In today’s knowledge-based economy, having proper expertise is crucial in resolving many tasks. Expertise Finding (EF) is the area of research concerned with matching available experts to given tasks. A standard approach is to input a task description/proposal/paper into an EF system and receive recommended experts as output. Mostly, EF systems operate either via a content-based approach, which uses the text of the input as well as the text of the available experts’ profiles to determine a match, and structure-based approaches, which use the inherent relationship between experts, affiliations, papers, etc. The underlying data representation is fundamentally different, which makes the methods mutually incompatible. However, previous work (Watanabe et al., 2005a) achieved good results by converting content-based data to a structure-representation and using a structure-based approach. The authors posit that the reverse may also hold merit, namely, a content-based approach leveraging structure-based data converted to a content-based representation. This paper compares the authors’ idea to a content only-based approach, demonstrating that their method yields substantially better performance, and thereby substantiating their claim.
Learning to Generate Task-Specific Adapters from Task Description
