Abstract: A computing system for identifying tasks at risk in a collaborative project includes one or more processors configured to execute, during an inference-time phase, a collaborative project management program and a machine learning model. The collaborative project management program is configured to receive telemetry data associated with a task, process the telemetry data based at least in part on one or more task attributes, and output at least one feature associated with the task. The machine learning model is configured to receive, as inference-time input, the at least one feature associated with the task, and, responsive to receiving the at least one feature, output a risk prediction for the task. The system is configured to output an alert when the task is predicted to be at risk of not being completed by a predetermined due date.

Abstract: Aspects of the present disclosure relate to obtaining task and/or list information from various types of media files. In examples, an image of an environment may be obtained, where the image may include a depiction of a plurality of tasks. The tasks may be extracted from the image and assigned to one or more users based contextual information within the image. In some examples, tasks within an image may be identified based on positional information of the text and/or character delimiters. In some examples, audio information may be received and processed such that the audio information is converted to text. The text may then be parsed to extract one or more items of a list and/or one or more tasks.

Abstract: 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.

Abstract: A computing system for identifying tasks at risk in a collaborative project includes one or more processors configured to execute, during an inference-time phase, a collaborative project management program and a machine learning model. The collaborative project management program is configured to receive telemetry data associated with a task, process the telemetry data based at least in part on one or more task attributes, and output at least one feature associated with the task. The machine learning model is configured to receive, as inference-time input, the at least one feature associated with the task, and, responsive to receiving the at least one feature, output a risk prediction for the task. The system is configured to output an alert when the task is predicted to be at risk of not being completed by a predetermined due date.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for facilitating an enterprise user to obtain an answer to a user question within an enterprise based on an enterprise knowledge graph are provided. In particular, an enterprise server may receive the user question from the enterprise user, determine a suggested topic associated with the user question based on the enterprise knowledge graph by transforming the user question into a semantic representation to identify a plurality of similar entities within the enterprise knowledge graph, and determine whether a relevant question-and-answer (Q&A) pair linked to the suggested topic exists based on the enterprise knowledge graph. In response to a determination that the relevant Q&A pair does not exist, the enterprise server may determine a predicted answer to the user question and update the enterprise knowledge graph.

Abstract: 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.

Abstract: 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.

Abstract: Automatically detected and identified tasks and calendar items from electronic communications may be populated into one or more tasks applications and calendaring applications. Text content retrieved from one or more electronic communications may be extracted and parsed for determining whether keywords or terms contained in the parsed text may lead to a classification of the text content or part of the text content as a task. Identified tasks may be automatically populated into a tasks application. Similarly, text content from such sources may be parsed for keywords and terms that may be identified as indicating calendar items, for example, meeting requests. Identified calendar items may be automatically populated into a calendar application as a calendar entry.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.

Abstract: Automatically detected and identified tasks and calendar items from electronic communications may be populated into one or more tasks applications and calendaring applications. Text content retrieved from one or more electronic communications may be extracted and parsed for determining whether keywords or terms contained in the parsed text may lead to a classification of the text content or part of the text content as a task. Identified tasks may be automatically populated into a tasks application. Similarly, text content from such sources may be parsed for keywords and terms that may be identified as indicating calendar items, for example, meeting requests. Identified calendar items may be automatically populated into a calendar application as a calendar entry.

Abstract: A method includes receiving an email addressed to a recipient user, processing the received email using a reparametrized recurrent neural network model to identify an action based on the received email, and wherein the reparametrized recurrent neural network model has been trained on an email dataset annotated with recipient corresponding actions and reparametrized on unannotated conversation data having structures similar to email data.

Abstract: Automatically detected and identified tasks and calendar items from electronic communications may be populated into one or more tasks applications and calendaring applications. Text content retrieved from one or more electronic communications may be extracted and parsed for determining whether keywords or terms contained in the parsed text may lead to a classification of the text content or part of the text content as a task. Identified tasks may be automatically populated into a tasks application. Similarly, text content from such sources may be parsed for keywords and terms that may be identified as indicating calendar items, for example, meeting requests. Identified calendar items may be automatically populated into a calendar application as a calendar entry.

Abstract: Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.

Abstract: Aspects of the present disclosure relate to obtaining task and/or list information from various types of media files. In examples, an image of an environment may be obtained, where the image may include a depiction of a plurality of tasks. The tasks may be extracted from the image and assigned to one or more users based contextual information within the image. In some examples, tasks within an image may be identified based on positional information of the text and/or character delimiters. In some examples, audio information may be received and processed such that the audio information is converted to text. The text may then be parsed to extract one or more items of a list and/or one or more tasks.

Abstract: Aspects of the present disclosure relate to task modification and optimization. In examples, a user provides an indication of a task goal. A set of candidate task templates are identified based on the task goal. The user specifies optimization criteria, and the set of candidate task templates is ranked based on the optimization criteria. Accordingly, at least a part of the ranked set is presented to the user, from which the user selects a task template. In other examples, an optimal task template is determined automatically. In some instances, a user selects a subtask of an existing task to optimize in view of optimization criteria. Accordingly, a set of candidate subtasks is identified. The set of candidate subtasks is ranked according to the optimization criteria, after which a user may select one or more replacement subtasks. As a result, subtasks of the task are replaced according to the selected subtask.

Abstract: 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.