A fusion scheme for eliminating input interference induced by spelling errors

Abstract

Spelling errors are a prevalent form of interference that may result from human handwriting or natural language processing systems, such as optical character recognition. This type of interference presents a challenge for natural language processing systems that rely on context-based embeddings to comprehend the exact semantics of a sentence. The proposed fusion scheme is the main focus of this paper, which aims to correct spelling errors in sentences. The scheme employs both the original input and the masked input. The masked input is acquired by utilizing a detection module to mask any spelling errors present in the original input. By utilizing the masked input, the scheme is capable of learning the real sentence semantic information that is not affected by spelling errors. Utilizing the original input can prevent the loss of input information resulting from masking operations. Finally, the utilization of the Kullback-Leibler divergence is employed to establish concordance between the two output distributions within the framework, consequently facilitating the acquisition of comprehensive input information, all while mitigating the influence of spelling errors. Experiments were conducted on two widely used benchmarks. Compared with previous methods, the approach presented herein exhibited superior performance as it achieved improvements on two benchmarks.