{"url":"/dataset/mrda","name":"MRDA","full_name":"ICSI Meeting Recorder Dialog Act Corpus","description_markdown":"The **MRDA** corpus consists of about 75 hours of speech from 75 naturally-occurring meetings among 53 speakers. The tagset used for labeling is a modified version of the SWBD-DAMSL tagset. It is annotated with three types of information: marking of the dialogue act segment boundaries, marking of the dialogue acts and marking of correspondences between dialogue acts.\r\n\r\nDescription from [NLP Progress](http://nlpprogress.com/english/dialogue.html)","description_withheld":null,"homepage":"http://www1.icsi.berkeley.edu/Speech/mr/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Speech","url":"/datasets/modality/speech"}],"tasks":[{"name":"Dialogue Act Classification","url":"/task/dialogue-act-classification","datasets_with_task":"/datasets/task/dialogue-act-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ICSI Meeting Recorder Dialog Act (MRDA) corpus","MRDA"],"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/dialogue-act-classification-on-icsi-meeting","task":"Dialogue Act Classification","dataset_variant":"ICSI Meeting Recorder Dialog Act (MRDA) corpus","rows":8,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Pretrained Hierarchical Transformer","paper":"/paper/hierarchical-pre-training-for-sequence","metrics":{"Accuracy":"92.4"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hierarchical-fusion-for-online-multimodal","title":"Hierarchical Fusion for Online Multimodal Dialog Act Classification","date":"2023-12-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/speaker-turn-modeling-for-dialogue-act","title":"Speaker Turn Modeling for Dialogue Act Classification","date":"2021-09-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hierarchical-pre-training-for-sequence","title":"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog","date":"2020-09-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/guiding-attention-in-sequence-to-sequence","title":"Guiding attention in Sequence-to-sequence models for Dialogue Act prediction","date":"2020-02-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dialogue-act-classification-with-context","title":"Dialogue Act Classification with Context-Aware Self-Attention","date":"2019-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/self-governing-neural-networks-for-on-device","title":"Self-Governing Neural Networks for On-Device Short Text Classification","date":"2018-10-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dialogue-act-recognition-via-crf-attentive","title":"Dialogue Act Recognition via CRF-Attentive Structured Network","date":"2017-11-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dialogue-act-sequence-labeling-using","title":"Dialogue Act Sequence Labeling using Hierarchical encoder with CRF","date":"2017-09-13","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}