@InProceedings{zhao-EtAl:2017:EMNLP20172,
  author    = {Zhao, Jie  and  Su, Yu  and  Guan, Ziyu  and  Sun, Huan},
  title     = {An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1276--1282},
  abstract  = {Given a question and a set of answer candidates, answer triggering determines
	whether the candidate set contains any correct answers. If yes, it then outputs
	a correct one. In contrast to existing pipeline methods which first consider
	individual candidate answers separately and then make a prediction based on a
	threshold, we propose an end-to-end deep neural network framework, which is
	trained by a novel group-level objective function that directly optimizes the
	answer triggering performance. Our objective function penalizes three potential
	types of error and allows training the framework in an end-to-end manner.
	Experimental results on the WikiQA benchmark show that our framework
	outperforms the state of the arts by a 6.6% absolute gain under F1 measure.},
  url       = {https://www.aclweb.org/anthology/D17-1131}
}