@InProceedings{hopkins-EtAl:2017:EMNLP2017,
  author    = {Hopkins, Mark  and  Petrescu-Prahova, Cristian  and  Levin, Roie  and  Le Bras, Ronan  and  Herrasti, Alvaro  and  Joshi, Vidur},
  title     = {Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers},
  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     = {795--804},
  abstract  = {We present an approach for answering questions that span multiple sentences and
	exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich
	source of such questions -- the math portion of the Scholastic Aptitude Test
	(SAT). By using a tree transducer cascade as its basic architecture, our system
	propagates uncertainty from multiple sources (e.g. coreference resolution or
	verb interpretation) until it can be confidently resolved. Experiments show the
	first-ever results 43% recall and 91% precision) on SAT algebra word problems.
	We also apply our system to the public Dolphin algebra question set, and
	improve the state-of-the-art F1-score from 73.9% to 77.0%.},
  url       = {https://www.aclweb.org/anthology/D17-1083}
}