BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue ModelingDownload PDF

Published: 29 Jul 2021, Last Modified: 08 Sept 2024NeurIPS 2021 Datasets and Benchmarks Track (Round 1)Readers: Everyone
Keywords: End-to-end, Task-oriented Dialogue, Bilingual
TL;DR: We propose a bilingual multi-domain dataset for end-to-end task-oriented dialogue modeling.
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 modeling 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 bilingual 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.
URL: https://github.com/HLTCHKUST/BiToD
Supplementary Material: zip
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/bitod-a-bilingual-multi-domain-dataset-for/code)
15 Replies

Loading