{"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/unsupervised-latent-tree-induction-with-deep-1","title":"Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders","arxiv_id":null,"date":"2019-06-01","proceeding":"NAACL 2019 6","authors":["Andrew Drozdov","Patrick Verga","Mohit Yadav","Mohit Iyyer","Andrew McCallum"],"abstract":"We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence. During training we use dynamic programming to consider all possible binary trees over the sentence, and for inference we use the CKY algorithm to extract the highest scoring parse. DIORA outperforms previously reported results for unsupervised binary constituency parsing on the benchmark WSJ dataset.","url_abs":"https://aclanthology.org/N19-1116","url_pdf":"https://aclanthology.org/N19-1116.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":"unsupervised-latent-tree-induction-with-deep-1","repo_url":"https://github.com/iesl/diora","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"constituency-grammar-induction","task_name":"Constituency Grammar Induction"},{"task_slug":"constituency-parsing","task_name":"Constituency Parsing"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/constituency-grammar-induction-on-ptb","task":"Constituency Grammar Induction","dataset":"PTB Diagnostic ECG Database","model":"DIORA (+PP)","rank_in_archive_order":14,"of":24,"metrics":{"Max F1 (WSJ)":"56.2","Max F1 (WSJ10)":"60.55","Mean F1 (WSJ)":"55.7"},"uses_additional_data":false},{"leaderboard":"/sota/constituency-grammar-induction-on-ptb","task":"Constituency Grammar Induction","dataset":"PTB Diagnostic ECG Database","model":"DIORA","rank_in_archive_order":18,"of":24,"metrics":{"Max F1 (WSJ)":"49.6","Max F1 (WSJ10)":"68.5","Mean F1 (WSJ)":"48.9","Mean F1 (WSJ10)":"67.7"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}