Google Inc.

University of Southern California

Ignacio E Thayer

Franz Josef Och

Peng Xu

Jeffrey Adgate Dean

Jay Michael Ponte

Thorsten Brants

Alexander Mark Franz

Jeffrey Chin

Daniel Rosart

John S Hawkins

Sha-Mayn Teh

Karel Driesen

Anton Carver

Daniel Marcu

Kevin Knight

Michael Jahr

Dmitriy Genzel

Ioannis Tsochandaridis

Michel Galley

Mark Hopkins

Jeffrey Dean

Jay Ponte

John Hawkins

Systems, methods, and apparatus for accessing distributed models in automated machine processing, including using large language models in machine translation, speech recognition and other applications.

Encoding and adaptive, scalable accessing of distributed models

Systems, methods, and apparatuses including computer program products for machine learning. A method is provided that includes determining model parameters for a plurality of feature functions for a linear machine learning model, ranking the plurality of feature functions according to a quality criterion, and selecting, using the ranking, a group of feature functions from the plurality of feature functions to update with the determined model parameters.

Minimum error rate training with a large number of features for machine learning

Training and translation using trees and/or subtrees as parts of the rules. A target language is word aligned with a source language, and at least one of the languages is parsed into trees. The trees are used for training, by aligning conversion steps, forming a manual set of information representing the conversion steps and then learning rules from that reduced set. The rules include subtrees as parts thereof, and are used for decoding, along with an n-gram language model and a syntax based language mode.

Training for a text-to-text application which uses string to tree conversion for training and decoding

Systems, methods, and apparatuses including computer program products are provided for training machine learning systems. In some implementations, a method is provided. The method includes receiving a collection of phrases, normalizing a plurality of phrases of the collection of phrases, the normalizing being based at least in part on lexicographic normalizing rules, and generating a normalized phrase table including a plurality of key-value pairs, each key value pair includes a key corresponding to a normalized phrase and a value corresponding to one or more un-normalized phrases associated with the normalized key, each un-normalized phrase having one or more parameters.

Linguistic key normalization

Growing community of inventors

San Francisco, CA, United States of America

Ignacio E Thayer