Abstract: Described herein are systems and methods for providing hierarchical state tracking in a spoken dialogue system. A sequence of turns is received by a spoken dialogue system. Each turn includes a user utterance and a machine act. At each turn, a value pointer and a turn pointer are provided for that turn. The value pointer represents a probability distribution over the one or more words in the user utterance that indicates whether each word in the user utterance is a slot value for a slot. The turn pointer identifies which turn in a set of turns includes a currently-relevant slot value for the slot, where the set of turns includes a current turn for which the turn point is being provided, and all turns that precede the current turn.

Abstract: A electronic sensor signals monitoring unit (10), system and computer program, for sepsis monitoring, includes an interactive visualization status calculation monitor (M), a sensor data acquisition interface (11), acquiring time-critical status-relevant sensor signals from medical devices (G), a rule engine interface (12) to a memory or a data bank (DB) with stored rules for analyzing and processing status-relevant parameters and/or sensor signals, and a computer-assisted control unit (S), configured to dynamically control the interactive visualization on the monitor (M) and including an arithmetic unit (RW). The arithmetic unit (RW) is supplied with the acquired sensor signals via the sensor data acquisition interface (11) and is intended for the status calculation with the rules stored in the data bank (DB). The status calculation includes an overview display, a detail view and a logbook view. A display of the course of status-relevant sensor signals over time is visualized in the detail view.

Abstract: Examples of the present disclosure describe systems and methods relating to conversation state management using frame tracking. In an example, a frame may represent one or more constraints (e.g., parameters, variables, or other information) received from or generated as a result of interactions with a user. Consequently, each frame may represent one or more states of an ongoing conversation. When the user provides new or different information, a new frame may be created to represent the now-current state of the conversation. The previous frame may be retained for later access by what is referred to herein as a “dialog agent,” which is the portion of the system that can search and use previous state-related information. When an utterance is received, a frame to which the utterance relates may be identified. Thus, the dialog agent may track multiple states simultaneously, thereby enabling conversation features that were not previously possible.

Abstract: Described herein are systems and methods for two-stage training of a spoken dialog system. The first stage trains a policy network using external data to produce a semi-trained policy network. The external data includes one or more known fixed dialogs. The second stage trains the semi-trained policy network through interaction to produce a trained policy network. The interaction may be interaction with a user simulator.

Abstract: Described herein are systems and methods for providing a natural language generator in a spoken dialog system that considers both lexicalized and delexicalized dialog act slot-value pairs when translating one or more dialog act slot-value pairs into a natural language output. Each slot and value associated with the slot in a dialog act are represented as (dialog act+slot, value), where the first term (dialog act+slot) is delexicalized and the second term (value) is lexicalized. Each dialog act slot-value representation is processed to produce at least one delexicalized sentence as an output. A lexicalized sentence is produced by replacing each delexicalized slot with the value associated with the delexicalized slot.

Abstract: Examples of the present disclosure describe systems and methods relating to conversation state management using frame tracking. In an example, a frame may represent one or more constraints (e.g., parameters, variables, or other information) received from or generated as a result of interactions with a user. Consequently, each frame may represent one or more states of an ongoing conversation. When the user provides new or different information, a new frame may be created to represent the now-current state of the conversation. The previous frame may be retained for later access by what is referred to herein as a “dialogue agent,” which is the portion of the system that can search and use previous state-related information. When an utterance is received, a frame to which the utterance relates may be identified. Thus, the dialogue agent may track multiple states simultaneously, thereby enabling conversation features that were not previously possible.

Abstract: Described herein are systems and methods for providing a natural language generator in a spoken dialogue system that considers both lexicalized and delexicalized dialogue act slot-value pairs when translating one or more dialogue act slot-value pairs into a natural language output. Each slot and value associated with the slot in a dialogue act are represented as (dialogue act+slot, value), where the first term (dialogue act+slot) is delexicalized and the second term (value) is lexicalized. Each dialogue act slot-value representation is processed to produce to produce at least one delexicalized sentence as an output. A lexicalized sentence is produced by replacing each delexicalized slot with the value associated with the delexicalized slot.

Abstract: Described herein are systems and methods for two-stage training of a spoken dialogue system. The first stage trains a policy network using external data to produce a semi-trained policy network. The external data includes one or more known fixed dialogues. The second stage trains the semi-trained policy network through interaction to produce a trained policy network. The interaction may be interaction with a user simulator.

Abstract: Described herein are systems and methods for providing hierarchical state tracking in a spoken dialogue system. A sequence of turns is received by a spoken dialogue system. Each turn includes a user utterance and a machine act. At each turn, a value pointer and a turn pointer are provided for that turn. The value pointer represents a probability distribution over the one or more words in the user utterance that indicates whether each word in the user utterance is a slot value for a slot. The turn pointer identifies which turn in a set of turns includes a currently-relevant slot value for the slot, where the set of turns includes a current turn for which the turn point is being provided, and all turns that precede the current turn.

Abstract: A electronic sensor signals monitoring unit (10), system and computer program, for sepsis monitoring, includes an interactive visualization status calculation monitor (M), a sensor data acquisition interface (11), acquiring time-critical status-relevant sensor signals from medical devices (G), a rule engine interface (12) to a memory or a data bank (DB) with stored rules for analyzing and processing status-relevant parameters and/or sensor signals, and a computer-assisted control unit (S), configured to dynamically control the interactive visualization on the monitor (M) and including an arithmetic unit (RW). The arithmetic unit (RW) is supplied with the acquired sensor signals via the sensor data acquisition interface (11) and is intended for the status calculation with the rules stored in the data bank (DB). The status calculation includes an overview display, a detail view and a logbook view. A display of the course of status-relevant sensor signals over time is visualized in the detail view.