Research

Papers related to WebNLG are listed below. Please send an email to webnlg2017@inria.fr if you want your research to appear on this page.

Gao, H., Wu, L., Hu, P., & Xu, F. (2020). RDF-to-text generation with graph-augmented structural neural encoders. In C. Bessiere (Ed.), Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI-20 (pp. 3030–3036). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2020/419
Main track

Liu, J., Chen, S., Wang, B., Zhang, J., Li, N., & Xu, T. (2020). Attention as relation: Learning supervised multi-head self-attention for relation extraction. In C. Bessiere (Ed.), Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI-20 (pp. 3787–3793). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2020/524
Main track

Sellam, T., Das, D., & Parikh, A. (2020). BLEURT: Learning robust metrics for text generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7881–7892. https://doi.org/10.18653/v1/2020.acl-main.704

Shen, X., Chang, E., Su, H., Niu, C., & Klakow, D. (2020). Neural data-to-text generation via jointly learning the segmentation and correspondence. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7155–7165. https://doi.org/10.18653/v1/2020.acl-main.641

Song, L., Wang, A., Su, J., Zhang, Y., Xu, K., Ge, Y., & Yu, D. (2020). Structural information preserving for graph-to-text generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7987–7998. https://doi.org/10.18653/v1/2020.acl-main.712

Wei, Z., Su, J., Wang, Y., Tian, Y., & Chang, Y. (2020). A novel cascade binary tagging framework for relational triple extraction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1476–1488. https://doi.org/10.18653/v1/2020.acl-main.136

Zhao, C., Walker, M., & Chaturvedi, S. (2020). Bridging the structural gap between encoding and decoding for data-to-text generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2481–2491. https://doi.org/10.18653/v1/2020.acl-main.224

Laha, A., Jain, P., Mishra, A., & Sankaranarayanan, K. (2020). Scalable micro-planned generation of discourse from structured data. Computational Linguistics, 45(4), 737–763. https://doi.org/10.1162/coli\_a\_00363

Mille, S., Dasiopoulou, S., Fisas, B., & Wanner, L. (2019a). Teaching FORGe to verbalize DBpedia properties in Spanish. Proceedings of the 12th International Conference on Natural Language Generation, 473–483. https://doi.org/10.18653/v1/W19-8659

Moryossef, A., Goldberg, Y., & Dagan, I. (2019a). Improving quality and efficiency in plan-based neural data-to-text generation. Proceedings of the 12th International Conference on Natural Language Generation, 377–382. https://doi.org/10.18653/v1/W19-8645

Cao, M., & Cheung, J. C. K. (2019). Referring expression generation using entity profiles. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (Emnlp-Ijcnlp), 3163–3172. https://doi.org/10.18653/v1/D19-1312

Castro Ferreira, T., Lee, C. van der, Miltenburg, E. van, & Krahmer, E. (2019). Neural data-to-text generation: A comparison between pipeline and end-to-end architectures. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (Emnlp-Ijcnlp), 552–562. https://doi.org/10.18653/v1/D19-1052

Cui, W., Zhou, M., Zhao, R., & Norouzi, N. (2019). KB-NLG: From knowledge base to natural language generation. Proceedings of the 2019 Workshop on Widening Nlp, 80–82.

Shimorina, A., Khasanova, E., & Gardent, C. (2019). Creating a corpus for Russian data-to-text generation using neural machine translation and post-editing. Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, 44–49. https://doi.org/10.18653/v1/W19-3706

Fu, T.-J., Li, P.-H., & Ma, W.-Y. (2019). GraphRel: Modeling text as relational graphs for joint entity and relation extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1409–1418. https://doi.org/10.18653/v1/P19-1136

Moryossef, A., Goldberg, Y., & Dagan, I. (2019b). Step-by-step: Separating planning from realization in neural data-to-text generation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2267–2277. https://doi.org/10.18653/v1/N19-1236

Kilias, T., Löser, A., Gers, F. A., Zhang, Y., Koopmanschap, R., & Kersten, M. L. (2019). IDEL: in-database neural entity linking. IEEE International Conference on Big Data and Smart Computing, Bigcomp 2019, Kyoto, Japan, February 27 - March 2, 2019, 1–8. https://doi.org/10.1109/BIGCOMP.2019.8679486

