Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that implement a peer-to-peer social media recommendation system. For example, the peer-to-peer recommendation process can involve providing social media accounts to a recommender to recommend to a recommendee. For example, based on determining triggering criteria, the disclosed systems can determine social media accounts for a recommender to recommend to a recommendee based on recommender features and recommendee features. In addition, the disclosed systems can provide a recommender user interface for display on a recommender device that includes a listing of social media accounts together with selectable options to recommend the social media accounts to a recommendee. The disclosed systems can further provide a recommendee user interface to a recommendee device to display social media accounts recommended by the recommender together with selectable options to follow the social media accounts.

Abstract: Embodiments of an interface system that enables a call center agent to access and intervene in an interaction between an automated call center system and a caller whenever necessary for complex application tasks is described. The system includes a user interface that presents the agent with one or more categories of information, including the conversation flow, obtained semantic information, the recognized utterances, and access to the utterance waveforms. This information is cross-linked and attached with a confidence level for better access and navigation within the dialog system for the generation of appropriate responses to the caller.

Abstract: Embodiments of a dialog system that utilizes grammar-based labeling scheme to generate labeled sentences for use in training statistical models. During the process of training data development, a grammar is constructed manually based on the application domain or adapted from a general grammar rule. An annotation schema is created accordingly based on the application requirements, such as syntactic and semantic information. Such information is then included in the grammar specification. After the labeled grammar is constructed, a generation algorithm is then used to generate sentences for training various statistical models.

Abstract: Embodiments of a dialog system that utilizes grammar-based labeling scheme to generate labeled sentences for use in training statistical models. During the process of training data development, a grammar is constructed manually based on the application domain or adapted from a general grammar rule. An annotation schema is created accordingly based on the application requirements, such as syntactic and semantic information. Such information is then included in the grammar specification. After the labeled grammar is constructed, a generation algorithm is then used to generate sentences for training various statistical models.

Abstract: Embodiments of an interface system that enables a call center agent to access and intervene in an interaction between an automated call center system and a caller whenever necessary for complex application tasks is described. The system includes a user interface that presents the agent with one or more categories of information, including the conversation flow, obtained semantic information, the recognized utterances, and access to the utterance waveforms. This information is cross-linked and attached with a confidence level for better access and navigation within the dialog system for the generation of appropriate responses to the caller.