Abstract
Text Similarity has significant application in many real-world problems. Text Similarity Estimation using NLP techniques can be leveraged for automating a variety of tasks that are relevant in business and social context. The outcomes given by AI-powered automated systems provide guidance for humans to take decisions. However, since the AI-powered system is a “black-box”, for the human to trust its outcome and to take the right decision or action based on the outcome, there needs to be an interface between the human and the machine which can explain the reason for the outcome and that interface is what we call “Explainable AI”. In this paper, we have made a twofold attempt, first, 1) to build a state-of-the-art Text Similarity Scoring System which would match two texts based on semantic similarity and then, 2) build an Explanation Generation Methodology to generate human-interpretable explanation for the text similarity match score.
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De, T., Mukherjee, D. (2021). Explainable NLP: A Novel Methodology to Generate Human-Interpretable Explanation for Semantic Text Similarity. In: Thampi, S.M., Krishnan, S., Hegde, R.M., Ciuonzo, D., Hanne, T., Kannan R., J. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2020. Communications in Computer and Information Science, vol 1365. Springer, Singapore. https://doi.org/10.1007/978-981-16-0425-6_3
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