@conference {2583,
	title = {Learning to Answer Questions from Wikipedia Infoboxes},
	booktitle = {The 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP 2016)},
	year = {2016},
	abstract = {<p>A natural language interface to answers on the Web can help us access information more ef- ficiently.\&nbsp; We start with an interesting source of information{\textemdash}infoboxes\&nbsp; in Wikipedia that summarize factoid knowledge{\textemdash}and develop a comprehensive\&nbsp; approach\&nbsp; to\&nbsp; answering\&nbsp; ques- tions\&nbsp; with\&nbsp; high\&nbsp; precision.\&nbsp;\&nbsp;\&nbsp; We\&nbsp; first\&nbsp; build\&nbsp; a system to access data in infoboxes in a struc- tured manner. We use our system to construct a crowdsourced dataset of over 15,000 high- quality,\&nbsp; diverse\&nbsp; questions.\&nbsp;\&nbsp; With\&nbsp; these\&nbsp; ques- tions, we train a convolutional neural network model\&nbsp; that\&nbsp; outperforms\&nbsp; models\&nbsp; that\&nbsp; achieve top results in similar answer selection tasks.</p>
},
	author = {Alvaro Morales and Varot Premtoon and Cordelia Avery and Sue Felshin and Boris Katz}
}
