METHOD AND APPARATUS FOR GENERATING A TRANSLATION AND MACHINE TRANSLATION

• US 20080262829A1
• Filed: 02/25/2008
• Published: 10/23/2008
• Est. Priority Date: 03/21/2007
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Wang, Haifeng, Wu, Hua, Liu, Zhanyi
• Application Number:
  US12/036,568
• Publication Number:
  US 20080262829A1
• Time in Patent Office: Days
• US Class Current: 704/4
• CPC Class Codes: G06F 40/131 Fragmentation of text files, e.g. creating reusable text-blocks; Linking to fragments, e.g. using XInclude; Namespaces
• CPC Class Codes: G06F 40/45 Example-based machine translation; Alignment

Abstract

The present invention provides a method and an apparatus for generating a translation and machine translation. According to an aspect of the present invention, there is provided a method for generating a translation, wherein a sentence of a first language to be translated is split into a plurality of fragments, an aligned bilingual example corpus comprises a plurality of example sentence pairs of the first language and a second language and alignment information between each sentence pair, and comprises at least one translation fragment of the second language corresponding to each of said plurality of fragments of the first language; the method comprising: selecting an optimum translation fragment combination of the second language from a plurality of possible translation fragment combinations of the second language corresponding to said sentence of the first language based on an integrated score obtained from a plurality of feature functions on a translation fragment combination; and generating the translation of the second language based on said optimum translation fragment combination.

Claims

1. A method for generating a translation, wherein a sentence of a first language to be translated is split into a plurality of fragments, an aligned bilingual example corpus comprises a plurality of example sentence pairs of the first language and a second language and alignment information between each sentence pair, and comprises at least one translation fragment of the second language corresponding to each of said plurality of fragments of the first language;

the method comprising;

selecting an optimum translation fragment combination of the second language from a plurality of possible translation fragment combinations of the second language corresponding to said sentence of the first language based on an integrated score obtained from a plurality of feature functions on a translation fragment combination; and

generating the translation of the second language based on said optimum translation fragment combination.

2. The method according to claim 1, wherein said step of selecting comprises:

selecting an optimum translation fragment combination of the second language based on an integrated score obtained from a plurality of feature functions on each of said plurality of possible translation fragment combinations.

3. The method according to claim 1, wherein, said sentence of the first language to be translated is split in a plurality of splitting schemes, and said step of selecting comprises:

selecting an optimum translation fragment combination of the second language based on an integrated score obtained from a plurality of feature functions on a translation fragment combination of each of said plurality of splitting schemes.

4. The method according to claim 3, wherein said step of selecting comprises:

selecting an optimum translation fragment combination of the second language based on an integrated score obtained from a plurality of feature functions on each of said plurality of translation fragment combinations of each of said plurality of splitting schemes.

5. The method according to any one of claims 1-4, wherein said integrated score obtained from a plurality of feature functions on a translation fragment combination is calculated by integrating scores obtained from each of said plurality of feature functions on said translation fragment combination with a log-linear model.

6. The method according to claim 5, wherein said step of calculating said integrated score obtained from a plurality of feature functions on a translation fragment combination further takes into account a weight of each of said plurality of feature functions.

7. The method according to claim 6, wherein said step of calculating said integrated score obtained from a plurality of feature functions on a translation fragment combination is performed with the following formula:

$s<br><br>\left(e\right)=\underset{m=1}{\overset{M}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_m<br><br>h_m<br><br>\left(e,f,E\right)$

wherein h_m denotes the m^th feature function, λ

_m denotes the weight of the m^th feature function, f denotes said sentence of the first language to be translated, e denotes said translation fragment combination of the second language, E denotes a collection of translation fragments required to generate e, and s(e) denotes said integrated score obtained from said plurality of feature functions on e.

8. The method according to claim 1 or 3, wherein said step of selecting comprises:

selecting an optimum translation fragment combination of the second language by using a search algorithm, wherein an integrated score is obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments as a cost of said search algorithm.

9. The method according to claim 1, wherein said sentence of the first language to be translated is split in a plurality of splitting schemes, and said step of selecting comprises:

selecting an optimum translation fragment combination of the second language by using a search algorithm, wherein an integrated score is obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments as a cost of said search algorithm.

10. The method according to claim 8, wherein said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments is calculated by integrating scores obtained from each of said plurality of feature functions on said possible translation fragment or said combination of translation fragments with a log-linear model.

11. The method according to claim 10, wherein said step of calculating said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments further takes into account a weight of each of said plurality of feature functions.

12. The method according to claim 11, wherein said step of calculating said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments is performed with the following formula:

$s<br><br>\left(e\right)=\underset{m=1}{\overset{M}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_m<br><br>h_m<br><br>\left(e,f,E\right)$

wherein h_m denotes the m^th feature function, ?_m denotes the weight of the m^th feature function, f denotes said possible fragment or said combination of fragments of the first language, e denotes said possible translation fragment or said combination of translation fragments of the second language, E denotes a collection of translation fragments required to generate e, and s(e) denotes said integrated score obtained from said plurality of feature functions on e.

