A Neural Network for Factoid Question Answering over Paragraphs

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. 1