The actual chatbot training uses a combination of techniques and algorithms that Trendskout’s AI carries out independently, requiring minimal human intervention. Read on to discover a few of the underlying techniques and principles.
In addition to specific modules for manufacturers, the Trendskout AutoML platform also offers many possibilities for IT teams to quickly set up AI applications, Powered by our unique double AI low and easy to use UI that fully automates data transformations, algorithm selection and hypertuning.
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Chatbot is one of the available Trendskout AI Flow analysis-functions.
In order to read and understand the text entered in the chatbot, it must be processed. For this, this text is split into various parts, an internal tree structure is generated, and this is linked to other texts and question / answer combinations to discover semantic connections. Relational contexts are discovered based on this technique and the algorithm can provide meaningful answers. There are two common ways in which Trendskout can prepare this answer, Natural Language Generation (NLG) and Classification.
NLG techniques can independently, based on training data, generate texts that resemble answers that people would draft. This is a very advanced technique based on the latest developments in Deep Learning and hybrid neural networks. NLG algorithms are very powerful but require very large amounts of training data. If the training data is not large or qualitative enough, the generated texts will be of little significance. For most organizations it is therefore recommended to work with a different, ready-to-use approach for chatbots: text and answer classification.
Just like with other Deep Learning functions, Trendskout automates the entire training process for chatbots including algorithm selection and hypertuning.
