# GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems

arXiv: https://arxiv.org/abs/2110.07679 · HTML: https://mlsanity.com/paper/2110.07679/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://mlsanity.com/paper/2008.09335/) (2020)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.12005/) (2020)
- [Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1704.00057/) (2017)
- [Recent Advances and Challenges in Task-oriented Dialog System](https://mlsanity.com/paper/2003.07490/) (2020)
- [CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset](https://mlsanity.com/paper/2002.11893/) (2020)
- [BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling](https://mlsanity.com/paper/2106.02787/) (2021)
- [From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2105.07316/) (2021)
- [RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling](https://mlsanity.com/paper/2010.08738/) (2020)
- [VOnDA: A Framework for Ontology-Based Dialogue Management](https://mlsanity.com/paper/1910.00340/) (2019)

## Built on by (most cited first, 30 of 31)

- [Is GPT-3 a Good Data Annotator?](https://mlsanity.com/paper/2212.10450/) (2022)
- [Retrieving Multimodal Information for Augmented Generation: A Survey](https://mlsanity.com/paper/2303.10868/) (2023)
- [XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages](https://mlsanity.com/paper/2305.11938/) (2023)
- [Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog](https://mlsanity.com/paper/2205.10400/) (2022)
- [Neuro-Symbolic Procedural Planning with Commonsense Prompting](https://mlsanity.com/paper/2206.02928/) (2022)
- [Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation](https://mlsanity.com/paper/2201.13405/) (2022)
- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Multi3WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems](https://mlsanity.com/paper/2307.14031/) (2023)
- [MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2212.10455/) (2022)
- [MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages](https://mlsanity.com/paper/2208.13078/) (2022)
- [X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents](https://mlsanity.com/paper/2306.17674/) (2023)
- [Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues](https://mlsanity.com/paper/2111.02574/) (2021)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition](https://mlsanity.com/paper/2308.16207/) (2023)
- [The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding](https://mlsanity.com/paper/2209.07800/) (2022)
- [Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning](https://mlsanity.com/paper/2304.01295/) (2023)
- [InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2310.08885/) (2023)
- [Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation](https://mlsanity.com/paper/2302.09424/) (2023)
- [SEADialogues: A Multilingual Culturally Grounded Multi-turn Dialogue Dataset on Southeast Asian Languages](https://mlsanity.com/paper/2508.07069/) (2025)
- [Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact](https://mlsanity.com/paper/2410.17532/) (2024)
- [mPMR: A Multilingual Pre-trained Machine Reader at Scale](https://mlsanity.com/paper/2305.13645/) (2023)
- [Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2210.16960/) (2022)
- [Talking with Machines: A Comprehensive Survey of Emergent Dialogue Systems](https://mlsanity.com/paper/2305.16324/) (2023)
- [DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models](https://mlsanity.com/paper/2401.02208/) (2024)
- [Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents](https://mlsanity.com/paper/2405.17840/) (2024)
- [Is Translation Helpful? An Empirical Analysis of Cross-Lingual Transfer in Low-Resource Dialog Generation](https://mlsanity.com/paper/2305.12480/) (2023)
- [Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/2406.06127/) (2024)
- [Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking](https://mlsanity.com/paper/2605.30107/) (2026)
- [τ-Multilingual: Benchmarking Voice Agents Across Languages](https://mlsanity.com/paper/2609.35820/) (2026)

## Abstract (arXiv)

Much recent progress in task-oriented dialogue (ToD) systems has been driven by available annotation data across multiple domains for training. Over the last few years, there has been a move towards data curation for multilingual ToD systems that are applicable to serve people speaking different languages. However, existing multilingual ToD datasets either have a limited coverage of languages due to the high cost of data curation, or ignore the fact that dialogue entities barely exist in countries speaking these languages. To tackle these limitations, we introduce a novel data curation method that generates GlobalWoZ -- a large-scale multilingual ToD dataset globalized from an English ToD dataset for three unexplored use cases. 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.