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

TL;DR

BiToD introduces the first bilingual multi-domain dataset for task-oriented dialogue, enabling better evaluation and development of multilingual dialogue systems with cross-lingual transfer learning.

Contribution

It provides a large, realistic bilingual dataset and benchmarks for end-to-end ToD modeling, addressing the lack of multilingual datasets.

Findings

Bilingual ToD systems outperform separate monolingual systems.

Leveraging bilingual knowledge bases enhances low-resource system performance.

Cross-lingual transfer learning improves dialogue system robustness.

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 compared 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 under low resource condition.