{"type":"ScholarlyArticle","title":"RDF-to-Text Generation with Graph-augmented Structural Neural Encoders","authors":[{"name":"Hanning Gao","slug":"hanning-gao","given":"Hanning","family":"Gao","affiliation":"School of Computer Science , Central China Normal University , Wuhan , China"},{"name":"Lingfei Wu","slug":"lingfei-wu","given":"Lingfei","family":"Wu","affiliation":"IBM Thomas J. Watson Research Center , Yorktown Heights , NY , USA"},{"name":"Po Hu","slug":"po-hu","given":"Po","family":"Hu","affiliation":"School of Computer Science , Central China Normal University , Wuhan , China; Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning , Wuhan , China"},{"name":"Fangli Xu","slug":"fangli-xu","given":"Fangli","family":"Xu","affiliation":"Squirrel AI Learning"}],"abstract":"The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples. Experimental results on two different WebNLG datasets show that our proposed model outperforms the state-of-the-art baselines. Furthermore, we perform a human evaluation that demonstrates the effectiveness of the proposed method by evaluating generated text quality using various subjective metrics.","doi":"10.24963/ijcai.2020/419","url":"https://ijcai.org/proceedings/2020/419","pdf":"https://ijcai.org/proceedings/2020/0419.pdf","markdown":"https://ijcai.org/proceedings/2020/419.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/419","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":419,"pages":{"first":3030,"last":3036},"keywords":["Machine Learning: Relational Learning","Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Natural Language Processing: Natural Language Generation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24560/file_24560.m3u8","length_sec":299,"provider":"hls"}],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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