Online and Punta Cana, Dominican Republic Proceedings of the 2021 Conference on Empirical Methods in Natural Language ProcessingĪssociation for Computational Linguistics Finally, this dataset provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation.",ĭuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation 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. We then build monolingual, multilingual, and cross-lingual conversational recommendation baselines on DuRecDial 2.0. ![]() 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. 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. Publisher = "Association for Computational Linguistics",Ībstract = "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. Cite (Informal): DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation (Liu et al., EMNLP 2021) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Video: Code liuzeming01/durecdial Data DuRecDial, = "ial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation",īooktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",Īddress = "Online and Punta Cana, Dominican Republic", Association for Computational Linguistics. ![]() In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4335–4347, Online and Punta Cana, Dominican Republic. DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation. Anthology ID: 2021.emnlp-main.356 Volume: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing Month: November Year: 2021 Address: Online and Punta Cana, Dominican Republic Venue: EMNLP SIG: Publisher: Association for Computational Linguistics Note: Pages: 4335–4347 Language: URL: DOI: 10.18653/v1/2021.emnlp-main.356 Bibkey: liu-etal-2021-durecdial Cite (ACL): Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, and Wanxiang Che. Finally, this dataset provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation. ![]() 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.
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