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The use of attention makes explicit the manner in which\ninformation is propagated between different locations in the sentence, which we\nuse to both analyze our model and propose potential improvements. For example,\nwe find that separating positional and content information in the encoder can\nlead to improved parsing accuracy. Additionally, we evaluate different\napproaches for lexical representation. Our parser achieves new state-of-the-art\nresults for single models trained on the Penn Treebank: 93.55 F1 without the\nuse of any external data, and 95.13 F1 when using pre-trained word\nrepresentations. Our parser also outperforms the previous best-published\naccuracy figures on 8 of the 9 languages in the SPMRL dataset.","url_abs":"http://arxiv.org/abs/1805.01052v1","url_pdf":"http://arxiv.org/pdf/1805.01052v1.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":"constituency-parsing-with-a-self-attentive","repo_url":"https://github.com/nikitakit/self-attentive-parser","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"constituency-parsing-with-a-self-attentive","repo_url":"https://github.com/asadovsky/nn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"constituency-parsing-with-a-self-attentive","repo_url":"https://github.com/napakalas/NLIMED","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"constituency-parsing-with-a-self-attentive","repo_url":"https://github.com/ringos/nfc-parser","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"constituency-parsing-with-a-self-attentive","repo_url":"https://github.com/thomaslu2000/incremental-parsing-representations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"constituency-parsing","task_name":"Constituency Parsing"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/constituency-parsing-on-ctb5","task":"Constituency Parsing","dataset":"CTB5","model":"Kitaev etal. 2018","rank_in_archive_order":9,"of":9,"metrics":{"F1 score":"87.43"},"uses_additional_data":false},{"leaderboard":"/sota/constituency-parsing-on-penn-treebank","task":"Constituency Parsing","dataset":"Penn Treebank","model":"Self-attentive encoder + ELMo","rank_in_archive_order":15,"of":27,"metrics":{"F1 score":"95.13"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.01052","atlas_url":"https://app.syntology.ai/?focus=1805.01052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.01052"}},"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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