Microsoft Technology Licensing, LLC

Sujay Kumar Jauhar

Michael Gamon

Mark James Encarnacion

Elnaz Nouri

Nirupama Chandrasekaran

Bahareh Sarrafzadeh

Oriana Riva

Patrick Pantel

Scott Joseph Counts

Reid Andersen

Chirag Deepakkumar Shah

Curtis Dean Anderson

Timothy C Franklin

Longqi Yang

Mengting Wan

Casey Jo Gossard

Naoki Otani

Mei Yang

Sathish Kumar Manivannan

Liye Fu

Sri Raghu Malireddi

Siddharth Suri

Xuchao Zhang

Jennifer Lynay Neville

Georg Ludwig Wilhelm Buscher

Tara Lynn Safavi

Xiaochuan Ni

Maria Leonor Pacheco Gonzalez

Systems and methods are described that are generally directed to generating a general task embedding representing task information. In examples, the generated task embedding may include predicted task information such that, rather being underspecified, the task embedding representative of the task may include additional specified information, where the task embedding can then be utilized in many different models and applications. Thus, task data may be received and at least a portion of the task data may be encoded using an encoder. Based on one or more outputs generated by the encoder and a type embedding associated with the task data, a task intent may be extracted or otherwise predicted based on the task data and one or more type encodings associated with the task data. The intent extractor may be trained on multiple auxiliary tasks with weak supervision that provide semantic augmentation to under-specified task texts.

Intent-based task representation learning using weak supervision

Various embodiments discussed herein relate to prompting a model, such as a Large Language Model (LLM), to ingest natural language clustering instructions and generate corresponding natural language clustering information, such as a cluster description and/or a cluster label without the need to generate any numeric text embeddings.

Dataset clustering via language model prompts

Generally discussed herein are devices, systems, and methods for determining a relationship between an edit and a comment. A system can include a memory to store parameters defining a machine learning (ML) model, the ML model to determine a relationship between an edit, by an author or reviewer, of content of a document and a comment, by a same or different author or reviewer, regarding the content of the document, and processing circuitry to provide the comment and the edit as input to the ML model, and receive, from the ML model, data indicating a relationship between the comment and the edit, the relationship including whether the edit addresses the comment or a location of the content that is a target of the comment.

Architectures for modeling comment and edit relations

Aspects of the present disclosure relate to task template generation and social task discovery. In examples, a task template catalog comprises task templates, which may be automatically generated and/or user-submitted, among other examples. Task templates can be reviewed, shared, and curated within the task template catalog. A user may browse the task catalog or search the task catalog for task templates. Once the user selects a task template, a task is generated based on the task template and added to the user's task list. In some examples, aspects of a task template may be customized. For example, a task may comprise parametric or conditional subtasks, thereby enabling a user to further tailor the task template to his or her needs. Thus, the task catalog provides a starting point from which the user can author a task in a task management application.

Task templates and social task discovery

Systems, storage media and methods for providing information for user prioritization of tasks associated with collaboratively developed content are described. Some examples may include: receiving a conversation thread associated with collaboratively developed content, the conversation thread including a plurality of comments authored by multiple different authors, generating a predicted measure of completion for the received conversation thread, the predicted measure of completion being at least one of a predicted number of remaining actions until the received conversation thread is resolved or a predicted number of total actions for the conversation thread to be resolved and providing, for display at a user interface, the predicted measure of completion for the received conversation thread, the predicted measure of completion being associated with the conversation thread at the user interface.

Collaborative communication triage assistance

A system and method and for identifying a template for a document includes receiving a request to identify the template from among a plurality of available templates, the plurality of templates being templates that are available for use in an application. After receiving the request, the content and structure of the document are encoded into one or more embedding representations via a trained document encoder and the embedding representations are compared to a plurality of template representations, each of the plurality of template representations being a representation of content and structure of one of the plurality of templates to identify one of the plurality of the templates as corresponding to the document. The identified template is then provided for display as a recommended template.

Method and system of discovering templates for documents

A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.

Minimization of computational demands in model agnostic cross-lingual transfer with neural task representations as weak supervision

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Kirkland, WA, United States of America

Sujay Kumar Jauhar