{"url":"/dataset/cdcp","name":"CDCP","full_name":"Cornell eRulemaking Corpus","description_markdown":"The Cornell eRulemaking Corpus – CDCP is an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. The corpus consists of 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB); the resulting dataset contains 4931 elementary unit and 1221 support relation annotations. It is a resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify what additional information is necessary\r\nfor readers to understand and evaluate a given argument. Immediate applications include providing real-time feedback to commenters, specifying which types of support for which propositions can be added to construct better-formed arguments.","description_withheld":null,"homepage":"https://facultystaff.richmond.edu/~jpark/","introduced_date":"2018-05-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-corpus-of-erulemaking-user-comments-for","title":"A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments","first_author":"Joonsuk Park","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"},{"name":"Relation Classification","url":"/task/relation-classification","datasets_with_task":"/datasets/task/relation-classification"},{"name":"Component Classification","url":"/task/component-classification","datasets_with_task":"/datasets/task/component-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CDCP"],"data_loaders":[],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/component-classification-on-cdcp","task":"Component Classification","dataset_variant":"CDCP","rows":1,"metrics":["Macro F1"],"first_row_in_archive_order":{"model":"ResAttArg","paper":"/paper/multi-task-attentive-residual-networks-for","metrics":{"Macro F1":"78.71"},"code_links":[{"title":"AGalassi/StructurePrediction18","url":"https://github.com/AGalassi/StructurePrediction18"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/link-prediction-on-cdcp","task":"Link Prediction","dataset_variant":"CDCP","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"ResAttArg","paper":"/paper/multi-task-attentive-residual-networks-for","metrics":{"F1":"29.73"},"code_links":[{"title":"AGalassi/StructurePrediction18","url":"https://github.com/AGalassi/StructurePrediction18"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-classification-on-cdcp","task":"Relation Classification","dataset_variant":"CDCP","rows":1,"metrics":["Macro F1"],"first_row_in_archive_order":{"model":"ResAttArg","paper":"/paper/multi-task-attentive-residual-networks-for","metrics":{"Macro F1":"42.95"},"code_links":[{"title":"AGalassi/StructurePrediction18","url":"https://github.com/AGalassi/StructurePrediction18"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/multi-task-attentive-residual-networks-for","title":"Multi-Task Attentive Residual Networks for Argument Mining","date":"2021-02-24","rows_on_this_dataset":3,"code_links":1,"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."}