SAST: A self-attention based method for skill translation in T-shaped expert finding

Abstract

Nowadays, organizations are seeking professionals who can both excel in their areas of expertise and collaborate effectively across different disciplines. This demand has given rise to the concept of T-shaped experts who possess a deep understanding of one topic domain and a broad knowledge of several others. This combination allows these professionals to be more creative, flexible, and adaptable in problem-solving by leveraging their diverse perspectives and experiences. To find T-shaped experts in any skill area, we need to measure how deep and wide their knowledge is. In this paper, we present a novel translation-based method to estimate each user's depth of knowledge in a given skill area. The proposed method leverages a self-attention-based multi-label classification network to identify the most relevant translations for each skill that belongs to the given skill area. We utilize two new methods based on binary cross-entropy and focal loss to determine whether a user's expertise shape matches the T-shaped. We evaluate the proposed method using the standard benchmark datasets. The experimental results on three collections of the StackOverflow dataset demonstrate the superiority of the proposed methods in comparison with existing baselines.