@InProceedings{savenkov-agichtein:2017:Short,
  author    = {Savenkov, Denis  and  Agichtein, Eugene},
  title     = {EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {299--304},
  abstract  = {A critical task for question answering is the final answer selection stage,
	which has to combine multiple signals available about each answer candidate.
	This paper proposes EviNets: a novel neural network architecture for factoid
	question answering. EviNets scores candidate answer entities by combining the
	available supporting evidence, e.g., structured knowledge bases and
	unstructured text documents. EviNets represents each piece of evidence with a
	dense embeddings vector, scores their relevance to the question, and aggregates
	the support for each candidate to predict their final scores. Each of the
	components is generic and allows plugging in a variety of models for semantic
	similarity scoring and information aggregation. We demonstrate the
	effectiveness of EviNets in experiments on the existing TREC QA and WikiMovies
	benchmarks, and on the new Yahoo! Answers dataset introduced in this paper.
	EviNets can be extended to other information types and could facilitate future
	work on combining evidence signals for joint reasoning in question answering.},
  url       = {http://aclweb.org/anthology/P17-2047}
}