Deep Learning for Answer Sentence Selection Lei Yu1 Karl Moritz Hermann2 Phil Blunsom12 Stephen Pulman1 1Department of Computer Science, University of Oxford 2Google DeepMind {lei.yu, phil.blunsom,stephen.pulman}@cs.ox.ac.uk
[email protected] Abstract Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and learn to match questions with answers by considering their semantic encoding. This contrasts prior work on this task, which typically relies on classifiers with large numbers of hand-crafted syntactic and semantic features and various external resources. Our approach does not require any feature engineering nor does it involve specialist linguistic data, making this model easily applicable to a wide range of domains and languages. Experimental results on a standard benchmark dataset from TREC demonstrate that—despite its simplicity—our model matches state of the art performance on the answer sentence selection task. 1 Introduction Question answering can broadly be divided into two categories. One approach focuses on semantic parsing, where answers are retrieved by turning a question into a database query and subsequently applying that query to an existing knowledge base. The other category is open domain question answering, which is more closely related to the field of information retrieval. Open domain question answering requires a number of intermediate steps. For instance, to answer a question such as “Who wrote the book Harry Potter?”, a system would first identify the question arXiv:1412.1632v1 [cs.CL] 4 Dec 2014 type and retrieve relevant documents.