@InProceedings{talmor-geva-berant:2017:starSEM,
  author    = {Talmor, Alon  and  Geva, Mor  and  Berant, Jonathan},
  title     = {Evaluating Semantic Parsing against a Simple Web-based Question Answering Model},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {161--167},
  abstract  = {Semantic parsing shines at analyzing complex natural language that involves
	composition and computation over multiple pieces of evidence. However, datasets
	for semantic parsing contain many factoid questions that can be answered from a
	single web document. In this paper, we propose to evaluate semantic
	parsing-based question answering models by comparing them to a question
	answering baseline that queries the web and extracts the answer only from web
	snippets, without access to the target knowledge-base. We investigate this
	approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional
	language, and find that our model obtains reasonable performance (∼35 F1
	compared to 41 F1 of state-of-the-art). We find in our analysis that our model
	performs well on complex questions involving conjunctions, but struggles on
	questions that involve relation composition and superlatives.},
  url       = {http://www.aclweb.org/anthology/S17-1020}
}