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Unsupervised Technique To Conversational Machine Reading

2021-06-29 10:59:03
Peter Ochieng, Dennis Mugambi
arXiv_AI
     
Abstract

Conversational machine reading (CMR) tools have seen a rapid progress in the recent past. The current existing tools rely on the supervised learning technique which require labeled dataset for their training. The supervised technique necessitates that for every new rule text, a manually labeled dataset must be created. This is tedious and error prone. This paper introduces and demonstrates how unsupervised learning technique can be applied in the development of CMR. Specifically, we demonstrate how unsupervised learning can be used in rule extraction and entailment modules of CMR. Compared to the current best CMR tool, our developed framework reports 3.3% improvement in micro averaged accuracy and 1.4 % improvement in macro averaged accuracy.

Abstract (translated)

URL

https://arxiv.org/abs/2106.15247

PDF

https://arxiv.org/pdf/2106.15247.pdf

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