13. The method according to claim 7 or 12, wherein said plurality of feature functions comprise:

any functions selected from a translation probability of a word from a source language to a target language, a translation probability of a word from a target language to a source language, a translation probability of a phrase from a source language to a target language, a translation probability of a phrase from a target language to a source language, a selection probability of a target language based on length, a target language model, and a semantic similarity.

19. A method for machine translation, wherein an aligned bilingual example corpus comprises a plurality of example sentence pairs of a first language and a second language and alignment information between each sentence pair;

the method comprising;

splitting a sentence of the first language to be translated into a plurality of fragments; and

generating the translation of the second language by means of the method for generating a translation according to any one of claims 1-13.

14. A method for generating a translation, wherein an aligned bilingual example corpus comprises a plurality of example sentence pairs of a first language and a second language and alignment information between each sentence pair, a sentence of the first language to be translated is matched with respect to said aligned bilingual example corpus, and at least one translation fragment of the second language corresponding to each possible fragment of said sentence of the first language is obtained;

the method comprising;

selecting an optimum translation fragment combination of the second language by using a search algorithm, wherein an integrated score is obtained from a plurality of feature functions on a possible translation fragment or a combination of translation fragments as a cost of said search algorithm; and

generating the translation of the second language based on said optimum translation fragment combination.

15. The method according to claim 14, wherein said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments is calculated by integrating scores obtained from each of said plurality of feature functions on said possible translation fragment or said combination of translation fragments with a log-linear model.

16. The method according to claim 15, wherein said step of calculating said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments further takes into account a weight of each of said plurality of feature functions.

17. The method according to claim 16, wherein said step of calculating said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments is performed with the following formula:

$s<br><br>\left(e\right)=\underset{m=1}{\overset{M}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_m<br><br>h_m<br><br>\left(e,f,E\right)$

wherein h_m denotes the m^th feature function, ?_m denotes the weight of the m^th feature function, f denotes said possible fragment or said combination of fragments of the first language, e denotes said possible translation fragment or said combination of translation fragments of the second language, E denotes a collection of translation fragments required to generate e, and s(e) denotes said integrated score obtained from said plurality of feature functions on e.

18. The method according to claim 17, wherein said plurality of feature functions comprise:

any functions selected from a translation probability of a word from a source language to a target language, a translation probability of a word from a target language to a source language, a translation probability of a phrase from a source language to a target language, a translation probability of a phrase from a target language to a source language, a selection probability of a target language based on length, a target language model, and a semantic similarity.

20. A method for machine translation, wherein an aligned bilingual example corpus comprises a plurality of example sentence pairs of a first language and a second language and alignment information between each sentence pair;

the method comprising;

matching a sentence of the first language to be translated with respect to said aligned bilingual example corpus to obtain at least one translation fragment of the second language corresponding to each possible fragment of said sentence of the first language; and

generating the translation of the second language by means of the method for generating a translation according to any one of claims 14-18.

21. An apparatus for generating a translation, wherein a sentence of a first language to be translated is split into a plurality of fragments, an aligned bilingual example corpus comprises a plurality of example sentence pairs of the first language and a second language and alignment information between each sentence pair, and comprises at least one translation fragment of the second language corresponding to each of said plurality of fragments of the first language;

the apparatus comprising;

a selecting unit configured to select an optimum translation fragment combination of the second language from a plurality of possible translation fragment combinations of the second language corresponding to said sentence of the first language based on an integrated score obtained from a plurality of feature functions on a translation fragment combination; and

a translation generating unit configured to generate the translation of the second language based on said optimum translation fragment combination.

22. The apparatus according to claim 21, wherein said selecting unit is configured to select an optimum translation fragment combination of the second language based on an integrated score obtained from a plurality of feature functions on each of said plurality of possible translation fragment combinations.

23. The apparatus according to claim 21, wherein said sentence of the first language to be translated is split in a plurality of splitting schemes, and said selecting unit is configured to select an optimum translation fragment combination of the second language based on an integrated score obtained from a plurality of feature functions on a translation fragment combination of each of said plurality of splitting schemes.

24. The apparatus according to claim 23, wherein said selecting unit is configured to select an optimum translation fragment combination of the second language based on an integrated score obtained from a plurality of feature functions on each of said plurality of translation fragment combinations of each of said plurality of splitting schemes.

25. The apparatus according to any one of claims 21-24, further comprising a calculating unit configured to calculate said integrated score obtained from a plurality of feature functions on a translation fragment combination by integrating scores obtained from each of said plurality of feature functions on said translation fragment combination with a log-linear model.

26. The apparatus according to claim 25, wherein said calculating unit further takes into account a weight of each of said plurality of feature functions during calculating said integrated score obtained from a plurality of feature functions on a translation fragment combination.

