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Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems

Paper · 2023 · Songbo Hu, Han Zhou, Mete Hergul et al.

Summary

Multilingual task-oriented dialog (TOD) systems need data that is hard to obtain. Existing multilingual TOD datasets are usually built by translating English data, which brings in "translationese" and inflates scores. Many are also small, ignore local culture, lack parallel dialogs across languages, or cover only NLU. The authors survey these weaknesses and present MULTI3WOZ to address them. Starting from the cleaned English MultiWOZ 2.3, they adapt the outline-based method of Majewska et al. (2023). Abstract, language-independent dialog schemata are rewritten from scratch by native speakers, using real local entities, in Arabic, French and Turkish, alongside English. The resulting corpus is large, multi-parallel and culturally localized. It supports NLU, DST, NLG and end-to-end (E2E) modelling in monolingual, multilingual and cross-lingual settings. The authors also release baselines, including the first public multilingual E2E system compatible with MultiWOZ. The baseline scores reveal clear performance gaps across languages, with English generally ahead. This makes the dataset a demanding benchmark for fair multilingual dialog research.

Key points

  • Scale and languages: MULTI3WOZ has 494,116 human-written dialog turns in English, Arabic, French and Turkish. Each language has 7,440 training, 860 development and 860 test dialogs.
  • Cost of creation: Building the development and test sets took over 300 hours per language. Annotation cost about $0.17 per utterance.
  • E2E baseline design: The pipeline combines a DST model, an SQL-based database interface and a response generator. Both models use mT5-large, with preprocessing and evaluation following SOLOIST.
  • E2E results: Average scores across the four languages were 57.1 Inform Rate, 29.8 Success Rate and 14.6 BLEU. Switching from mT5-small to mT5-large improved these by 16.4, 17.2 and 4.6 points, respectively.
  • Availability: The dataset and code are released at github.com/cambridgeltl/multi3woz.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CambridgeCambridge · United Kingdom243 works · 121,396 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 828 worksDialogue systems · 624 works

Cite

Cite as Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić and Anna Korhonen. 2023. Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00609

@article{hu2023multi,
  title = {{Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems}},
  author = {Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić and Anna Korhonen},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00609},
  url = {https://doi.org/10.1162/tacl_a_00609}
}

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