A Neural Network for Factoid Question Answering over Paragraphs

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 633–644, October 25-29, 2014, Doha, Qatar. c⃝2014 Association for Computational Linguistics A Neural Network for Factoid Question Answering over Paragraphs Mohit Iyyer1, Jordan Boyd-Graber2, Leonardo Claudino1, Richard Socher3, Hal Daum´e III1 1University of Maryland, Department of Computer Science and umiacs 2University of Colorado, Department of Computer Science 3Stanford University, Department of Computer Science {miyyer,claudino,hal}@umiacs.umd.edu, Jordan.Boyd.Graber@colorado.edu, richard@socher.org Abstract 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 Introduction Deep neural networks have seen widespread use in natural language processing tasks such as parsing, language modeling, and sentiment analysis (Bengio et al., 2003; Socher et al., 2013a; Socher et al., 2013c). The vector spaces learned by these models cluster words and phrases together based on similarity. For exam- ple, a neural network trained for a sentiment analysis task such as restaurant review classifi- cation might learn that “tasty” and “delicious” should have similar representations since they are synonymous adjectives. These models have so far only seen success in a limited range of text-based prediction tasks, Later in its existence, this polity’s leader was chosen by a group that included three bishops and six laymen, up from the seven who traditionally made the decision. Free imperial cities in this polity included Basel and Speyer. Dissolved in 1806, its key events included the Investiture Controversy and the Golden Bull of 1356. Led by Charles V, Frederick Barbarossa, and Otto I, for 10 points, name this polity, which ruled most of what is now Germany through the Middle Ages and rarely ruled its titular city. Figure 1: An example quiz bowl question about the Holy Roman Empire. The first sentence contains no words or named entities that by themselves are indicative of the answer, while subsequent sentences contain more and more obvious clues. where inputs are typically a single sentence and outputs are either continuous or a limited dis- crete set. Neural networks have not yet shown to be useful for tasks that require mapping paragraph-length inputs to rich output spaces. Consider factoid question answering: given a description of an entity, identify the per- son, place, or thing discussed. We describe a task with high-quality mappings from natural language text to entities in Section 2. This task—quiz bowl—is a challenging natural lan- guage problem with large amounts of diverse and compositional data. …

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