| # BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling | |
| This repository includes the dataset and baselines of the paper: | |
| **BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling** (Accepted in NeurIPS 2021 Track on Datasets and Benchmarks) [[PDF]](https://arxiv.org/pdf/2106.02787.pdf). | |
| **Authors**: [Zhaojiang Lin](https://zlinao.github.io), [Andrea Madotto](https://andreamad8.github.io), [Genta Indra Winata](https://gentawinata.com), Peng Xu, Feijun Jiang, Yuxiang Hu, Chen Shi, Pascale Fung | |
| ## Abstract: | |
| Task-oriented dialogue (ToD) benchmarks provide an important avenue to measure progress and develop better conversational agents. However, existing datasets for end-to-end ToD modelling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions. Here we introduce BiToD, the first bilingual multi-domain dataset for end-to-end task-oriented dialogue modeling. BiToD contains over 7k multi-domain dialogues (144k utterances) with a large and realistic parallel knowledge base. It serves as an effective benchmark for evaluating bilingual ToD systems and cross-lingual transfer learning approaches. We provide state-of-the-art baselines under three evaluation settings (monolingual, bilingual and cross-lingual). The analysis of our baselines in different settings highlights 1) the effectiveness of training a bilingual ToD system comparing to two independent monolingual ToD systems, and 2) the potential of leveraging a bilingual knowledge base and cross-lingual transfer learning to improve the system performance in the low resource condition. | |
| ## Dataset | |
| Training, validation and test data are avalible in `data` folder. We also provide the data split for cross-lingual few shot setting. | |
| ``` | |
| { | |
| dialogue_id:{ | |
| "Scenario": { | |
| "WizardCapabilities": [ | |
| ], | |
| "User_Goal": { | |
| } | |
| } | |
| "Events":{ | |
| { | |
| "Agent": "User", | |
| "Actions": [ | |
| { | |
| "act": "inform_intent", | |
| "slot": "intent", | |
| "relation": "equal_to", | |
| "value": [ | |
| "restaurants_en_US_search" | |
| ] | |
| } | |
| ], | |
| "active_intent": "restaurants_en_US_search", | |
| "state": { | |
| "restaurants_en_US_search": {} | |
| }, | |
| "Text": "Hi, I'd like to find a restaurant to eat", | |
| }, | |
| { | |
| "Agent": "Wizard", | |
| "Actions": [ | |
| { | |
| "act": "request", | |
| "slot": "price_level", | |
| "relation": "", | |
| "value": [] | |
| } | |
| ], | |
| "Text": "Hi there. Would you like a cheap or expensive restaurant?", | |
| "PrimaryItem": null, | |
| "SecondaryItem": null, | |
| }, | |
| ... | |
| } | |
| } | |
| } | |
| ``` | |
| ## Citation: | |
| The bibtex is listed below: | |
| <pre> | |
| @article{lin2021bitod, | |
| title={BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling}, | |
| author={Lin, Zhaojiang and Madotto, Andrea and Winata, Genta Indra and Xu, Peng and Jiang, Feijun and Hu, Yuxiang and Shi, Chen and Fung, Pascale}, | |
| journal={arXiv preprint arXiv:2106.02787}, | |
| year={2021} | |
| } | |
| </pre> |