Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts

Author:

Bhowmik Rajarshi1,de Melo Gerard1

Affiliation:

1. Rutgers University, USA

Publisher

ACM Press

Reference27 articles.

1. Sungjin Ahn, Heeyoul Choi, Tanel Pärnamaa, and Yoshua Bengio. 2016. A Neural Knowledge Language Model. CoRR abs/1608.00318(2016). arxiv:1608.00318http://arxiv.org/abs/1608.00318

2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. CoRR abs/1409.0473(2014). arxiv:1409.0473http://arxiv.org/abs/1409.0473

3. Rajarshi Bhowmik and Gerard Melo. 2018. Generating Fine-Grained Open Vocabulary Entity Type Descriptions. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 877-888. http://aclweb.org/anthology/P18-1081

4. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger(Eds.). Curran Associates, Inc., 2787-2795. http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf

5. Thiago Castro Ferreira, Diego Moussallem, Ákos Kádár, Sander Wubben, and Emiel Krahmer. 2018. NeuralREG: An end-to-end approach to referring expression generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 1959-1969. http://aclweb.org/anthology/P18-1182

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