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Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMR-to-text generation and syntax-based neural machine translation. <\/jats:p>","DOI":"10.1162\/tacl_a_00269","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T17:40:02Z","timestamp":1560966002000},"page":"297-312","source":"Crossref","is-referenced-by-count":75,"title":["Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning"],"prefix":"10.1162","volume":"7","author":[{"given":"Zhijiang","family":"Guo","sequence":"first","affiliation":[{"name":"Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, 8 Somapah Road, Singapore, 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