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# 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> |