{"url":"/dataset/dstc7-task-1","name":"DSTC7 Task 1","full_name":"Dialog System Technology Challenges Task 1","description_markdown":"The **DSTC7 Task 1** dataset is a dataset and task for goal-oriented dialogue. The data originates from human-human conversations, which is built from online resources, specifically the Ubuntu Internet Relay Chat (IRC) channel and an Advising dataset from the University of Michigan.\r\n\r\nSource: [Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems](https://arxiv.org/abs/1907.01166)\r\nImage Source: [https://www.aclweb.org/anthology/W19-4107.pdf](https://www.aclweb.org/anthology/W19-4107.pdf)","description_withheld":null,"homepage":"http://workshop.colips.org/dstc7/call.html","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/dstc7-task-1-noetic-end-to-end-response","title":"DSTC7 Task 1: Noetic End-to-End Response Selection","first_author":"Chulaka Gunasekara","url":null},"license":{"name":"Unknown","url":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"},{"name":"Goal-Oriented Dialog","url":"/task/goal-oriented-dialog","datasets_with_task":"/datasets/task/goal-oriented-dialog"},{"name":"Goal-Oriented Dialogue Systems","url":"/task/goal-oriented-dialogue-systems","datasets_with_task":"/datasets/task/goal-oriented-dialogue-systems"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["DSTC7 Ubuntu","DSTC7 Task 1"],"data_loaders":[{"repo":"https://github.com/facebookresearch/ParlAI","url":"https://parl.ai/docs/tasks.html#dstc7-subtrack-1---ubuntu","frameworks":["pytorch"]}],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/conversational-response-selection-on-dstc7","task":"Conversational Response Selection","dataset_variant":"DSTC7 Ubuntu","rows":5,"metrics":["1-of-100 Accuracy"],"first_row_in_archive_order":{"model":"Multi-context ConveRT","paper":"/paper/convert-efficient-and-accurate-conversational","metrics":{"1-of-100 Accuracy":"71.2%"},"code_links":[{"title":"golsun/dialogrpt","url":"https://github.com/golsun/dialogrpt"},{"title":"davidalami/convert","url":"https://github.com/davidalami/convert"},{"title":"jordiclive/Convert-PolyAI-Torch","url":"https://github.com/jordiclive/Convert-PolyAI-Torch"},{"title":"koujm/convert-tf","url":"https://github.com/koujm/convert-tf"},{"title":"phamnam-mta/ConveRT-PolyAI-Vietnamese","url":"https://github.com/phamnam-mta/ConveRT-PolyAI-Vietnamese"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/convert-efficient-and-accurate-conversational","title":"ConveRT: Efficient and Accurate Conversational Representations from Transformers","date":"2019-11-09","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/190501969","title":"Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring","date":"2019-04-22","rows_on_this_dataset":2,"code_links":7,"syntology":null},{"paper":"/paper/sequential-attention-based-network-for-noetic","title":"Sequential Attention-based Network for Noetic End-to-End Response Selection","date":"2019-01-09","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/building-sequential-inference-models-for-end","title":"Building Sequential Inference Models for End-to-End Response Selection","date":"2018-12-03","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":9,"samples_ran":0,"samples_unverified":9,"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."}