  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.
