Leveraging Term Banks for Answering Complex Questions: A Case for Sparse Vectors
This addresses the challenge of reliable complex question answering for users in domains like science, with an incremental approach using existing term banks.
The paper tackles the problem of answering complex science questions by leveraging term banks to guide QA systems, resulting in significant performance improvements over state-of-the-art methods.
While open-domain question answering (QA) systems have proven effective for answering simple questions, they struggle with more complex questions. Our goal is to answer more complex questions reliably, without incurring a significant cost in knowledge resource construction to support the QA. One readily available knowledge resource is a term bank, enumerating the key concepts in a domain. We have developed an unsupervised learning approach that leverages a term bank to guide a QA system, by representing the terminological knowledge with thousands of specialized vector spaces. In experiments with complex science questions, we show that this approach significantly outperforms several state-of-the-art QA systems, demonstrating that significant leverage can be gained from continuous vector representations of domain terminology.