@InProceedings{iyyer-yih-chang:2017:Long,
  author    = {Iyyer, Mohit  and  Yih, Wen-tau  and  Chang, Ming-Wei},
  title     = {Search-based Neural Structured Learning for Sequential 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     = {1821--1831},
  abstract  = {Recent work in semantic parsing for question answering has focused on long and
	complicated questions, many of which would seem unnatural if asked in a normal
	conversation between two humans. In an effort to explore a conversational QA
	setting, we present a more realistic task: answering sequences of simple but
	inter-related questions. We collect a dataset of 6,066 question sequences that
	inquire about semi-structured tables from Wikipedia, with 17,553
	question-answer pairs in total. To solve this sequential question answering
	task, we propose a novel dynamic neural semantic parsing framework trained
	using a weakly supervised reward-guided search. Our model effectively leverages
	the sequential context to outperform state-of-the-art QA systems that are
	designed to answer highly complex questions.},
  url       = {http://aclweb.org/anthology/P17-1167}
}