27. The apparatus according to claim 26, wherein said calculating unit calculates said integrated score obtained from a plurality of feature functions on a translation fragment combination with the following formula:

$s<br><br>\left(e\right)=\underset{m=1}{\overset{M}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_m<br><br>h_m<br><br>\left(e,f,E\right)$

wherein h_m denotes the m^th feature function, ?_m denotes the weight of the m^th feature function, f denotes said sentence of the first language to be translated, e denotes said translation fragment combination of the second language, E denotes a collection of translation fragments required to generate e, and s(e) denotes said integrated score obtained from said plurality of feature functions on e.

28. The apparatus according to claim 21 or 23, wherein said selecting unit is configured to select an optimum translation fragment combination of the second language by using a search algorithm, wherein an integrated score is obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments as a cost of said search algorithm.

29. The apparatus according to claim 21, wherein said sentence of the first language to be translated is split in a plurality of splitting schemes, and said selecting unit is configured to select an optimum translation fragment combination of the second language by using a search algorithm, wherein an integrated score is obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments as a cost of said search algorithm.

30. The apparatus according to claim 28, further comprising a calculating unit configured to calculate said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments by integrating scores obtained from each of said plurality of feature functions on said possible translation fragment or said combination of translation fragments with a log-linear model.

31. The apparatus according to claim 30, wherein said calculating unit further takes into account a weight of each of said plurality of feature functions during calculating said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments.

32. The apparatus according to claim 31, wherein said calculating unit is configured to calculate said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments with the following formula:

$s<br><br>\left(e\right)=\underset{m=1}{\overset{M}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_m<br><br>h_m<br><br>\left(e,f,E\right)$

wherein h_m denotes the m^th feature function, ?_m denotes the weight of the m^th feature function, f denotes said possible fragment or said combination of fragments of the first language, e denotes said possible translation fragment or said combination of translation fragments of the second language, E denotes a collection of translation fragments required to generate e, and s(e) denotes said integrated score obtained from said plurality of feature functions on e.

33. The apparatus according to claim 27 or 32, wherein said plurality of feature functions comprise:

any functions selected from a translation probability of a word from a source language to a target language, a translation probability of a word from a target language to a source language, a translation probability of a phrase from a source language to a target language, a translation probability of a phrase from a target language to a source language, a selection probability of a target language based on length, a target language model, and a semantic similarity.

39. An apparatus for machine translation, wherein an aligned bilingual example corpus comprises a plurality of example sentence pairs of a first language and a second language and alignment information between each sentence pair;

the apparatus comprising;

a splitting unit configured to split a sentence of the first language to be translated into a plurality of fragments; and

the apparatus for generating a translation according to any one of claims 21-33 configured to generate the translation of the second language.

34. An apparatus for generating a translation, wherein an aligned bilingual example corpus comprises a plurality of example sentence pairs of a first language and a second language and alignment information between each sentence pair, a sentence of the first language to be translated is matched with respect to said aligned bilingual example corpus, and at least one translation fragment of the second language corresponding to each possible fragment of said sentence of the first language is obtained;

the apparatus comprising;

a selecting unit configured to select an optimum translation fragment combination of the second language by using a search algorithm, wherein an integrated score is obtained from a plurality of feature functions on a possible translation fragment or a combination of translation fragments as a cost of said search algorithm; and

a translation generating unit configured to generate the translation of the second language based on said optimum translation fragment combination.

35. The apparatus according to claim 34, further comprising a calculating unit configured to calculate said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments by integrating scores obtained from each of said plurality of feature functions on said possible translation fragment or said combination of translation fragments with a log-linear model.

36. The apparatus according to claim 35, wherein said calculating unit further takes into account a weight of each of said plurality of feature functions during calculating said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments.

37. The apparatus according to claim 36, wherein said calculating unit is configured to calculate said integrated score obtained from said plurality of feature functions on a possible translation fragment or a combination of translation fragments with the following formula:

$s<br><br>\left(e\right)=\underset{m=1}{\overset{M}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_m<br><br>h_m<br><br>\left(e,f,E\right)$

wherein h_m denotes the m^th feature function, ?m denotes the weight of the m^th feature function, f denotes said possible fragment or said combination of fragments of the first language, e denotes said possible translation fragment or said combination of translation fragments of the second language, E denotes a collection of translation fragments required to generate e, and s(e) denotes said integrated score obtained from said plurality of feature functions on e.

38. The apparatus according to claim 37, wherein said plurality of feature functions comprise:

any functions selected from a translation probability of a word from a source language to a target language, a translation probability of a word from a target language to a source language, a translation probability of a phrase from a source language to a target language, a translation probability of a phrase from a target language to a source language, a selection probability of a target language based on length, a target language model, and a semantic similarity.

40. An apparatus for machine translation, wherein an aligned bilingual example corpus comprises a plurality of example sentence pairs of a first language and a second language and alignment information between each sentence pair;

the apparatus comprising;

a matching unit configured to match a sentence of the first language to be translated with respect to said aligned bilingual example corpus to obtain at least one translation fragment of the second language corresponding to each possible fragment of said sentence of the first language; and

the apparatus for generating a translation according to any one of claims 34-38 configured to generate the translation of the second language.