Answer Selection in a Multi-stream Open Domain Question Answering System

Question answering systems aim to meet users’ information needs by returning exact answers in response to a question. Traditional open domain question answering systems are built around a single pipeline architecture. In an attempt to exploit multiple resources as well as multiple answering strategies, systems based on a multi-stream architecture have recently been introduced. Such systems face the challenging problem of having to select a single answer from pools of answers obtained using essentially different techniques. We report on experiments aimed at understanding and evaluating the effect of different options for answer selection in a multi-stream question answering system. We examine the impact of local tiling techniques, assignments of weights to streams based on past performance and/or question type, as well redundancy-based ideas. Our main finding is that redundancy-based ideas in combination with naively learned stream weights conditioned on question type work best, and improve significantly over a number of baselines.