Abstract: This application addresses techniques for personalizing natural language generation by conversational agents. These solutions allow for human-like, large scale opinion expression using a consistent style or personality. Training sentences may be retrieved and a vocabulary may be built based on an analysis of the training sentences. The sentences may be analyzed to determine: (1) whether they express an opinion; (2) whether the opinion is positive or negative; (3) whether the sentence fits in the context of the currently communication; and (4) whether the sentence came from a person with first-hand experience of the topic. Further classifications may be made based on characteristics such as the age or gender of the person expressing the opinion. These opinions may be entered into a repository and used for opinion expression, for example by using the statements directly in a conversation or by training a language generation model with the opinions.

Abstract: This application addresses techniques for personalizing natural language generation by conversational agents. These solutions allow for human-like, large scale opinion expression using a consistent style or personality. Training sentences may be retrieved and a vocabulary may be built based on an analysis of the training sentences. The sentences may be analyzed to determine: (1) whether they express an opinion; (2) whether the opinion is positive or negative; (3) whether the sentence fits in the context of the currently communication; and (4) whether the sentence came from a person with first-hand experience of the topic. Further classifications may be made based on characteristics such as the age or gender of the person expressing the opinion. These opinions may be entered into a repository and used for opinion expression, for example by using the statements directly in a conversation or by training a language generation model with the opinions.

Abstract: This application addresses techniques for personalizing natural language generation by conversational agents. These solutions allow for human-like, large scale opinion expression using a consistent style or personality. Opinion statements may be retrieved and categorized. These opinion statements may be entered into a database and used directly for opinion expression. A model, such as an artificial neural network, may be generated or parameterized based on these opinions. Within the model structure, a personality embedding space may be defined. The embedding space may be used when generating new sentences, or to classify newly-generated or existing sentences. Thus, the model may be used to find further examples of opinions in the same style, or to generate entirely new opinion sentences in a given style.