{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T16:26:31Z","timestamp":1745079991959},"reference-count":0,"publisher":"MIT Press - Journals","license":[{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages. We introduce Universal Discourse Representation Theory (UDRT), a variant of DRT that explicitly anchors semantic representations to tokens in the linguistic input. We develop a semantic parsing framework based on the Transformer architecture and utilize it to obtain semantic resources in multiple languages following two learning schemes. The Many-to-One approach translates non-English text to English, and then runs a relatively accurate English parser on the translated text, while the One-to-Many approach translates gold standard English to non-English text and trains multiple parsers (one per language) on the translations. Experimental results on the Parallel Meaning Bank show that our proposal outperforms strong baselines by a wide margin and can be used to construct (silver-standard) meaning banks for 99 languages.<\/jats:p>","DOI":"10.1162\/coli_a_00406","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T15:19:40Z","timestamp":1617031180000},"page":"1-32","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":2,"title":["Universal Discourse Representation Structure Parsing"],"prefix":"10.1162","author":[{"given":"Jiangming","family":"Liu","sequence":"first","affiliation":[{"name":"School of Informatics, University of Edinburgh. jiangming.liu@ed.ac.uk"}]},{"given":"Shay B.","family":"Cohen","sequence":"additional","affiliation":[{"name":"School of Informatics, University of Edinburgh. scohen@inf.ed.ac.uk"}]},{"given":"Mirella","family":"Lapata","sequence":"additional","affiliation":[{"name":"School of Informatics, University of Edinburgh. mlap@inf.ed.ac.uk"}]},{"given":"Johan","family":"Bos","sequence":"additional","affiliation":[{"name":"Center for Language and Cognition, University of Groningen. johan.bos@rug.nl"}]}],"member":"281","published-online":{"date-parts":[[2021,5,20]]},"container-title":["Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/direct.mit.edu\/coli\/article-pdf\/doi\/10.1162\/coli_a_00406\/1919751\/coli_a_00406.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/direct.mit.edu\/coli\/article-pdf\/doi\/10.1162\/coli_a_00406\/1919751\/coli_a_00406.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T18:43:49Z","timestamp":1621622629000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/coli\/article\/doi\/10.1162\/coli_a_00406\/98515\/Universal-Discourse-Representation-Structure"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,20]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1162\/coli_a_00406","relation":{},"ISSN":["0891-2017","1530-9312"],"issn-type":[{"value":"0891-2017","type":"print"},{"value":"1530-9312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,20]]}}}