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

By Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Peng Xu, Feijun Jiang, Yuxiang Hu, Chen Shi, Pascale Fung. 2021

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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.

Subfields: Artificial Intelligence

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Language Models are Few-shot Multilingual Learners (2021, methodology): “, 2021) and then apply pre-training on the source language and fine-tune on the target languages (Schuster et al., 2019; Lin et al., 2019; Winata et al., 2019, 2021; Pfeiffer et al., 2020; Zheng et al., 2021; Lin et al., 2021b).”