@InProceedings{bakhshandeh-allen:2017:Long,
  author    = {Bakhshandeh, Omid  and  Allen, James},
  title     = {Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task},
  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     = {906--916},
  abstract  = {Understanding common entities and their attributes is a primary requirement for
	any system that comprehends natural language. In order to enable learning about
	common entities, we introduce a novel machine comprehension task, GuessTwo:
	given a short paragraph comparing different aspects of two real-world
	semantically-similar entities, a system should guess what those entities are.
	Accomplishing this task requires deep language understanding which enables
	inference, connecting each comparison paragraph to different levels of
	knowledge about world entities and their attributes. So far we have
	crowdsourced a dataset of more than 14K comparison paragraphs comparing
	entities from a variety of categories such as fruits and animals. We have
	designed two schemes for evaluation: open-ended, and binary-choice prediction.
	For benchmarking further progress in the task, we have collected a set of
	paragraphs as the test set on which human can accomplish the task with an
	accuracy of 94.2\% on open-ended prediction. We have implemented various models
	for tackling the task, ranging from semantic-driven to neural models. The
	semantic-driven approach outperforms the neural models, however, the results
	indicate that the task is very challenging across the models.},
  url       = {http://aclweb.org/anthology/P17-1084}
}