Adapter for allowing both online and offline training of a text to text system

• US 7,624,020 B2
• Filed: 09/09/2005
• Issued: 11/24/2009
• Est. Priority Date: 09/09/2005
• Status: Active Grant

• Current Assignee: SDL Inc (RWS Holdings Plc)
• Original Assignee: Language Weaver, Inc. (RWS Holdings Plc)
• Inventors: Yamada, Kenji, Langmead, Greg, Knight, Kevin
• Primary Examiner(s): DeCady; Albert
• Assistant Examiner(s): Stevens; Thomas H
• Application Number:
  US11/223,823
• Publication Number:
  US 20070094169A1
• Time in Patent Office: 1,537 Days
• Field of Search: 706/15, 706/10, 704/8, 704/237, 704/256.2, 704/258, 704/277
• US Class Current: 704/277
• CPC Class Codes: G06F 40/42 Data-driven translation

Abstract

An adapter for a text to text training. A main corpus is used for training, and a domain specific corpus is used to adapt the main corpus according to the training information in the domain specific corpus. The adaptation is carried out using a technique that may be faster than the main training. The parameter set from the main training is adapted using the domain specific part.

Claims

1. A computer implemented method, comprising:

first carrying out a first generic training using at least one corpus of language information based at least in part on Internet information, using a first generic training operation to obtain a first generic parameter set;

second carrying out a second domain specific training using a fast train module associated with a domain specific corpus, said fast train module including a second domain specific training operation which operates faster than said first generic training operation, and which is less accurate than said first generic training operation, to obtain a second domain specific parameter set;

merging said first generic parameter set and said second domain specific parameter set into a merged parameter set, and using said merged parameter set for a text to text operation, wherein said merging comprises a weighted merge between said first generic parameter set and said second domain specific parameter set; and

using said second domain specific parameter set to adapt said first generic parameter set to carry out said to text operation, wherein said using comprises using partial information from the first generic training and partial information from the second domain specific training, forming an original table and an override table, and using both said original table and said override table as part of said text to text operation.

2. A computer implemented method as in claim 1, wherein said text to text operation is a translation between first and second languages.

3. A computer implemented method as in claim 1, wherein said merging comprises an adaptive training merge between said first generic parameter set and said second domain specific parameter set.

4. A computer implemented method as in claim 1, wherein said weighted merge is sensitive to frequency of specified terms in the corpus.

5. A computer implemented method as in claim 1, wherein said second domain specific training operation uses parameters from said first generic training operation.

6. A computer implemented method as in claim 5, wherein said second domain specific training operation uses a basic seed probability from the first generic training operation.

7. A computer implemented method as in claim 1, wherein said merging uses an adaptive merging.

8. A computer implemented method as in claim 7, wherein said adaptive merging uses a merge which is proportional to a frequency of a specified term in a training database.

9. A computer implemented method as in claim 1, wherein said merging comprises adding indications of counts.

10. A computer implemented method as in claim 1, wherein said merging comprises adding information that represent counts related to alignment.

11. A computer implemented method as in claim 1, wherein said first carrying out is carried out at a first location, and said second carrying out is carried out at a second location, different than said first location.

12. A computer implemented method as in claim 1, wherein the override table includes precomputed versions of specified formulas.

13. A computer implemented method as in claim 1, wherein the partial information includes probabilities.

14. A computer implemented method as in claim 1, wherein the partial information includes counts.

15. An apparatus, comprising:

a first training computer at a first location, carrying out a first generic training using at least one corpus of information based at least in part on Internet information, using a first generic training operation to obtain a first generic parameter set; and

a second training computer, at a second location, different than the first location, carrying out a second domain specific training using a fast train module associated with a domain specific corpus that has different information than said at least one corpus, said fast train module including a second domain specific training operation which operates faster than said first generic training operation, and which is less accurate than said first generic training operation, to obtain a second domain specific parameter set, and using said first generic parameter set and said second domain specific parameter set together for a text to text operation,

wherein said second training computer also operates to merge said first generic parameter set and said second domain specific parameter set into a merged parameter set, to use said merged parameter set for said text to text operation, and to carry out a weighted merge between said first generic parameter set and said second domain specific parameter set, and

wherein said training second computer uses partial information from the first generic training and partial information from the second domain specific training, forms an original table and an override table, and uses both said original table and said override table as part of said text to text operation.

16. An apparatus as in claim 15, wherein said text to text operation is a translation between first and second languages.

17. An apparatus as in claim 15, wherein said second training computer carries out an adaptive training merge between said first generic parameter set and said second domain specific parameter set.

18. An apparatus as in claim 15, wherein said override table represents information which is present in both the at least one corpus and the domain specific corpus.

19. An apparatus as in claim 15, wherein the override table includes precomputed versions of specified formulas.

20. An apparatus as in claim 15, wherein the partial information includes probabilities.

21. An apparatus, comprising:

a training part including at least one computer, which carries out a first generic training for a text to text operation using at least one corpus of training information based at least in part on Internet information, to obtain a first generic parameter set and at a different time than first generic training, carrying out a second domain specific training using a fast train module associated with a domain specific corpus that has different information than said at least one corpus, said fast train module including a second domain specific training operation which operates faster than said first generic training operation, and which is less accurate than said first generic training operation, to obtain a second domain specific parameter set and using said second domain specific parameter set to adapt said first generic parameter set to create an adapted parameter set, and to use the adapted parameter set for a text to text operation,

wherein said at least one training computer merges said first generic parameter set and said second domain specific parameter set into a merged parameter set, and uses said merged parameter set for said text to text operation, and carries out a weighted merge between said first generic parameter set and said second domain specific parameter set, and

wherein said at least one training computer uses partial information from the first generic training and partial information from the second domain specific training, forms an original table and an override table, and uses both said original table and said override table as part of said text to text operation.

22. An apparatus as in claim 21, wherein said text to text operation is a translation between first and second languages.

23. An apparatus as in claim 21, wherein said training computer carries out an adaptive training merge between the first generic parameter set and said second domain specific parameter set.

24. An apparatus as in claim 21, wherein said weighted merge is sensitive to frequency of specified terms in the corpus.