Efficient online domain adaptation

• US 9,213,694 B2
• Filed: 10/10/2013
• Issued: 12/15/2015
• Est. Priority Date: 10/10/2013
• Status: Active Grant

• Current Assignee: SDL PLC (RWS Holdings Plc)
• Original Assignee: Language Weaver, Inc. (RWS Holdings Plc)
• Inventors: Hieber, Felix, May, Jonathan
• Primary Examiner(s): Han, Qi
• Application Number:
  US14/051,175
• Publication Number:
  US 20150106076A1
• Time in Patent Office: 796 Days
• Field of Search: 704/2, 704/4, 704/5, 704/7, 704/8, 704/10
• US Class Current: 1/1
• CPC Class Codes: G06F 40/45 Example-based machine translation; Alignment
• CPC Class Codes: G06F 40/51 Translation evaluation

Abstract

Systems and methods for efficient online domain adaptation are provided herein. Methods may include receiving a post-edited machine translated sentence pair, updating a machine translation model by adjusting translation weights for a translation memory and a language model while generating test machine translations of the machine translated sentence pair until one of the test machine translations approximately matches the post-edits for the machine translated sentence pair, and retranslating the remaining machine translation sentence pairs that have yet to be post-edited using the updated machine translation model.

Claims

1. A method of immediately updating a machine translation system with post-edits during translation of a document, using a machine translation system that comprises a processor and a memory for storing logic that is executed by the processor to perform the method, comprising:

receiving a post-edited machine translated sentence pair, the post-edited machine translated sentence pair comprising a source sentence unit and a post-edited target sentence unit;

updating a machine translation model by;

performing an alignment of the post-edits of the machine translated sentence pair to generate phrases; and

adding the phrases to the machine translation model;

adapting a language model from the post-edited target sentence unit;

calculating translation statistics for the post-edits;

adjusting translation weights using the translation statistics while generating test machine translations of the machine translated sentence pair until one of the test machine translations approximately matches the post-edits for the machine translated sentence pair; and

retranslating remaining machine translation sentence pairs that have yet to be post-edited using the updated machine translation model and the adjusted translation weights.

2. The method according to claim 1, further comprising:

receiving a document for translation from a source language into a target language; and

performing a machine translation of the document to generate a set of machine translated sentence pairs.

3. The method according to claim 1, wherein the updating of the machine translation model further comprises tokenizing the post-edits of the machine translated sentence pair.

4. The method according to claim 3, wherein the updating of the machine translation model further comprises updating a vocabulary of source sentence units with unknown source sentence units included in the post-edits of the machine translated sentence pair and updating a vocabulary of target sentence units with unknown target sentence units included in the post-edits of the machine translated sentence pair.

5. The method according to claim 4, further comprising extracting fractional counts from the post-edits of the machine translated sentence pair and adding the extracted fractional counts to a fractional count table.

6. The method according to claim 5, further comprising adjusting probability distributions for the source sentence unit of the post-edits of the machine translated sentence pair.

7. The method according to claim 1, further comprising updating a phrase table with counts that define a number of occurrences of phrases in the phrase table;

and reordering feature values for the phrases in the phrase table based upon the counts.

8. The method according to claim 1, wherein the translation weights for the machine translation system are adjusted using discriminative ridge regression.

9. The method according to claim 1, wherein the adapting of the language model includes executing an ngram-count of the post-edited machine translated sentence pair to update a count file that comprises counts for input sentence pairs;

and recompiling the language model using a smoothing algorithm.

10. The method according to claim 1, wherein the alignment includes both forward and reverse alignments of the post-edited machine translated sentence pair.

11. A machine translation system that immediately incorporates post-edits into a machine translation model during translation of a document, the machine translation system comprising:

a processor; and

a memory for storing logic that is executed by the processor to;

receive a post-edit of a target sentence unit of a machine translated sentence pair of a set of machine translated sentence pairs, wherein a machine translated sentence pair comprises a source sentence unit and the target sentence unit;

receive a post-edited machine translated sentence pair, the post-edited machine translated sentence pair comprising a post-edited source sentence unit and a post-edited target sentence unit;

update the machine translation model by;

performing an alignment of the post-edits of the machine translated sentence pair to generate phrases; and

adding the phrases to the machine translation model;

adapt a language model from the post-edited target sentence unit;

calculate translation statistics for the post-edits;

adjust translation weights using the translation statistics while generating test machine translations of the machine translated sentence pair until one of the test machine translations approximately matches the post-edits for the machine translated sentence pair; and

retranslate remaining machine translation sentence pairs that have yet to be post-edited using the updated machine translation model.

12. The machine translation system according to claim 11, wherein the processor further executes the logic to:

receive the document for translation from a source language into a target language; and

perform a machine translation of the document to generate the set of machine translated sentence pairs.

13. The machine translation system according to claim 11, wherein the machine translation system updates the machine translation model by further tokenizing the post-edits of the machine translated sentence pair.

14. The machine translation system according to claim 13, wherein the machine translation system updates the machine translation model by updating a vocabulary of source sentence units with unknown source sentence units included in the post-edits of the machine translated sentence pair and updating a vocabulary of target sentence units with unknown target sentence units included in the post-edits of the machine translated sentence pair.

15. The machine translation system according to claim 14, wherein the processor further executes the logic to extract fractional counts from the post-edits of the machine translated sentence pair and adding the extracted fractional counts to a fractional count table.

16. The machine translation system according to claim 15, wherein the processor further executes the logic to adjust probability distributions for the source sentence unit of the post-edits of the machine translated sentence pair.

17. The machine translation system according to claim 11, wherein the processor further executes the logic to update a phrase table with counts that define a number of occurrences of phrases in the phrase table;

and reorder feature values for the phrases in the phrase table based upon the counts.

18. The machine translation system according to claim 11, wherein the translation weights for the machine translation system are adjusted using discriminative ridge regression.

19. The machine translation system according to claim 11, wherein the machine translation system is configured to adapt the language model by executing an ngram-count of the post-edited machine translated sentence pair to update a count file that comprises counts for input sentence pairs;

and recompiling the language model using a smoothing algorithm.

20. The machine translation system according to claim 11, wherein the machine translation system is configured to:

receive post-edits for a retranslated machine translation sentence pair;

re-updat the machine translation model;

calculate translation statistics for the post-edits of the retranslated machine translation sentence pair;

adjust translation weights using the translation statistics while generating test machine translations of the retranslated machine translated sentence pair until one of the test machine translations approximately matches the post-edits for the retranslated machine translation sentence pair; and

retranslate any remaining retranslated machine translation sentence pairs that have yet to be post-edited using the updated machine translation model and the translation weights.