{"url":"/dataset/rrs","name":"RRS","full_name":"Restoration-200k for Response Selection","description_markdown":"|           | Train | Validation | Test    | Ranking Test |\r\n| --------- | ----- | ---------- | ------- | ------------ |\r\n| size      | 0.4M  | 50K        | 5K      | 800          |\r\n| pos:neg   | 1:1   | 1:9        | 1.2:8.8 | -            |\r\n| avg turns | 5.0   | 5.0        | 5.0     | 5.0          |\r\n\r\nRanking test set contains the high-quality responses that selected by some baselines, and their correlation with the conversation context are carefully annotated by 8 professional annotators (the average annotation scores are saved for ranking). For ranking test set, the metrics should be NDCG@3 and NDCG@5, since the correlation scores are provided. More details are available in the Appendix of the paper.","description_withheld":null,"homepage":"https://github.com/gmftbyGMFTBY/SimpleReDial-v1","introduced_date":"2021-10-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/exploring-dense-retrieval-for-dialogue","title":"Exploring Dense Retrieval for Dialogue Response Selection","first_author":"Tian Lan","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Conversational Response Selection","url":"/task/conversational-response-selection","datasets_with_task":"/datasets/task/conversational-response-selection"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["RRS"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/conversational-response-selection-on-rrs","task":"Conversational Response Selection","dataset_variant":"RRS","rows":7,"metrics":["MAP","MRR","P@1","R10@1","R10@2","R10@5"],"first_row_in_archive_order":{"model":"BERT-FP","paper":"/paper/fine-grained-post-training-for-improving","metrics":{"MAP":"0.702","MRR":"0.712","P@1":"0.543","R10@1":"0.488","R10@2":"0.708","R10@5":"0.927"},"code_links":[{"title":"hanjanghoon/BERT_FP","url":"https://github.com/hanjanghoon/BERT_FP"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fine-grained-post-training-for-improving","title":"Fine-grained Post-training for Improving Retrieval-based Dialogue Systems","date":"2021-05-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dialogue-response-selection-with-hierarchical","title":"Dialogue Response Selection with Hierarchical Curriculum Learning","date":"2020-12-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/speaker-aware-bert-for-multi-turn-response","title":"Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots","date":"2020-04-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/multi-hop-selector-network-for-multi-turn","title":"Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots","date":"2019-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/domain-adaptive-training-bert-for-response","title":"An Effective Domain Adaptive Post-Training Method for BERT in Response Selection","date":"2019-08-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-turn-response-selection-for-chatbots","title":"Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network","date":"2018-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sequential-matching-network-a-new","title":"Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots","date":"2016-12-06","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}