{"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/convamr-abstract-meaning-representation","title":"ConvAMR: Abstract meaning representation parsing for legal document","arxiv_id":"1711.06141","date":"2017-11-16","proceeding":null,"authors":["Lai Dac Viet","Vu Trong Sinh","Nguyen Le Minh","Ken Satoh"],"abstract":"Convolutional neural networks (CNN) have recently achieved remarkable\nperformance in a wide range of applications. In this research, we equip\nconvolutional sequence-to-sequence (seq2seq) model with an efficient graph\nlinearization technique for abstract meaning representation parsing. Our\nlinearization method is better than the prior method at signaling the turn of\ngraph traveling. Additionally, convolutional seq2seq model is more appropriate\nand considerably faster than the recurrent neural network models in this task.\nOur method outperforms previous methods by a large margin on both the standard\ndataset LDC2014T12. Our result indicates that future works still have a room\nfor improving parsing model using graph linearization approach.","url_abs":"http://arxiv.org/abs/1711.06141v2","url_pdf":"http://arxiv.org/pdf/1711.06141v2.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":"convamr-abstract-meaning-representation","repo_url":"https://github.com/laiviet/ConvAMR-torch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"abstract-meaning-representation","task_name":"Abstract Meaning Representation"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"seq2seq","method_name":"Seq2Seq"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}