DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4335–4347 November 7–11, 2021. c⃝2021 Association for Computational Linguistics 4335 DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation Zeming Liu1∗, Haifeng Wang2, Zheng-Yu Niu2, Hua Wu2, Wanxiang Che1† 1Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China 2Baidu Inc., Beijing, China {zmliu, car}@ir.hit.edu.cn {wanghaifeng, niuzhengyu, wu_hua}@baidu.com Abstract In this paper, we provide a bilingual paral- lel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommenda- tion. The difference between DuRecDial 2.0 and existing conversational recommendation datasets is that the data item (Profile, Goal, Knowledge, Context, Response) in DuRecDial 2.0 is annotated in two languages, both En- glish and Chinese, while other datasets are built with the setting of a single language. We collect 8.2k dialogs aligned across English and Chinese languages (16.5k dialogs and 255k ut- terances in total) that are annotated by crowd- sourced workers with strict quality control pro- cedure. We then build monolingual, multilin- gual, and cross-lingual conversational recom- mendation baselines on DuRecDial 2.0. Exper- iment results show that the use of additional English data can bring performance improve- ment for Chinese conversational recommen- dation, indicating the benefits of DuRecDial 2.0. Finally, this dataset provides a challeng- ing testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation. 1 1 Introduction In recent years, there has been a significant in- crease in the research topic of conversational rec- ommendation due to the rise of voice-based bots (Kang et al., 2019; Li et al., 2018; Sun and Zhang, 2018; Christakopoulou et al., 2016; Warnestal, 2005). These works focus on how to provide recom- mendation service in a more user-friendly manner through dialog-based interactions. They fall into two categories: (1) task-oriented dialog-modeling approaches with requirement of pre-defined user in- tents and slots (Warnestal, 2005; Christakopoulou ∗ This work was done at Baidu. † Corresponding author: Wanxiang Che. 1https://github.com/liuzeming01/DuRecDial. et al., 2016; Sun and Zhang, 2018); (2) non-task dialog-modeling approaches that can conduct more free-form interactions for recommendation, with- out pre-defined user intents and slots (Li et al., 2018; Kang et al., 2019). Recently more and more efforts are devoted to the research line of the sec- ond category and many datasets have been created, including English dialog datasets (Dodge et al., 2016; Li et al., 2018; Kang et al., 2019; Moon et al., 2019; Hayati et al., 2020) and Chinese dialog datasets (Liu et al., 2020b; Zhou et al., 2020). However, to the best of our knowledge, almost all these datasets are constructed in the setting of a single language, and there is no publicly available multilingual dataset for conversational recommendation. Previous work on other NLP tasks have proved that multilingual corpora can bring performance improvement in comparison with monolingual task setting, such as for the tasks of task-oriented dialog (Schuster et al., 2019b), semantic parsing (Li et al., 2021), QA and read- ing comprehension (Jing et al., …

Content truncated to 500 words