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Task-oriented dialogue (ToD) benchmarks provide an important avenue tomeasure progress and develop better conversational agents. However, existingdatasets for end-to-end ToD modeling are limited to a single language,hindering the development of robust end-to-end ToD systems for multilingualcountries and regions. Here we introduce BiToD, the first bilingualmulti-domain dataset for end-to-end task-oriented dialogue modeling. BiToDcontains over 7k multi-domain dialogues (144k utterances) with a large andrealistic bilingual knowledge base. It serves as an effective benchmark forevaluating bilingual ToD systems and cross-lingual transfer learningapproaches. We provide state-of-the-art baselines under three evaluationsettings (monolingual, bilingual, and cross-lingual). The analysis of ourbaselines in different settings highlights 1) the effectiveness of training abilingual ToD system compared to two independent monolingual ToD systems, and2) the potential of leveraging a bilingual knowledge base and cross-lingualtransfer learning to improve the system performance under low resourcecondition.

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