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In this paper, we provide a bilingual parallel human-to-human recommendationdialog dataset (DuRecDial 2.0) to enable researchers to explore a challengingtask of multilingual and cross-lingual conversational recommendation. Thedifference between DuRecDial 2.0 and existing conversational recommendationdatasets is that the data item (Profile, Goal, Knowledge, Context, Response) inDuRecDial 2.0 is annotated in two languages, both English and Chinese, whileother datasets are built with the setting of a single language. We collect 8.2kdialogs aligned across English and Chinese languages (16.5k dialogs and 255kutterances in total) that are annotated by crowdsourced workers with strictquality control procedure. We then build monolingual, multilingual, andcross-lingual conversational recommendation baselines on DuRecDial 2.0.Experiment results show that the use of additional English data can bringperformance improvement for Chinese conversational recommendation, indicatingthe benefits of DuRecDial 2.0. Finally, this dataset provides a challengingtestbed for future studies of monolingual, multilingual, and cross-lingualconversational recommendation.

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation