{"url":"/sota/answer-selection-on-ubuntu-dialogue-v2","task":{"name":"Answer Selection","url":"/task/answer-selection","note":null},"dataset":{"name":"Ubuntu Dialogue (v2, Ranking)","url":"/dataset/ubuntu-dialogue-corpus"},"category":"Natural Language Processing","categories":["Miscellaneous","Natural Language Processing","Reasoning"],"category_note":null,"description":"**Answer Selection** is the task of identifying the correct answer to a question from a pool of candidate answers. This task can be formulated as a classification or a ranking problem.\n\n\n<span class=\"description-source\">Source: [Learning Analogy-Preserving Sentence Embeddings for Answer Selection ](https://arxiv.org/abs/1910.05315)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["1 in 10 R@1","1 in 10 R@2","1 in 10 R@5","1 in 2 R@1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"1 in 10 R@1":null,"1 in 10 R@2":null,"1 in 10 R@5":null,"1 in 2 R@1":null}},"counts":{"rows":2,"rows_with_code":1,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"BERT + Keep Learning","metrics":{"1 in 10 R@1":"82.4"},"uses_additional_data":false,"paper_date":"2021-04-01","paper":"/paper/keep-learning-self-supervised-meta-learning","paper_url":"https://aclanthology.org/2021.eacl-main.6","paper_title":"Keep Learning: Self-supervised Meta-learning for Learning from Inference","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"HRDE-LTC","metrics":{"1 in 10 R@1":"0.652","1 in 10 R@2":"0.815","1 in 10 R@5":"0.966","1 in 2 R@1":"0.915"},"uses_additional_data":false,"paper_date":"2017-10-10","paper":"/paper/learning-to-rank-question-answer-pairs-using","paper_url":"http://arxiv.org/abs/1710.03430v3","paper_title":"Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering","code":"https://github.com/david-yoon/QA_HRDE_LTC","n_code_links":3,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}