Google Inc.

David Robert Talbot

Franz Josef Och

Jakob David Uszkoreit

Ashish Venugopal

Jurij Ganitkevic

Thorsten Brants

Ashok Chhabedia Popat

Klaus Macherey

Andrew M Dai

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for decompounding compound words are disclosed. In one aspect, a method includes obtaining a token that includes a sequence of characters, identifying two or more candidate sub-words that are constituents of the token, and one or more morphological operations that are required to transform the sub-words into the token, where at least one of the morphological operations involves a use of a non-dictionary word, and determining a cost associated with each sub-word and a cost associated with each morphological operation.

Compound splitting

A way of detecting a watermark present in a structured result, such as a search result or a machine translation. The structured result is received and a hash is computed based upon at least part of the result. The resulting bit sequence is tested against a null hypothesis that the bit sequence was generated by a random variable with a binomial distribution with a parameter p=0.5. The result of this test is compared to a significance level, which indicates whether the structured result is watermarked.

Watermarking of structured results and watermark detection

A way of generating a watermark for a structured result, such as a search result or a machine translation. A hash function is used to generate a bit sequence for each of a plurality of structured results. A ranking score is generated for each resulting bit sequence. The ranking score can be based on the detectability of the bit sequence compared to a randomly-generated bit sequence and the quality of each of the structured results. A structured result is selected as the watermarked structured result based upon the ranking score.

Watermarking of structured results and watermark generation

Systems, methods, and apparatuses including computer program products are provided for encoding and using a language model. In one implementation, a method is provided. The method includes generating a compact language model, including receiving a collection of n-grams, each n-gram having one or more associated parameter values, determining a fingerprint for each n-gram of the collection of n-grams, identifying locations in an array for each n-gram using a plurality of hash functions, and encoding the one or more parameter values associated with each n-gram in the identified array locations as a function of corresponding array values and the fingerprint for the n-gram.

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San Francisco, CA, United States of America

David Robert Talbot