Effects of answer weight boosting in strategy-driven question answering

With the advances in natural language processing (NLP) techniques and the need to deliver more fine-grained information or answers than a set of documents, various QA techniques have been developed corresponding to different question and answer types. A comprehensive QA system must be able to incorporate individual QA techniques as they are developed and integrate their functionality to maximize the system’s overall capability in handling increasingly diverse types of questions. To this end, a new QA method was developed to learn strategies for determining module invocation sequences and boosting answer weights for different types of questions. In this article, we examine the roles and effects of the answer verification and weight boosting method, which is the main core of the automatically generated strategy-driven QA framework, in comparison with a strategy-less, straightforward answer-merging approach and a strategy-driven but with manually constructed strategies.

Research highlights
► Investigations of the effects and the roles of the strategy-driven QA method.
► Effects of the strategies on different question types.
► Effects of the strategies on reducing the use of computing resources.
► Effects of adding new QA modules incrementally in the proposed QA framework.
► Resilience of the framework in terms of consistency in improving effectiveness.