Abstract: A computer-implemented method of tagging a text, comprises: determining a value for each of a plurality of locations in a first vector; processing (402), by a trained first neural network component, the first vector to generate a second vector; processing (404), at a trained second neural network component, the second vector to generate a probability score for each of at least ten predetermined tags; determining (406) if each probability score meets a criterion; if the criterion is met, assigning (408) the tag corresponding to the probability score to the text. Each of the locations may correspond to a respective predetermined word, each value relating to existence and/or frequency of the corresponding word in the text, and the number of locations may be between 600 and 20000. The number of locations in the second vector may be fewer than the number of locations in the first vector and is from 100 to 5000.

Abstract: Certain examples described herein provide methods and systems for implementing a conversational agent, e.g. to train a predictive model used by the conversational agent. In examples, text data representing agent messages from a dialogue database are clustered and the clusters are used to generate response templates for use by the conversational agent. The predictive model is trained on training data generated by selectively assigning response templates to agent messages from text dialogues. Examples enable a predictive model to be trained on high quality data sets that are generated automatically from a corpus of historical data. In turn, they enable a natural language interface to be efficiently provided.

Abstract: Certain examples are described that provide methods and systems for generating templates for use by a conversational agent. These examples enable a natural language interface to be provided. Certain examples cluster user and agent messages from a corpus of text data representing text dialogues. This clustering enables response templates to be generated in a way that takes into account a context in which responses are given. In certain examples, messages that are exchanged between a user and a conversational agent are embedded as numeric arrays based a neural sequence-to-sequence model. Clustering routines are used to group dialogue encodings into one or more response clusters, and these clusters may then be used to generate response templates. The response templates may be used by a conversational agent to prepare a response to a user message.

Abstract: A method comprises: receiving input of a dialogue; processing the dialogue by a neural network based system, to output, for each of a plurality of slots, a probability distribution over a range of values associated with the respective slot, the neural network based system being trained using a training dataset comprising a plurality of dialogues and, for each dialogue, a value corresponding to each slot, wherein each dialogue resulted in an action; determining, based at least on the probability distribution for each slot, if an action requiring one of values for at least some of the slots can be performed; if not, causing continuing of the dialogue.

Abstract: A computer-implemented method of tagging a text, comprises: determining a value for each of a plurality of locations in a first vector; processing (402), by a trained first neural network component, the first vector to generate a second vector; processing (404), at a trained second neural network component, the second vector to generate a probability score for each of at least ten predetermined tags; determining (406) if each probability score meets a criterion; if the criterion is met, assigning (408) the tag corresponding to the probability score to the text. Each of the locations may correspond to a respective predetermined word, each value relating to existence and/or frequency of the corresponding word in the text, and the number of locations may be between 600 and 20000.