{"url":"/dataset/ldc2020t02","name":"LDC2020T02","full_name":"Abstract Meaning Representation (AMR) Annotation Release 3.0","description_markdown":"Abstract Meaning Representation (AMR) Annotation Release 3.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc., the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 59,255 English natural language sentences from broadcast conversations, newswire, weblogs, web discussion forums, fiction and web text. This release adds new data to, and updates material contained in, Abstract Meaning Representation 2.0 (LDC2017T10), specifically: more annotations on new and prior data, new or improved PropBank-style frames, enhanced quality control, and multi-sentence annotations.\r\n\r\nAMR captures \"who is doing what to whom\" in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure of a sentence largely independent of its syntax.","description_withheld":null,"homepage":"https://catalog.ldc.upenn.edu/LDC2020T02","introduced_date":"2020-01-15","introduced_date_note":null,"introduced_by":null,"license":{"name":"LDC User Agreement for Non-Members","url":"https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"AMR Parsing","url":"/task/amr-parsing","datasets_with_task":"/datasets/task/amr-parsing"},{"name":"AMR-to-Text Generation","url":"/task/amr-to-text-generation","datasets_with_task":"/datasets/task/amr-to-text-generation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LDC2020T02"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/amr-parsing-on-ldc2020t02","task":"AMR Parsing","dataset_variant":"LDC2020T02","rows":13,"metrics":["Smatch"],"first_row_in_archive_order":{"model":"Graphene Smatch (MBSE paper) (IBM)","paper":"/paper/maximum-bayes-smatch-ensemble-distillation","metrics":{"Smatch":"85.4"},"code_links":[{"title":"IBM/transition-amr-parser","url":"https://github.com/IBM/transition-amr-parser"},{"title":"ibm/amr-annotations","url":"https://github.com/ibm/amr-annotations"},{"title":"pournaki/transition-amr-parser","url":"https://github.com/pournaki/transition-amr-parser"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/incorporating-graph-information-in","title":"Incorporating Graph Information in Transformer-based AMR Parsing","date":"2023-06-23","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/bibl-amr-parsing-and-generation-with","title":"BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning","date":"2022-10-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/atp-amrize-then-parse-enhancing-amr-parsing","title":"ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs","date":"2022-04-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/graph-pre-training-for-amr-parsing-and-1","title":"Graph Pre-training for AMR Parsing and Generation","date":"2022-03-15","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/maximum-bayes-smatch-ensemble-distillation","title":"Maximum Bayes Smatch Ensemble Distillation for AMR Parsing","date":"2021-12-14","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/ensembling-graph-predictions-for-amr-parsing","title":"Ensembling Graph Predictions for AMR Parsing","date":"2021-10-18","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/one-spring-to-rule-them-both-symmetric-amr","title":"One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline","date":"2021-05-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/amr-parsing-with-action-pointer-transformer","title":"AMR Parsing with Action-Pointer Transformer","date":"2021-04-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"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":3,"samples_harvested":10,"samples_ran":7,"samples_unverified":3,"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."}