{"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/linguistic-information-in-neural-semantic","title":"Linguistic Information in Neural Semantic Parsing with Multiple Encoders","arxiv_id":null,"date":"2019-05-01","proceeding":"WS 2019 5","authors":["Rik van Noord","Antonio Toral","Johan Bos"],"abstract":"Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.","url_abs":"https://aclanthology.org/W19-0504","url_pdf":"https://aclanthology.org/W19-0504.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":[],"tasks":[{"task_slug":"drs-parsing","task_name":"DRS Parsing"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drs-parsing-on-pmb-2-2-0","task":"DRS Parsing","dataset":"PMB-2.2.0","model":"Character-level bi-LSTM seq2seq + linguistic features","rank_in_archive_order":3,"of":6,"metrics":{"F1":"86.8"},"uses_additional_data":false},{"leaderboard":"/sota/drs-parsing-on-pmb-3-0-0","task":"DRS Parsing","dataset":"PMB-3.0.0","model":"Character-level bi-LSTM seq2seq + linguistic features","rank_in_archive_order":2,"of":3,"metrics":{"F1":"87.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}