Knowledge Injection with Perturbation-based Constrained Attention Network for Word Sense Disambiguation

Abstract

sparse dot product operation. Their results by using a benchmark dataset were beyond 80%. Supervised Word Sense Disambiguation (WSD) has been studied intensively for over three decades. However, disentangling diverse contexts is still a challenging problem. This paper addresses the problem and proposes a Perturbation-based constrained attention network (Pconan) for injecting lexical knowledge derived from the WordNet. The Pconan allows modeling beneficial dependencies between the segments/words within the input sequence with the mask-attention technique. We incorporate a perturbation method into our model to mitigate the overfitting problem resulting from intensive learning. The experimental results by using a benchmark dataset show that our method is comparable to the SOTA WSD methods. Our source codes are available online1 .