Mille, S., Dasiopoulou, S., & Wanner, L. (2019b). A portable grammar-based nlg system for verbalization of structured data. Proceedings of the 34th Acm/Sigapp Symposium on Applied Computing, 1054–1056. https://doi.org/10.1145/3297280.3297571

Zhu, Y., Wan, J., Zhou, Z., Chen, L., Qiu, L., Zhang, W., Jiang, X., & Yu, Y. (2019). Triple-to-text: Converting rdf triples into high-quality natural languages via optimizing an inverse kl divergence. Proceedings of the 42nd International Acm Sigir Conference on Research and Development in Information Retrieval, 455–464. https://doi.org/10.1145/3331184.3331232

Colin, E., & Gardent, C. (2018). Generating syntactic paraphrases. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 937–943. https://doi.org/10.18653/v1/D18-1113

Castro Ferreira, T., Moussallem, D., Krahmer, E., & Wubben, S. (2018a). Enriching the WebNLG corpus. Proceedings of the 11th International Conference on Natural Language Generation, 171–176. https://doi.org/10.18653/v1/W18-6521

Jagfeld, G., Jenne, S., & Vu, N. T. (2018). Sequence-to-sequence models for data-to-text natural language generation: Word- vs. character-based processing and output diversity. Proceedings of the 11th International Conference on Natural Language Generation, 221–232. https://doi.org/10.18653/v1/W18-6529

Marcheggiani, D., & Perez-Beltrachini, L. (2018). Deep graph convolutional encoders for structured data to text generation. Proceedings of the 11th International Conference on Natural Language Generation, 1–9. https://doi.org/10.18653/v1/W18-6501

Shimorina, A., & Gardent, C. (2018). Handling rare items in data-to-text generation. Proceedings of the 11th International Conference on Natural Language Generation, 360–370. https://doi.org/10.18653/v1/W18-6543

Castro Ferreira, T., Moussallem, D., Kádár, Á., Wubben, S., & Krahmer, E. (2018b). NeuralREG: An end-to-end approach to referring expression generation. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1959–1969. https://doi.org/10.18653/v1/P18-1182

Trisedya, B. D., Qi, J., Zhang, R., & Wang, W. (2018). GTR-LSTM: A triple encoder for sentence generation from RDF data. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1627–1637. https://doi.org/10.18653/v1/P18-1151

Zeng, X., Zeng, D., He, S., Liu, K., & Zhao, J. (2018). Extracting relational facts by an end-to-end neural model with copy mechanism. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 506–514. https://doi.org/10.18653/v1/P18-1047

Moussallem, D., Ferreira, T., Zampieri, M., Cavalcanti, M. C., Xexéo, G., Neves, M., & Ngonga Ngomo, A.-C. (2018, May). RDF2PT: Generating Brazilian Portuguese texts from RDF data. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). https://www.aclweb.org/anthology/L18-1481

Shimorina, A. (2018). Human vs automatic metrics: On the importance of correlation design. CoRR, abs/1805.11474. http://arxiv.org/abs/1805.11474

Gardent, C., Shimorina, A., Narayan, S., & Perez-Beltrachini, L. (2017a). The WebNLG challenge: Generating text from RDF data. Proceedings of the 10th International Conference on Natural Language Generation, 124–133. https://doi.org/10.18653/v1/W17-3518

Narayan, S., Gardent, C., Cohen, S. B., & Shimorina, A. (2017). Split and rephrase. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 606–616. https://doi.org/10.18653/v1/D17-1064

Gardent, C., Shimorina, A., Narayan, S., & Perez-Beltrachini, L. (2017b). Creating training corpora for NLG micro-planners. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 179–188. https://doi.org/10.18653/v1/P17-1017

Colin, E., Gardent, C., M’rabet, Y., Narayan, S., & Perez-Beltrachini, L. (2016). The WebNLG challenge: Generating text from DBPedia data. Proceedings of the 9th International Natural Language Generation Conference, 163–167. https://doi.org/10.18653/v1/W16-6626

Perez-Beltrachini, L., Sayed, R., & Gardent, C. (2016). Building RDF content for data-to-text generation. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 1493–1502. https://www.aclweb.org/anthology/C16-1141