D u R ec D ial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation (2021)

Abstract: In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation. 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 English 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 utterances in total) that are annotated by crowdsourced workers with strict quality control procedure. We then build monolingual, multilingual, and cross-lingual conversational recommendation baselines on DuRecDial 2.0. Experiment results show that the use of additional English data can bring performance improvement for Chinese conversational recommendation, indicating the benefits of DuRecDial 2.0. Finally, this dataset provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation.

## introduction in recent years, there has been a significant increase in the research topic of conversational recommendation due to the rise of voice-based bots @xcite @xcite @xcite . these works focus on how to provide recommendation 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 intents and slots @xcite @xcite ; (2) non-task dialog-modeling approaches that can conduct more free-form interactions for recommendation, without pre-defined user intents and slots @xcite . recently more and more efforts are devoted to the research line of the second category and many datasets have been created, including english dialog datasets @xcite @xcite @xcite and chinese dialog datasets @xcite . 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 @xcite , semantic parsing @xcite , qa and reading comprehension @xcite @xcite @xcite , machine translation @xcite , document classification @xcite @xcite , semantic role labelling @xcite and nli @xcite . therefore it is necessary to create multilingual conversational recommendation dataset that might enhance model performance when compared with monolingual training setting, and it could provide a new benchmark dataset for the study of multilingual modeling techniques. to facilitate the study of this challenge, we present a bilingual parallel recommendation dialog dataset, durecdial 2.0, for multilingual and crosslingual conversational recommendation. durec-dial 2.0 consists of 8.2k dialogs aligned across two languages, english and chinese (16.5k dialogs and 255k utterances in total). we define five tasks on this dataset. as shown in figure 1 m onolingual, the first two tasks are english or chinese monolingual conversational recommendation, where dialog context, knowledge, dialog goal, and response are in the same language. it aims at investigating the performance variation of the same model across two different languages. as shown in figure 1 m ultilingual, there is another task that is called multilingual conversational recommendation. here we directly mix training instances of the two languages into a single training set and train a single model to handle both english and chinese conversational recommendation at the same time. as shown in figure 1 crosslingual, the last two tasks are cross-lingual conversational recommendation, where model input and output are in different languages, e.g. dialog context is in english (or chinese) and generated response is in chinese (or english). to address these tasks, we build baselines using xnlg @xcite foot_0 and mbart @xcite foot_1 . we conduct an empirical study of the baselines on durecdial 2.0, and experiment results indicate that the use of additional english data can bring performance improvement for chinese conversational recommendation. in summary, this work makes the following contributions: datasets language parallel #dial. #utt. dialog types domains facebook_rec(dodge et al., 2016) en 1m 6m rec. movie redial (li et al., 2018) en 10k 163k rec., chitchat movie gorecdial (kang et al., 2019) en 9k 170k rec. movie opendialkg (moon et al., 2019) en 12k 143k rec. movie, book durecdial (liu et al., 2020b) zh 10.2k 156k rec., chitchat, qa, task movie, music, star, food, restaurant, news, weather tg-redial (zhou et al., 2020) zh 10k 129k rec. movie inspired (hayati et al., 2020) en 1k 35k rec. movie durecdial 2.0 (ours) en-zh 16.5k 255k rec., chitchat, qa, task movie, music, star, food, restaurant, weather ## dataset collection durecdial 2.0 is designed to collect highly parallel data to facilitate the study of monolingual, multilingual and cross-lingual conversational recommendation. in this section, we describe the three steps for dataset construction: (1) constructing the parallel data item; (2) collecting conversation utterances by crowdsourcing; (3) collecting knowledge triples by crowdsourcing. ## conclusion to facilitate the study of multilingual and crosslingual conversational recommendation, we create a bilingual parallel dataset durecdial 2.0 and define 5 tasks on it. we further establish baselines for monolingual, multilingual, and cross-lingual conversational recommendation. automatic evaluation and human evaluation results show that our bilingual dataset, durecdial 2.0, can bring performance improvement for chinese conversational recommendation. besides, durecdial 2.0 provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation. in future work, we will investigate the possibility of combining multilinguality and few (or zero) shot learning to see if it can help dialog tasks in low-resource languages.