Microsoft Technology Licensing, LLC

Other

Imed Zitouni

Vipul Agarwal

Soumya Batra

Ahmed Hassan Awadallah

Omar Zia Khan

Lihong Li

Karthik Tangirala

Mohammad Makarechian

Rahul Kumar Jha

Jinyoung Kim

Young-Bum Kim

Ziad Al Bawab

Ranjitha Gurunath Kulkarni

Dongchan Kim

Ahmed Moustafa El Kholy

Noha Alon

Hany Mohamed SalahEldeen Mohamed Khalil

Kyle Mark Williams

Hisami Suzuki

Paul Anthony Crook

Marius Alexandru Marin

Aidan C Crook

Frederic H Behr, Jr

Jin Young Kim

Rajesh Patel

Bradley Wethington

Hany Mohamed Salah Eldeen Mohamed Khalil

An example system includes a processor to receive production log data and pre-process the production log data to generate an exploration data set. The production log data includes continuous updates of a ranking function associated with different actions for a same query issued by a same user in different impressions. The processor can perform offline estimation of online metrics for ranking functions using the exploration data set, evaluate a proposed ranking function by comparing the estimated online metrics to a set of baseline metrics of a baseline ranking function, detect that the proposed ranking function is a preferred ranking function having a higher detected metric score than the baseline ranking function based on a comparison of the estimated online metrics with the baseline metrics of the baseline ranking function, and execute an action on an information retrieval system based on the preferred ranking function.

Offline evaluation of ranking functions

A computer-implemented technique is described herein for expediting a user's interaction with a digital assistant. In one implementation, the technique involves receiving a system prompt generated by a digital assistant in response to an input command provided by a user via an input device. The technique then generates a predicted response based on linguistic content of the system prompt, together with contextual features pertaining to a circumstance in which the system prompt was issued. The predicted response corresponds to a prediction of how the user will respond to the system prompt. The technique then selects one or more dialogue actions from a plurality of dialogue actions, based on a confidence value associated with the predicted response. The technique expedites the user's interaction with the digital assistant by reducing the number of system prompts that the user is asked to respond to.

Expediting interaction with a digital assistant by predicting user responses

Systems and methods for adding a new domain to a natural language understanding system to form an updated language understanding system with multiple domain experts are provided. More specifically, the systems and methods are able to add a new domain utilizing data from one or more of the domains already present in the natural language understanding system while keeping the new domain and the already present domains separate from each other.

Domain addition systems and methods for a language understanding system

Developer and runtime environments supporting multi-modal input for computing systems are disclosed. The developer environment includes a gesture library of human body gestures (e.g., hand gestures) that a previously-trained, system-level gesture recognition machine is configured to recognize. The developer environment further includes a user interface for linking a gesture of the gesture library with a semantic descriptor that is assigned to a function of the application program. The application program is executable to implement the function responsive to receiving an indication of the gesture recognized by the gesture recognition machine within image data captured by a camera. The semantic descriptor may be additionally linked to a different input modality than the gesture, such as a natural language input.

Developer and runtime environments supporting multi-input modalities

The claimed subject matter includes techniques for offline evaluation of ranking functions. An example system includes a first module configured to receive production log data, the first module to pre-process the production log data to generate an exploration data set. The example system also includes a second module configured to perform offline estimation of online metrics for ranking functions using the exploration data set. The example system also includes a third module to evaluate a proposed ranking function by comparing the estimated online metrics to a set of baseline metrics of a baseline ranking function and detecting that the estimated online metrics of the proposed ranking function exceed, are lower than, or are within a predetermined range of the baseline metrics.

Systems and methods are disclosed for task identification and tracking using shared conversational context. In one implementation, a first communication from a first user is received within a communication session. The first communication is processed to identify a first content element within the first communication. A second communication is received within the communication session. The second communication is processed to identify a second content element within the second communication. The first content element is associated with the second content element. Based on an association between the first content element and the second content element, a task is identified. An action is initiated with respect to the task.

Task identification from electronic user communications using a shared conversational context

Described herein is a conversation engine that can be used in a system such as a personal digital assistant or search engine that combines a dynamic knowledge graph built during execution of a request and one or more static knowledge graphs holding long term knowledge. The conversation engine comprises a state tracker that holds the dynamic knowledge graph representing the current state of the conversation, a policy engine that selects entities in the dynamic knowledge graph and executes actions provided by those entities to move the state of the conversation toward completion, and a knowledge graph search engine to search the static knowledge graph(s). The conversation is completed by building the dynamic knowledge graph over multiple rounds and chaining together operations that build toward completion of the conversation. Completion of the conversation results in completion of a request by a user.

Knowledge graph for conversational semantic search

Described herein are technologies pertaining to determining which search engine results page (SERP), from a plurality of SERPs, is preferable to a user. A query is received, and multiple SERPS are retrieved based upon the query, wherein the multiple SERPs are generated independently from one another. Values of features of the query and the multiple SERPs are obtained, and a determination as to which of the SERPs from the multiple SERPs is preferable to the user is made based upon the values of the features. The SERP determined to be preferable over other SERPs in the multiple SERPs is presented to the user.

Identifying preferable results pages from numerous results pages

Automatic speech recognition systems can benefit from cues in user voice such as hyperarticulation. Traditional approaches typically attempt to define and detect an absolute state of hyperarticulation, which is very difficult, especially on short voice queries. This disclosure provides for an approach for hyperarticulation detection using pair-wise comparisons and on a real-world speech recognition system. The disclosed approach uses delta features extracted from a pair of repetitive user utterances. The improvements provided by the disclosed systems and methods include improvements in word error rate by using hyperarticulation information as a feature in a second pass N-best hypotheses rescoring setup.

Hyperarticulation detection in repetitive voice queries using pairwise comparison for improved speech recognition

Systems and methods are disclosed for personalized communications using semantic memory. In one implementation, a first communication is received from a user and processed to identify a first content element within the communication. The first content element is associated with a second content element within a content repository. A second communication that includes the first content element is received from the user. Based on an association between the first content element and the second content element within the content repository, a third communication that includes the second content element is generated and provided to the user in response to the second communication.

Growing community of inventors

Bellevue, WA, United States of America

Imed Zitouni