REORDERING WORDS FOR MACHINE TRANSLATION

• US 20150161109A1
• Filed: 01/13/2012
• Published: 06/11/2015
• Est. Priority Date: 01/13/2012
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: ICHIKAWA, Hiroshi, TALBOT, David, NEUBIG, Graham
• Application Number:
  US13/350,694
• Publication Number:
  US 20150161109A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/44 Statistical methods, e.g. probability models

Abstract

Embodiments generally relate to machine translation. In one embodiment, a method includes receiving a sentence of a source language. The method also includes parsing the sentence into words. The method also includes determining scores for the words, where each score is associated with features of each respective word. The method also includes reordering the words based on the scores.

Claims

1-20. -20. (canceled)

21. A computer-implemented method, comprising:

receiving, at a computing system having one or more processors, a sentence in a source language;

parsing, at the computing system, the sentence into a plurality of words;

determining, at the computing system, a score for each particular word of the plurality of words based on features of the particular word, the features including at least one of syntactic role, part of speech, and position within the sentence, wherein each score is indicative of a degree of likelihood of its corresponding word being a next word in a translated sentence corresponding to a translation of the sentence from the source language to a target language;

determining, at the computing system, pairwise data from the plurality of words, each particular word pair of the pairwise data including a different combination of words from the plurality of words;

determining, at the computing system, a reordering value for each particular word pair of the pairwise data based on the scores of words in the particular word pair, each reordering value being indicative of whether to reorder the words in the particular word pair;

reordering, at the computing system, the plurality of words in the sentence based on the reordering values to obtain a reordered sentence; and

obtaining, at the computing system, a translation of the sentence into a target language based on the reordered sentence.

22. A computer-implemented method, comprising:

receiving, at a computing system having one or more processors, a sentence in a source language;

parsing, at the computing system, the sentence into a plurality of words;

determining, at the computing system, a score for each particular word of the plurality of words based on a plurality of features of the particular word, the plurality of features including at least one of syntactic role, part of speech, and sentence position, wherein each score is indicative of a degree of likelihood of its corresponding word being a next word in a translated sentence corresponding to a translation of the sentence from the source language to a target language; and

reordering, at the computing system, the plurality of words based on the scores.

23. The computer-implemented method of claim 22, wherein determining the score for each word of the plurality of words comprises:

determining a sub-score corresponding to each feature of the plurality of features for each particular word; and

combining the sub-scores to obtain the score for each particular word.

24. The computer-implemented method of claim 23, wherein combining the sub-scores to obtain the score for each particular word comprises:

obtaining a weight corresponding to each feature of the plurality of features;

multiplying each sub-score by the weight corresponding to the particular feature to which the sub-score corresponds to obtain a weighted sub-score; and

summing the weighted sub-scores.

25. The computer-implemented method of claim 22, wherein determining the score for each word of the plurality of words is based on a parse tree of words of the plurality of words, wherein the parse tree includes a root node that is associated with a verb of the sentence, and wherein child nodes of the parse tree are associated with other words of the sentence.

26. The computer-implemented method of claim 22, wherein reordering the plurality of words based on the scores comprises:

determining one or more pairs of words;

comparing scores of the words in each of the one or more pairs of words; and

reordering the words in each of the one or more pairs of words based on the comparing of the scores.

27. The computer-implemented method of claim 26, wherein reordering the plurality of words based on the scores further comprises assigning a numerical value to each pair of the one or more pairs of words based on the reordering.

28. The computer-implemented method of claim 22, wherein reordering the plurality of words based on the scores comprises:

ranking the words of the plurality of words based on the scores; and

reordering the plurality of words based on the ranking.

30. The computer-implemented method of claim 22, wherein determining the score for each particular word comprises obtaining scores from a database that includes predetermined scores learned from training data.

31. The computer-implemented method of claim 22, further comprising:

obtaining a reordered sentence based on the reordering of the plurality of words; and

translating the sentence from the source language to a target language by translating each of the words in the reordered sentence from the source language to the target language.

29. (canceled)

32. A system comprising:

one or more processors; and

logic encoded in one or more non-transitory computer-readable media for execution by the one or more processors and when executed operable to perform operations comprising;

receiving a sentence in a source language;

parsing the sentence into a plurality of words;

determining a score for each particular word of the plurality of words based on a plurality of features of the particular word, the plurality of features including at least one of syntactic role, part of speech, and sentence position, wherein each score is indicative of a degree of likelihood of its corresponding word being a next word in a translated sentence corresponding to a translation of the sentence from the source language to a target language; and

reordering the plurality of words based on the scores.

33. The computer system of claim 32, wherein determining the score for each word of the plurality of words comprises:

determining a sub-score corresponding to each feature of the plurality of features for each particular word; and

combining the sub-scores to obtain the score for each particular word.

34. The computer system of claim 32, wherein combining the sub-scores to obtain the score for each particular word comprises:

obtaining a weight corresponding to each feature of the plurality of features;

multiplying each sub-score by the weight corresponding to the particular feature to which the sub-score corresponds to obtain a weighted sub-score; and

summing the weighted sub-scores.

35. The computer system of claim 32, wherein determining the score for each word of the plurality of words is based on a parse tree of words of the plurality of words, wherein the parse tree includes a root node that is associated with a verb of the sentence, and wherein child nodes of the parse tree are associated with other words of the sentence.

36. The computer system of claim 32, wherein reordering the plurality of words based on the scores comprises:

determining one or more pairs of words;

comparing scores of the words in each of the one or more pairs of words; and

reordering the words in each of the one or more pairs of words based on the comparing of the scores.

37. The computer system of claim 32, wherein reordering the plurality of words based on the scores comprises:

ranking the words of the plurality of words based on the scores; and

reordering the plurality of words based on the ranking.

39. The computer system of claim 32, wherein determining the score for each particular word comprises obtaining scores from a database that includes predetermined scores learned from training data.

40. The computer system of claim 32, wherein the operations further comprise:

obtaining a reordered sentence based on the reordering of the plurality of words; and

translating the sentence from the source language to a target language by translating each of the words in the reordered sentence from the source language to the target language.

38. (canceled)