Improving the Quality of the Conversational Datasets through Extensive Semantic Analysis

As more and more datasets become available their utilization in different applications increases in popularity. Their volume and production rate, however, means that their quality and content control is in most cases non-existing, resulting in many datasets that contain inaccurate information of low quality. Especially in the field of conversational assistants, where the datasets come from many heterogeneous sources with no quality assurance, the problem is aggravated. We present here an integrated platform that creates task-and topic-specific conversational datasets to be used for training conversational agents. The platform explores available conversational datasets, extracts information based on semantic similarity and relatedness, and applies a weight-based score function to rank the information based on its value for the specific task and topic. The finalized dataset can then be used for the training of an automated conversational assistance over accurate data of high quality.