Extraction and Systematic Synthesis of Textual Information by Contextualization and Enrichment : Applications of Automated Reasoning in the Cognitive Sciences
Abstract
One of the major problems in AI and Deep Learning is Commonsense Reasoning. As easy as it is for humans to perform certain tasks without having to think and waste much time, machines have difficulties in performing those tasks without necessarily been programmed.The Winograd schema is one of the recommended ways for testing the Commonsense reasoning ability of machines. It is difficult for machines to answer this Winograd Schema. In this work, we propose the use of neural network based on Language Models in tackling this problem. Our network takes in only word inputs for the training on large vocabulary size. This model attains an accuracy of
54.58 percent when ran on the Winograd Schema Challenge.
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