Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed.

Abstract: Methods, systems, and computer programs are presented for suggesting related search queries. One method includes an operation for obtaining a supervised model by training a machine-learning (ML) program with training data that includes search queries entered by users of an online service. Further, the method includes operations for initializing a generator model with the supervised model, and for improving the generator model using reinforcement learning. The reinforcement learning is being based on a reward based on naturalness, relatedness, and a user having a positive session on the online service. Further, the result of the improvement of the generator model is a roll-out model, which is utilized to generate query suggestions for a user of the online service based on a search query provided by the user.

Abstract: Computer-implemented machine learning-based techniques for assisted electronic message composition in a vertical messaging context. The vertical messaging context may be any electronic messaging context in which senders repetitively compose electronic messages to send to recipients where the messages are not identical but nonetheless have common tone, sentiment, content, and structure. The techniques assist users that compose electronic messages in a particular vertical messaging context in composing those messages quickly, with few or no grammatical errors, and with a likelihood of being positively received by the recipients of the messages.

Abstract: The disclosed technologies include a navigation agent for a search interface. In an embodiment, the navigation agent uses reinforcement learning to dynamically generate and select navigation options for presentation to a user during a search session. The navigation agent selects navigation options based on reward scores, which are computed using implicit and/or explicit user feedback received in response to presentations of navigation options.

Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed.

Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed.