Ferhan Ture
ABSTRACT
Voice-based interfaces are very popular in today's world, and Comcast customers are no exception. Usage stats show that our new X1 TV platform receives millions of voice queries per day. As a result, expanding the coverage of our voice interface provides a critical competitive advantage, allowing customers to speak freely instead of having to stick to a rigid set of commands. The ultimate objective is to provide a more natural user experience and increase access to our knowledge graph (KG) and entertainment platform.
We describe a real-time factoid question answering (QA) system, using our internal KG for training (i.e., generating labeled example question-answer pairs) and for retrieval at test time. We hope that this will inspire other companies to take advantage of readily available unlabeled data, machine learning and search technologies to build products that can improve customer experiences.Our approach consists of two steps: First, two neural network models are trained to predict a structured query from the free-form input question. Then, a search through all facts in the KG retrieves answers consistent with the structured query.
- A. Bordes, N. Usunier, S. Chopra, and J. Weston. Large-scale simple question answering with memory networks. CoRR, abs/1506.02075, 2015.Google Scholar
- L. Dong, F. Wei, M. Zhou, and K. Xu. Question answering over freebase with multi-column convolutional neural networks. In ACL, 2015.Google ScholarCross Ref
- M. Iyyer, J. L. Boyd-Graber, L. M. B. Claudino, R. Socher, and H. Daumé. A neural network for factoid question answering over paragraphs. In EMNLP, 2014.Google ScholarCross Ref
Index Terms
- Ask Your TV: Real-Time Question Answering with Recurrent Neural Networks
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