Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: A technique is described herein for assisting a user in editing a file. The technique involves producing current context information that includes an input message and selected file content. The input message describes a user's editing objective, while the selected file content describes a portion of the file to which the editing objective is to be applied. The technique then requests a pattern-completion engine to generate edit information based on the current context information. The edit information describes one or more changes to the selected file content that satisfy the objective of the user. The pattern-completion engine uses a machine-trained autoregressive text-completion model that is trained on revision history information. The model can be trained in a process that incorporates various tests to ensure that the edit information that is generated works as expected, satisfies various performance metrics, and fulfills the editing objectives of the user.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: A technique is described herein for assisting a user in editing a file. The technique involves producing current context information that includes an input message and selected file content. The input message describes a user's editing objective, while the selected file content describes a portion of the file to which the editing objective is to be applied. The technique then requests a pattern-completion engine to generate edit information based on the current context information. The edit information describes one or more changes to the selected file content that satisfy the objective of the user. The pattern-completion engine uses a machine-trained autoregressive text-completion model that is trained on revision history information. The model can be trained in a process that incorporates various tests to ensure that the edit information that is generated works as expected, satisfies various performance metrics, and fulfills the editing objectives of the user.

Abstract: A computer-implemented technique is described herein for providing assistance to a user in performing various computer-related tasks. The technique relies on a state machine system that transitions among plural modes based on mode-specific cues provided by a pattern-completion engine. The pattern-completion engine, in turn, is induced to generate these cues based on initial context information provided to a context store of the state machine system. Among other information, the initial context information provides example dialogues that are annotated with mode-specific cues. Throughout its operation, the technique updates context information provided in the context store. The plural modes can include at least a user mode, an answer mode, and a command mode. The technique also provides various mechanisms to ensure the privacy of sensitive-information items and to reduce the risk that commands will damage execution platforms.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: A computer-implemented technique receives an input expression that a user submits with an intent to accomplish some objective. The technique then uses a machine-trained intent encoder component to map the input expression into an input expression intent vector (IEIV). The IEIV corresponds to a distributed representation of the intent associated with the input expression, within a vector intent vector space. The technique then leverages the intent vector to facilitate some downstream application task, such as the retrieval of information. Some application tasks also use a neighbor search component to find expressions that express an intent similar to that of the input expression. A training system trains the intent encoder component based on the nexus between queries and user clicks, as recorded in a search engine's search log.

Abstract: Computer-implemented techniques are described herein for generating and utilizing a universal encoder component (UEC). The UEC maps a linguistic expression in a natural language to a language-agnostic representation of the linguistic expression. The representation is said to be agnostic with respect to language because it captures semantic content that is largely independent of the syntactic rules associated with the natural language used to compose the linguistic expression. The representations is also agnostic with respect to task because a downstream training system can leverage it to produce different kinds to machine-trained components that serve different respective tasks. The UEC facilitates the generation of downstream machine-trained models by permitting a developer to train a model based on input examples expressed in a language j?, and thereafter apply it to the interpretation of documents in language j?, with no additional training required.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: A computer-implemented technique is described herein that receives an input expression that a user submits with an intent to accomplish some objective. The technique then uses a machine-trained intent encoder component to map the input expression into an input expression intent vector (IEIV). The IEIV corresponds to a distributed representation of the intent associated with the input expression, within a vector intent vector space. The technique then leverages the intent vector to facilitate some downstream application task, such as the retrieval of information. Some application tasks also use a neighbor search component to find expressions that express an intent similar to that of the input expression. A training system trains the intent encoder component based on the nexus between queries and user clicks, as recorded in a search engine's search log.

Abstract: Computer-implemented techniques are described herein for generating and utilizing a universal encoder component (UEC). The UEC maps a linguistic expression in a natural language to a language-agnostic representation of the linguistic expression. The representation is said to be agnostic with respect to language because it captures semantic content that is largely independent of the syntactic rules associated with the natural language used to compose the linguistic expression. The representations is also agnostic with respect to task because a downstream training system can leverage it to produce different kinds to machine-trained components that serve different respective tasks. The UEC facilitates the generation of downstream machine-trained models by permitting a developer to train a model based on input examples expressed in a language j?, and thereafter apply it to the interpretation of documents in language j?, with no additional training required.

Abstract: Various technologies described herein pertain to controlling execution of an automated search task on search results returned by a search engine. The search results are received, where the search results are returned by the search engine responsive to the search engine receiving a seed query. An instantiation of a parameterized query for the automated search task is executed over documents specified by the search results, where the instantiation of the parameterized query describes a linguistic pattern, a structural pattern, and a visual pattern. Further, a set of answer strings is extracted from the documents. The answer strings in the set match the linguistic pattern, the structural pattern, and the visual pattern. The search engine is controlled to provide an output in response to the user search query, the output being based on the set of answer strings extracted from the documents.

Abstract: Systems and methods for automated generation of new content responses to answer user queries are provided. The systems and methods for automated generation of new content responses answer user queries utilizing deep learning and a reasoning algorithm. The generated response is composed of new content and is not merely cut or copied information from one or more search results. Accordingly, the systems and methods for automated generation of new content responses provide tailored query specific answers that can be long and detailed including several sentences of information or that can be short and concise, such as “yes” or “no.” The ability of the systems and methods described herein to create or generate new content in response to a user query improves the usability, improves the performance, and/or improves user interactions of/with a search query system.

Abstract: Various technologies described herein pertain to controlling execution of an automated search task on search results returned by a search engine. The search results are received, where the search results are returned by the search engine responsive to the search engine receiving a seed query. An instantiation of a parameterized query for the automated search task is executed over documents specified by the search results, where the instantiation of the parameterized query describes a linguistic pattern, a structural pattern, and a visual pattern. Further, a set of answer strings is extracted from the documents. The answer strings in the set match the linguistic pattern, the structural pattern, and the visual pattern. The search engine is controlled to provide an output in response to the user search query, the output being based on the set of answer strings extracted from the documents.

Abstract: In one embodiment of the invention, a method for designing an integrated circuit is disclosed. The method includes automatically partitioning clock sinks of an integrated circuit design into a plurality of partitions; automatically synthesizing a clock tree from a master clock generator into the plurality of partitions to minimize local clock skew within each of the plurality of partitions; and automatically synthesizing clock de-skew circuitry into each of the plurality of partitions to control clock skew between neighboring partitions.