Publication at Faculty of Mathematics and Physics |
2016
Abstract
We introduce a highly scalable approach for open-domain question answering with no dependence on any data set for surface form to logical form mapping or any linguistic analytic tool such as POS tagger or named entity recognizer. We define our approach under the Constrained Conditional Models framework which lets us scale up to a full knowledge graph with no limitation on the size.
On a standard benchmark, we obtained near 4 percent improvement over the state-of-the-art in open-domain question answering task.