DuRecDial 2.0：一个用于会话推荐的双语平行语料库
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation
摘要：
在本文中，我们提供了一个双语并行人对人的推荐对话数据集（DuRecDial 2.0），使研究人员能够探索具有挑战性的任务，多语言和跨语言的会话推荐。DuRecDial 2.0与现有会话式推荐数据集的区别在于，DuRecDial 2.0中的数据项（Profile，Goal，Knowledge，Context，Response）使用两种语言进行注释，英语和中文，而其他数据集则使用单一语言设置。我们收集了8.2k个跨英语和汉语的对话（共16.5k个对话和255k个话语），这些对话由众包工作人员进行注释，并具有严格的质量控制程序。然后，我们在DuRecDial 2.0上构建单语，多语言和跨语言会话推荐基线。实验结果表明，使用额外的英语数据可以带来性能的改善，中文会话推荐，显示DuRecDial 2.0的好处。最后，这个数据集提供了一个具有挑战性的测试平台，为未来的研究单语，多语言和跨语言的会话推荐。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
