{"url":"/dataset/ldc2017t10","name":"LDC2017T10","full_name":"Abstract Meaning Representation (AMR) Annotation Release 2.0","description_markdown":"Abstract Meaning Representation (AMR) Annotation Release 2.0 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 39,260 English natural language sentences from broadcast conversations, newswire, weblogs and web discussion forums.\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/LDC2017T10","introduced_date":"2017-06-15","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":["LDC2017T10"],"data_loaders":[{"repo":"https://github.com/qaq-v/hetgt","url":"https://github.com/qaq-v/hetgt","frameworks":["pytorch"]}],"num_papers_in_archive":27,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/amr-parsing-on-ldc2017t10","task":"AMR Parsing","dataset_variant":"LDC2017T10","rows":27,"metrics":["Smatch"],"first_row_in_archive_order":{"model":"StructBART + MBSE (IBM)","paper":"/paper/maximum-bayes-smatch-ensemble-distillation","metrics":{"Smatch":"86.7"},"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":2,"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/structure-aware-fine-tuning-of-sequence-to","title":"Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing","date":"2021-10-29","rows_on_this_dataset":1,"code_links":1,"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/levi-graph-amr-parser-using-heterogeneous","title":"Levi Graph AMR Parser using Heterogeneous Attention","date":"2021-07-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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":1,"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."}},{"paper":"/paper/a-differentiable-relaxation-of-graph","title":"A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing","date":"2020-10-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/transition-based-parsing-with-stack","title":"Transition-based Parsing with Stack-Transformers","date":"2020-10-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pushing-the-limits-of-amr-parsing-with-self","title":"Pushing the Limits of AMR Parsing with Self-Learning","date":"2020-10-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/improving-amr-parsing-with-sequence-to","title":"Improving AMR Parsing with Sequence-to-Sequence Pre-training","date":"2020-10-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/amr-parsing-via-graph-sequence-iterative","title":"AMR Parsing via Graph-Sequence Iterative Inference","date":"2020-04-12","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":31,"samples_ran":17,"samples_unverified":14,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/core-semantic-first-a-top-down-approach-for","title":"Core Semantic First: A Top-down Approach for AMR Parsing","date":"2019-09-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/broad-coverage-semantic-parsing-as","title":"Broad-Coverage Semantic Parsing as Transduction","date":"2019-09-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rewarding-smatch-transition-based-amr-parsing","title":"Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning","date":"2019-05-31","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/amr-parsing-as-sequence-to-graph-transduction","title":"AMR Parsing as Sequence-to-Graph Transduction","date":"2019-05-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/amr-parsing-as-graph-prediction-with-latent","title":"AMR Parsing as Graph Prediction with Latent Alignment","date":"2018-05-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/oxford-at-semeval-2017-task-9-neural-amr","title":"Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention","date":"2017-08-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/neural-semantic-parsing-by-character-based","title":"Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations","date":"2017-05-28","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":4,"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":5,"samples_harvested":45,"samples_ran":28,"samples_unverified":17,"pointer_only_for_licence":5,"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."}