2014 ANeuralNetworkforFactoidQuestio

• (Iyyer et al., 2014) ⇒ Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daumé, III. (2014). “A Neural Network for Factoid Question Answering over Paragraphs.” In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP-2014).

Subject Headings: QANTA, Supervised Question Answering.

Cited By

• http://scholar.google.com/scholar?q=%222014%22+A+Neural+Network+for+Factoid+Question+Answering+over+Paragraphs

Quotes

Abstract

Text classification methods for tasks like factoid question answering typically use manually defined string matching rules or bag of words representations. These methods are ineffective when question text contains very few individual words (e.g., named entities) that are indicative of the answer. We introduce a recursive neural network (RNN) model that can reason over such input by modeling textual compositionality. We apply our model, QANTA, to a dataset of questions from a trivia competition called quiz bowl. Unlike previous RNN models, QANTA learns word and phrase-level representations that combine across sentences to reason about entities. The model outperforms multiple baselines and, when combined with information retrieval methods, rivals the best human players.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ANeuralNetworkforFactoidQuestio	Richard Socher Mohit Iyyer Leonardo Claudino Hal Daumé, III			A Neural Network for Factoid Question Answering over Paragraphs