Google Inc.

Yahoo! Inc.

Other

Howard Scott Roy

Markus Freitag

Isaac Caswell

Oriol Vinyals

Larry Paul Heck

Brian Strope

Deepak Agarwal

Fuchun Peng

John Thrall

Bee-Chung Chen

Thomas L Dean

Amitabh Seth

Nitin Motgi

Raghu Ramakrishnan

Ben M Shahshahani

Pradheep K Elango

Vik Singh

Narinder Pal Singh

Vijay Krishna Narayanan

Shalini Ghosh

Joe Zachariah

Sharat Israni

Chandar Venkataraman

Amit Phadke

Michael Salisbury

Techniques are disclosed for training and/or utilizing an alignments and language model ('ALM') in automatically determining an ALM score corresponding with natural language text generated using a natural language generation model. The natural language text generated using the natural language generation model can be based on a set of structured data. Additionally or alternatively, the ALM can include a fluency model portion and a semantics model portion. The fluency model portion can be used in determining the fluency and/or grammar of the text. The semantics model portion be used in evaluating the content of the natural language text with respect to the content of the structured data.

Automatic evaluation of natural language text generated based on structured data

Techniques are disclosed for training and/or utilizing an automatic post-editing model in correcting translation error(s) introduced by a neural machine translation model. The automatic post-editing model can be trained using automatically generated training instances. A training instance is automatically generated by processing text in a first language using a neural machine translation model to generate text in a second language. The text in the second language is processed using a neural machine translation model to generate training text in the first language. A training instance can include the text in the first language as well as the training text in the first language.

Automatic post-editing model for generated natural language text

Automatic post-editing model for neural machine translation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing a natural language based program interface to software applications. One of the methods includes, obtaining, via a natural language front end, a natural language query or a natural language update statement issued by a software application; converting the natural language query or natural language update statement into structured operations to be performed on APIs of a knowledge base; performing the structured operations on the APIs to produce a natural language output statement; and providing, via a natural language output interface, the natural language output statement to the software application. The knowledge base stores entity information according to a data schema and has structured APIs for use by software applications to query the knowledge base; the software applications are limited to communicating with the knowledge base through the interfaces provided by the natural language front end.

Providing a natural language based application program interface

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing word sequences using neural networks. One of the methods includes receiving a first sequence of words arranged according to a first order; and for each word in the first sequence, beginning with a first word in the first order: determining a topic vector that is associated with the word; generating a combined input from the word and the topic vector, and processing the combined input through one or more sequence modeling layers to generate a sequence modeling output for the word; and processing one or more of the sequence modeling outputs through an output layer to generate a neural network output for the first sequence of words.

Topic-based sequence modeling neural networks

In some implementations, data that indicates multiple candidate transcriptions for an utterance is received. For each of the candidate transcriptions, data relating to use of the candidate transcription as a search query is received, a score that is based on the received data is provided to a trained classifier, and a classifier output for the candidate transcription is received. One or more of the candidate transcriptions may be selected based on the classifier outputs.

Speech recognition using domain knowledge

A computer-implemented method for constructing a profile for a target user is disclosed. The method comprises monitoring electronic communications across a network to identify at least one electronic communication that identifies a target user as an intended message recipient, extracting metadata from content associated with the at least one electronic communication; and constructing a user profile for the target user on the basis of the extracted metadata. It is assumed that the message senders send their messages, including associated content, to a given target user with the belief that the content is of some interest to the target user on the basis of some knowledge regarding the target user's personality, preferences, tastes and the like. In this manner, a profiling entity may indirectly construct a profile of the target user based on the content sent by one or more message senders to the target user.

System and method for creating user profiles

A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.

Customized today module

A display having a plurality of pixel locations includes first and second display layers, each including a cell at each pixel location, wherein each cell includes a liquid-crystal layer for modulating light passing through the display and a capacitor for controlling the liquid-crystal layer. Also disclosed are a method of making a display in accordance with the present invention and a method of displaying a composite image.

Multiple-shutter flat-panel display having individually controlled

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Palo Alto, CA, United States of America

Howard Scott Roy