{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-semantic-parsing-by-character-based","title":"Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations","arxiv_id":"1705.09980","date":"2017-05-28","proceeding":null,"authors":["Rik van Noord","Johan Bos"],"abstract":"We evaluate the character-level translation method for neural semantic\nparsing on a large corpus of sentences annotated with Abstract Meaning\nRepresentations (AMRs). Using a sequence-to-sequence model, and some trivial\npreprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1\n(F-score on AMR-triples). We examine five different approaches to improve this\nbaseline result: (i) reordering AMR branches to match the word order of the\ninput sentence increases performance to 58.3; (ii) adding part-of-speech tags\n(automatically produced) to the input shows improvement as well (57.2); (iii)\nSo does the introduction of super characters (conflating frequent sequences of\ncharacters to a single character), reaching 57.4; (iv) optimizing the training\nprocess by using pre-training and averaging a set of models increases\nperformance to 58.7; (v) adding silver-standard training data obtained by an\noff-the-shelf parser yields the biggest improvement, resulting in an F-score of\n64.0. Combining all five techniques leads to an F-score of 71.0 on holdout\ndata, which is state-of-the-art in AMR parsing. This is remarkable because of\nthe relative simplicity of the approach.","url_abs":"http://arxiv.org/abs/1705.09980v2","url_pdf":"http://arxiv.org/pdf/1705.09980v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-semantic-parsing-by-character-based","repo_url":"https://github.com/RikVN/AMR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"neural-semantic-parsing-by-character-based","repo_url":"https://github.com/freesunshine0316/sembleu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"amr-parsing","task_name":"AMR Parsing"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/amr-parsing-on-ldc2017t10","task":"AMR Parsing","dataset":"LDC2017T10","model":"ChSeq + 100K","rank_in_archive_order":26,"of":27,"metrics":{"Smatch":"71.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.09980","atlas_url":"https://app.syntology.ai/?focus=1705.09980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.09980"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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