On Automatic Question Answering Using Efficient Primal-Dual Models

Automatic question answering has been a major problem in natural language processing since the early days of research in the field. Given a large dataset of question-answer pairs, the problem can be tackled using text matching in two steps: find a set of similar questions to a given query from the dataset and then provide an answer to the query by evaluating the answers stored in the dataset for those questions. In this paper, we treat the text matching problem as an instance of the inexact graph matching problem and propose an efficient approximate matching scheme. We utilize the well known quadratic optimization problem metric labeling as the framework of graph matching. In order to solve the text matching, we first embed the sentences given in natural language into a weighted directed graph. Next, we present a primal-dual approximation algorithm for the linear programming relaxation of the metric labeling problem to match text graphs. We demonstrate the utility of our approach on a question answering task over a large dataset which involves matching of questions as well as plain text.