Abstract: Examples of the present disclosure describe systems and methods of configuring generic language understanding models. In aspects, one or more previously configured schemas for various applications may be identified and collected. A generic schema may be generated using the collected schemas. The collected schemas may be programmatically mapped to the generic schema. The generic schema may be used to train on ore more models. An interface may be provided to allow browsing the models. The interface may include a configuration mechanism that provides for selecting on or more of the models. The selected models may be bundled programmatically, such that the information and instructions needed to implement the models are configured programmatically. The bundled models may then be provided to a requestor.

Abstract: A primary virtual assistant and local virtual assistant herein can provide secondary information, including, without limitation, user-specific information, without having direct access to such information. For example, a user may invoke a secondary virtual assistant, through the local virtual assistant connected to a primary virtual assistant system. This invocation may be sent through the primary virtual assistant to the third party provider, i.e., the secondary virtual assistant. The secondary virtual assistant has access to the secondary information, for example, email, calendars, other types of information specifically associated with the user or learned from past user actions while this information is not directly available to the primary virtual assistant. This secondary information is then provided to the local virtual assistant in response to the invocation to be provided to the user.

Abstract: A primary virtual assistant and local virtual assistant herein can provide secondary information, including, without limitation, user-specific information, without having direct access to such information. For example, a user may invoke a secondary virtual assistant, through the local virtual assistant connected to a primary virtual assistant system. This invocation may be sent through the primary virtual assistant to the third party provider, i.e., the secondary virtual assistant. The secondary virtual assistant has access to the secondary information, for example, email, calendars, other types of information specifically associated with the user or learned from past user actions while this information is not directly available to the primary virtual assistant. This secondary information is then provided to the local virtual assistant in response to the invocation to be provided to the user.

Abstract: 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.

Abstract: Methods and systems are provided for collaborative completion of tasks using task frames. Upon receiving a request to perform a task, a system utilizes task frames in completing the requested task. A task frame is a data structure that contains the parameters and status signals that represent a particular task and captures the combined system's understanding of a current state of the task. Input is received at a client device and sent to a server, where the input is processed. Based on the processed input, a task frame is retrieved and filled. The filled task frame is sent to the client device, where the client device performs actions based on the task frame and updates the task frame parameters and the state of the task. The updated task frame is returned to the server. The shared task frame provides improvements to the overall task completion process.

Abstract: 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.

Abstract: Examples of the present disclosure describe systems and methods of configuring generic language understanding models. In aspects, one or more previously configured schemas for various applications may be identified and collected. A generic schema may be generated using the collected schemas. The collected schemas may be programmatically mapped to the generic schema. The generic schema may be used to train on ore more models. An interface may be provided to allow browsing the models. The interface may include a configuration mechanism that provides for selecting on or more of the models. The selected models may be bundled programmatically, such that the information and instructions needed to implement the models are configured programmatically. The bundled models may then be provided to a requestor.

Abstract: Methods and systems are provided for collaborative completion of tasks using task frames. Upon receiving a request to perform a task, a system utilizes task frames in completing the requested task. A task frame is a data structure that contains the parameters and status signals that represent a particular task and captures the combined system's understanding of a current state of the task. Input is received at a client device and sent to a server, where the input is processed. Based on the processed input, a task frame is retrieved and filled. The filled task frame is sent to the client device, where the client device performs actions based on the task frame and updates the task frame parameters and the state of the task. The updated task frame is returned to the server. The shared task frame provides improvements to the overall task completion process.

Abstract: The universal text input technique described herein addresses the difficulties of typing text in various languages and scripts, and offers a unified solution, which combines character conversion, next word prediction, spelling correction and automatic script switching to make it extremely simple to type any language from any device. The technique provides a rich and seamless input experience in any language through a universal IME (input method editor). It allows a user to type in any script for any language using a regular qwerty keyboard via phonetic input and at the same time allows for auto-completion and spelling correction of words and phrases while typing. The technique also provides a modeless input that automatically turns on and off an input mode that changes between different types of script.

Abstract: The universal text input technique described herein addresses the difficulties of typing text in various languages and scripts, and offers a unified solution, which combines character conversion, next word prediction, spelling correction and automatic script switching to make it extremely simple to type any language from any device. The technique provides a rich and seamless input experience in any language through a universal IME (input method editor). It allows a user to type in any script for any language using a regular qwerty keyboard via phonetic input and at the same time allows for auto-completion and spelling correction of words and phrases while typing. The technique also provides a modeless input that automatically turns on and off an input mode that changes between different types of script.

Abstract: Grammatical element prediction is used in the context of machine translation. Features from both the source language and the target language sentences (or other text fragments) are used in predicting the grammatical elements.

Abstract: Methods are disclosed for acquiring ontological knowledge using query logs. In one embodiment, query logs are first utilized as a basis for identifying important contexts associated with terms belonging to a semantic category. Then, those contexts are as a basis for identifying new terms belonging to the same category or, in another embodiment, as a basis for removing extraneous or obsolete terms identified as being in the same category.

Abstract: Described is a transliteration engine/substring decoder that back-transliterates an input string from a source language into an output string in a target language. The transliteration engine may be based upon discriminately weighted indicator features and/or generative models in which the decoder's discriminative parameters are learned. The training data may be based on source-target pairs, which may be transformed into derivations. Features extracted from these derivations include indicator features and hybrid generative model features.

Abstract: Grammatical element prediction is used to predict grammatical elements in text fragments (such as phrases or sentences). In one embodiment, a statistical model, using syntax features, is used to predict grammatical elements.

Abstract: Document summarization is performed by scoring individual words in sentences in a document or document cluster. Sentences from the document or document cluster are selected to form a summary based on the scores of the words contained in those sentences.

Abstract: A method of training language model parameters trains discriminative model parameters in the language model based on a performance measure having discrete values.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: Methods are disclosed for acquiring ontological knowledge using query logs. In one embodiment, query logs are first utilized as a basis for identifying important contexts associated with terms belonging to a semantic category. Then, those contexts are as a basis for identifying new terms belonging to the same category or, in another embodiment, as a basis for removing extraneous or obsolete terms identified as being in the same category.

Abstract: A new system is hereby provided that generates automatic summaries of groups of multiple documents using multiple variations of each sentence from a selected group of representative sentences from the documents, and then selecting from the multiple variations when assembling the automatic summary. The system may generate alternative strings of text, select from among the alternative strings of text, and provide a summary of the group of documents using the strings of text selected from among the alternatives. The alternative strings of text may be generated based on each of a plurality of sentences from the group of documents. Selecting from among the alternative strings of text may be based on one or more criteria indicating the strings of text to be representative of the content of the group of documents.

Abstract: Document summarization is performed by scoring individual words in sentences in a document or document cluster. Sentences from the document or document cluster are selected to form a summary based on the scores of the words contained in those sentences.