Paper: A Neural Network for Factoid Question Answering over Paragraphs

ACL ID D14-1070
Title A Neural Network for Factoid Question Answering over Paragraphs
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Text classification methods for tasks like factoid question answering typi- cally use manually defined string match- ing rules or bag of words representa- tions. 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 composition- ality. 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, ri- vals the best human players.