The exemplary embodiments describe, inter alia, an apparatus comprising: a processor configured to (1) generate a plurality of graphical user interfaces (GUIs) for interaction with a user to support configuration of a narrative story generator to automatically generate a narrative story based on input data, wherein at least one of the GUIs presents content blocks comprising a story outline in a hierarchical structure, (2) evaluate configuration elements of the narrative story generated using imported sample data, and (3) generate narrative stories based on the configuration of the narrative story generator and the input data.

Provided is an apparatus for evaluating and improving responses, and a method and a computer readable recording medium thereof. The apparatus for evaluating responses according to the present disclosure obtains cluster classifying information for training responses, and based on distribution of clusters to which test responses output from the dialogue generation model are classified, evaluate semantic diversity of the responses output from the dialogue generation model.

Provided is an apparatus for evaluating and improving responses, and a method and a computer readable recording medium thereof. The apparatus for evaluating responses according to the present disclosure obtains cluster classifying information for training responses, and based on distribution of clusters to which test responses output from the dialogue generation model are classified, evaluate semantic diversity of the responses output from the dialogue generation model.

Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Aspects of the disclosure relate to enhanced dynamic training response output generation control systems with enhanced dynamic training response output determinations. A computing platform may receive, from the user device and in response to an initial dynamic training interface, a training request input. The computing platform may send, to an NLU engine, the training request input and commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine a natural language result output. Using the natural language result output, the computing platform may determine third party data sources that correspond to the natural language result output, and may request source data from the third party data sources. Using the source data and the natural language result output, the computing platform may generate a dynamic training response output, and may direct the user device to cause display of the dynamic training response output.

Techniques are described herein for enabling autonomous agents to generate conclusive answers. An example of a conclusive answer is text that addresses concerns of a user who is interacting with an autonomous agent. For example, an autonomous agent interacts with a user device, answering user utterances, for example questions or concerns. Based on the interactions, the autonomous agent determines that a conclusive answer is appropriate. The autonomous agent formulates the conclusive answer, which addresses multiple user utterances. The conclusive answer provided to the user device.

A system and method for advanced document redaction are disclosed. According to one embodiment, a system comprises a parser that analyzes documents to identify structured, semi-structured, and unstructured data from a document. A candidates generator generates a list of words for redaction from the structured, semi-structured, and unstructured data. A replacement engine replaces one or more words from the list of words with one or more of a replacement word, random characters, and random numbers.

The present disclosure proposes a language generation method and apparatus. The method includes: performing encoding processing on an input sequence by using a preset encoder to generate a hidden state vector corresponding to the input sequence; in response to a granularity category of a second target segment being a phrase, decoding a first target segment vector, the hidden state vector, and a position vector corresponding to the second target segment by using N decoders to generate N second target segments; determining a loss value based on differences between respective N second target segments and a second target annotated segment; and performing parameter updating on the preset encoder, a preset classifier, and the N decoders based on the loss value to generate an updated language generation model for performing language generation.

The present disclosure proposes a language generation method and apparatus. The method includes: performing encoding processing on an input sequence by using a preset encoder to generate a hidden state vector corresponding to the input sequence; in response to a granularity category of a second target segment being a phrase, decoding a first target segment vector, the hidden state vector, and a position vector corresponding to the second target segment by using N decoders to generate N second target segments; determining a loss value based on differences between respective N second target segments and a second target annotated segment; and performing parameter updating on the preset encoder, a preset classifier, and the N decoders based on the loss value to generate an updated language generation model for performing language generation.

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. Narrative analytics that are linked to communication goal statements can employ a conditional outcome framework that allows the content and structure of resulting narratives to intelligently adapt as a function of the nature of the data under consideration. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal.