@InProceedings{yu-EtAl:2017:Long,
  author    = {Yu, Mo  and  Yin, Wenpeng  and  Hasan, Kazi Saidul  and  dos Santos, Cicero  and  Xiang, Bing  and  Zhou, Bowen},
  title     = {Improved Neural Relation Detection for Knowledge Base Question Answering},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {571--581},
  abstract  = {Relation detection is a core component of many NLP applications including
	Knowledge Base Question Answering (KBQA). In this paper, we propose a
	hierarchical recurrent neural network enhanced by residual learning which
	detects KB relations given an input question. Our method uses deep residual
	bidirectional LSTMs to compare questions and relation names via different
	levels of abstraction. Additionally, we propose a simple KBQA system that
	integrates entity linking and our proposed relation detector to make the two
	components enhance each other. Our experimental results show that our approach
	not only achieves outstanding relation detection performance, but more
	importantly, it helps our KBQA system achieve state-of-the-art accuracy for
	both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA
	benchmarks.},
  url       = {http://aclweb.org/anthology/P17-1053}
}