The commands and questions are split into training and validation sets. Human beings are asked to paraphrase commands and questions for task identification for further execution of algorithms as skills. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing (NLP).

вЂњRobot, can we have a conversation?вЂќ) and allows for better accessibility to AI by non-technical users.ДЄbstract = 'In this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal commands. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. In this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal commands.

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