Google Inc.

Shankar Kumar

Franz Josef Och

Wolfgang Macherey

Joshua James Estelle

Christopher James Dyer

Peng Xu

Tara N Sainath

Trevor D Strohman

Awaneesh Verma

Sarveshwar Duddu

Eahab Ibrahim

Ronny Huang

Roy W Tromble

Felix Stahlberg

A computer-implemented method includes receiving audio data that corresponds to an utterance spoken by a user and captured by a user device. The method also includes processing the audio data to determine a candidate transcription that includes a sequence of tokens for the spoken utterance. Tor each token in the sequence of tokens, the method includes determining a token embedding for corresponding token, determining a n-gram token embedding for a previous sequence of n-gram tokens, and concatenating the token embedding and the n-gram token embedding to generate a concatenated output for the corresponding token. The method also includes rescoring the candidate transcription for the spoken utterance by processing the concatenated output generated for each corresponding token in the sequence of tokens.

Lookup-table recurrent language model

The technology addresses ambiguity in neural machine translation. An encoder module receives a given text exemplar and generates an encoded representation of it. A decoder module receives the encoded representation and a set of translation prefixes. The decoder module outputs an unbounded function corresponding to a set of tokens associated with each pair of the given text exemplar and translation prefix from the set of translation prefixes. Each token is assigned a probability between 0 and 1 in a vocabulary of the exemplar at each time step. A logits module generates, based on the unbounded function, a corresponding bounded conditional probability for each token, wherein the probabilities are not normalized over the vocabulary at each time step. A loss function module having a positive loss component and a scaled negative loss component identifies whether each target text of a set of target texts is a valid translation of the exemplar.

Modeling ambiguity in neural machine translation

A computer-implemented method includes receiving a document and a request to translate the document to a different language, the document including at least one tag associated with a first portion of text within the document, receiving a manual translation of the document translated by a human translator but not including the at least one tag, generating a plurality of alignments between the document and the manual translation using a statistical alignment model, selecting one of the plurality of alignments based on a likelihood that the first portion of text in the document corresponds to an aligned second portion of text within the manual translation, mapping a location of the tag in the document to a corresponding location within the manual translation based on the selected alignment, and inserting the at least one tag into the manual translation at the corresponding location to obtain a modified manual translation of the document.

Techniques for translating documents including tags

Methods, systems, and apparatus, including computer program products, for language translation are disclosed. In one implementation, a method is provided. The method includes accessing a hypothesis space, where the hypothesis space represents a plurality of candidate translations; performing decoding on the hypothesis space to obtain a translation hypothesis that minimizes an expected error in classification calculated relative to an evidence space; and providing the obtained translation hypothesis for use by a user as a suggested translation in a target translation.

Optimizing parameters for machine translation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting alternative translations. In one aspect, a method includes receiving source language text; receiving translated text corresponding to the source language text from a machine translation system; receiving segmentation data for the translated text, wherein the segmentation data includes a first segmentation of the translated text, the first segmentation dividing the translated text into two or more segments; receiving one or more alternative translations for each of the two or more segments; presenting the source text and the translated text to a user in a user interface; and in response to a user selection of a first portion of the translated text, displaying, in the user interface, one or more alternative translations for a first segment to which the first portion of translated text corresponds according to the first segmentation.

Providing alternative translations

Systems, methods, and computer program products are provided for statistical machine translation. In some implementations a method is provided. The method includes receiving multi-lingual parallel text associating a source language, a target language, and one or more bridge languages, determining an alignment between the source language and the target language using a first bridge language that is distinct from the source language and the target language, and using the determined alignment to generate a candidate translation of an input text in the source language to the target language.

Word alignment with bridge languages

Methods, systems, and apparatus, including computer program products, for language translation are disclosed. In one aspect, a method includes accessing a translation hypergraph that represents a plurality of candidate translations, the translation hypergraph including a plurality of paths including nodes connected by edges; calculating first posterior probabilities for each edge in the translation hypergraph; calculating second posterior probabilities for each n-gram represented in the translation hypergraph based on the first posterior probabilities; and performing decoding on the translation hypergraph using the second posterior probabilities to convert a sample text from a first language to a second language.

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New York, NY, United States of America

Shankar Kumar