Abstract
We describe and analyze inference strategies for combining outputs from multiple question answering systems each of which was developed independently. Specifically, we address the DARPA-funded GALE information distillation Year 3 task of finding answers to the 5-Wh questions (who, what, when, where, and why) for each given sentence. The approach we take revolves around determining the best system using discrimina- tive learning. In particular, we train support vector machines with a set of novel features that encode systems’ capabilities of returning as many correct answers as possible. We analyze two combination strategies: one combines multiple systems at the granularity of sentences, and the other at the granularity of individual fields. Our experimental results indicate that the pro- posed features and combination strategies were able to improve the overall performance by 22% to 36% relative to a random selection, 16% to 35% relative to a majority voting scheme, and 15% to 23% relative to the best individual system.
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