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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>\n            Frame-semantic Parsing (FSP) is a challenging and critical task in Natural Language Processing (NLP). Most of the existing studies decompose the FSP task into frame identification (FI) and frame semantic role labeling (FSRL) subtasks, and adopt a pipeline model architecture that clearly causes error propagation problem. However, recent jointly learning models aim to address the above problem and generally treat FSP as a span-level structured prediction task, which, unfortunately, leads to cascading error propagation problem between roles and less-efficient solutions due to huge search space of roles. To address these problems, we reformulate the FSRL task into a\n            <jats:italic>target-aware relation classification task<\/jats:italic>\n            and propose a novel and lightweight jointly learning framework that simultaneously processes three subtasks of FSP, including frame identification, argument identification, and role classification. The novel task formulation and jointly learning with interaction mechanisms among subtasks can help improve the overall system performance and reduce the search space and time complexity, compared with existing methods. Extensive experimental results demonstrate that our proposed model significantly outperforms 10 state-of-the-art models in terms of F1 score across two benchmark datasets.\n          <\/jats:p>","DOI":"10.1145\/3569581","type":"journal-article","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T12:25:16Z","timestamp":1666873516000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Span-based Target-aware Relation Model for Frame-semantic Parsing"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8954-4412","authenticated-orcid":false,"given":"Xuefeng","family":"Su","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University; School of Modern Logistics, Shanxi Vocational University of Engineering Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1545-5553","authenticated-orcid":false,"given":"Ru","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0762-6562","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*Star, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2824-6750","authenticated-orcid":false,"given":"Baobao","family":"Chang","sequence":"additional","affiliation":[{"name":"The MOE Key Laboratory of Computational Linguistics, Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8246-4700","authenticated-orcid":false,"given":"Zhiwei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8827-8474","authenticated-orcid":false,"given":"Xiaoqi","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-8886","authenticated-orcid":false,"given":"Zhichao","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3115\/1621474.1621492"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.3115\/980845.980860"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.227"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00163"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_3_7_2","article-title":"Deep biaffine attention for neural dependency parsing","volume":"1611","author":"Dozat Timothy","year":"2017","unstructured":"Timothy Dozat and Christopher D. Manning. 2017. Deep biaffine attention for neural dependency parsing. ArXiv abs\/1611.01734 (2017).","journal-title":"ArXiv"},{"key":"e_1_3_3_8_2","article-title":"Span-based joint entity and relation extraction with transformer pre-training","volume":"1909","author":"Eberts Markus","year":"2019","unstructured":"Markus Eberts and Adrian Ulges. 2019. Span-based joint entity and relation extraction with transformer pre-training. CoRR abs\/1909.07755 (2019).","journal-title":"CoRR"},{"key":"e_1_3_3_9_2","unstructured":"C. Fillmore and Collin F. Baker. 2001. Frame semantics for text understanding. In Proceedings of the NAACL WordNet and Other Lexical Resources Workshop . Association for Computational Linguistics Pittsburgh."},{"issue":"2","key":"e_1_3_3_10_2","first-page":"222","article-title":"Frames and the semantics of understanding","volume":"6","author":"Fillmore C. J.","year":"1985","unstructured":"C. J. Fillmore. 1985. Frames and the semantics of understanding. 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