{"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/graph-to-sequence-learning-using-gated-graph","title":"Graph-to-Sequence Learning using Gated Graph Neural Networks","arxiv_id":"1806.09835","date":"2018-06-26","proceeding":"ACL 2018 7","authors":["Daniel Beck","Gholamreza Haffari","Trevor Cohn"],"abstract":"Many NLP applications can be framed as a graph-to-sequence learning problem.\nPrevious work proposing neural architectures on this setting obtained promising\nresults compared to grammar-based approaches but still rely on linearisation\nheuristics and/or standard recurrent networks to achieve the best performance.\nIn this work, we propose a new model that encodes the full structural\ninformation contained in the graph. Our architecture couples the recently\nproposed Gated Graph Neural Networks with an input transformation that allows\nnodes and edges to have their own hidden representations, while tackling the\nparameter explosion problem present in previous work. Experimental results show\nthat our model outperforms strong baselines in generation from AMR graphs and\nsyntax-based neural machine translation.","url_abs":"http://arxiv.org/abs/1806.09835v1","url_pdf":"http://arxiv.org/pdf/1806.09835v1.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":"graph-to-sequence-learning-using-gated-graph","repo_url":"https://github.com/beckdaniel/acl2018_graph2seq","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"mxnet","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"graph-to-sequence-learning-using-gated-graph","repo_url":"https://github.com/Cartus/DCGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-to-sequence","task_name":"Graph-to-Sequence"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1806.09835","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.09835"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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