Bot can be made where one doesn&#x27;t have to parse commands.<p>The author of the article could have collected all the conversations and learned a bot that would correctly converse with people asking similar questions.<p>there&#x27;s no reason that having the same question asked in different ways should be a problem.<p>language is structured. structured learning and prediction exists for more than two decades and just recently there have been very nice improvements to known methods (learning to search, neural networks for structured learning etc.).<p>one can try to summarize an answer to a question from relevant fetched documents. summarization is a structured prediction task.<p>for example, in the conversations with a bot, you store all of the questions and your answers.<p>your answers were formed by using documents that contain the needed information. now you&#x27;re trying to find a mapping that will successfully fetch the relevant documents for the question, and then summarize all of the documents to as close as possible summarization (summarized text should be similar to your stored answer).<p>structured prediction techniques use simple methods such as pos tagging and then pruning the dependency parse tree of sentences in document to shorten it, excluding whole sentences or text-between-commas or unneeded-adjectives etc. (these methods are based on statistical machine learning, not some silly rule based technique, one can incorporate word2vec features or other neural network magic)<p>it&#x27;s not impossible, given enough data, to build a bot that would interact successfully.<p>sarcasm, and emotions are still a bit away, mostly because they require knowledge about the world, and if your world is a small set of documents you won&#x27;t successfully get the sarcasm or emotions. this is also the case with people when they come to a different culture.