Method for Increasing the Accuracy of Subject-Specific Statistical Machine Translation (SMT)

• US 20120284015A1
• Filed: 07/18/2012
• Published: 11/08/2012
• Est. Priority Date: 01/28/2008
• Status: Abandoned Application

• Current Assignee: William Drewes
• Original Assignee: William Drewes
• Inventors: Drewes, William
• Application Number:
  US13/551,752
• Publication Number:
  US 20120284015A1
• Time in Patent Office: Days
• US Class Current: 704/3
• CPC Class Codes: G06F 40/44 Statistical methods, e.g. probability models
• CPC Class Codes: G06F 40/51 Translation evaluation

Abstract

A method of improving the accuracy of the translation output of Statistical Machine Translation (SMT), while increasing the effectiveness of an ongoing professional human translation effort by correlating the ongoing professional human translation effort directly with the translation errors made by the system. Once the translation errors have been corrected by professional human translators and are re-input to the system, the SMT'"'"'s training process may ensure that the same, and possibly similar, translation error(s) may not occur again.

Claims

1-10. -10. (canceled)

11. A method for determining whether a sentence has been translated correctly by a Statistical Machine Translation (SMT) system, said sentence translation correctness determination being for sentences that relate to a specific subject and which are designated for translation utilizing a specific SMT subject-specific domain, and for effecting the ongoing incremental improvement of the accuracy of SMT sentence translation of said sentences that relate to a specific subject and which are designated for translation utilizing a specific SMT subject-specific domain, the method comprising:

sending a user interface, from the SMT system to a user system, the user interface having an option that is available to the user for entering a user-defined threshold value;

the SMT system including at least one machine having a processor system having at least one processor and having a memory system;

receiving, at the SMT system, input determining the user-defined threshold value;

allowing, by the SMT system, the user to modify the user-defined threshold value prior to and after each translation;

sending a user interface, from the SMT system to a user system, the user interface having an option that is available to the user to specify a subject-specific domain to be utilized for SMT sentence translation;

the SMT system including at least one machine having a processor system having at least one processor and having a memory system;

receiving, at the SMT system, input determining the user specified subject-specific domain;

allowing, by the SMT system, the user to modify the user specified subject-specific domain prior to and after each translation;

after the SMT system has produced a translation of a single sentence, determining, by the SMT system, a probability that each possible translation of each word of the sentence is correct;

for each word of the sentence determining, by the SMT system, which possible translation has a probability that the translation is correct that is a highest value compared to other possible translations of the word; and

after the SMT has translated the single sentence, for each word of the sentence,

comparing, by the processor system, the highest value to the user-defined threshold value to determine whether the highest value is either equal to, or higher than, the threshold value, and

if the highest value relating to each word in the sentence is either equal to or higher than the user defined threshold value, presenting a translation of the sentence as a correct translation, otherwise the sentence is determined to have been translated incorrectly;

effecting the ongoing incremental improvement of the accuracy of SMT sentence translation of sentences that relate to a specific subject and which are designated for translation utilizing a specific SMT subject-specific domain by way of

(1)—

the user entering a user-defined threshold value for SMT translation by a specific subject-specific domain

(2)—

submitting to SMT individual sentences, the subject of said sentences relating directly to the subject of the specific subject-specific domain, for translation, one sentence at a time

(3)—

if SMT determined that the sentence submitted for translation was translated incorrectly, sending the incorrectly translated sentence to a human translator for translation

(4)—

receiving from the human translator a translation of the sentence that was incorrectly translated, therein creating a correctly translated parallel corpus source and target language sentences

(5)—

inputting the correctly translated parallel corpus source and target language sentences into a training system for the SMT subject-specific domain, so that the same translation error will not occur again

the continuing and repeated incremental increase of the user-defined threshold value by the user for SMT translation by the subject-specific domain at times that the user determines that there is a sustained and measurable decrease in the percentage of incorrectly translated sentences, and the subsequent repetition of steps #s 2 through 5 above until the desired level translation accuracy relating to sentences translated utilizing the subject-specific domain has been achieved.

12. The method according to claim 11, further comprising:

receiving a specification of the language to be spoken by each participant in a voice-to-voice conversation;

receiving a specification of the specific subject of the voice-to-voice conversation;

receiving audio information generated by a speaker vocalizing a sentence in a source language;

transforming the audio information into text information, the translation being a translation of the text information of a source sentence, and

if the translation of the text information of the source sentence is determined to have been translated correctly, then

(1)—

vocalizing, by a voice synthesis module, the translation;

(2)—

allowing the speaker to continue verbalizing his/her next sentence without interruption;

if the translation is determined to be incorrect, then

(1)—

interrupting the speaker, by a voice synthesis message spoken in a language of the speaker, informing the speaker that the sentence was not understood by the SMT System;

(2)—

playing to the speaker an audio recording of the speaker verbalizing the sentence spoken;

(3)—

requesting, by the voice synthesis message in the language of the speaker, the speaker to restate the sentence using different words;

(4) receiving from the speaker a restatement of the sentence; and

(5)—

repeating steps 1 through 4 until the sentence spoken by the speaker has been translated correctly.

13. The method according to claim 11 further comprising:

receiving a specification of a language of an e-mail and a specification of a language to which the e-mail is to be translated;

receiving a specification of the specific subject of the e-mail;

receiving text of the e-mail;

receiving a request from a user machine to translate the e-mail;

in response translating the e-mail;

if the SMT system detects at least one sentence that has been determined to have been translated incorrectly, sending information for rendering a display of the e-mail to the user'"'"'s machine, with the at least one sentence that has been translated incorrectly highlighted;

receiving a rewrite of the at least one sentence in different words and a request for a translation of the at least one sentence;

if at least one sentence was translated incorrectly, repeating the sending of the display of the e-mail to the user'"'"'s machine, the receiving of the rewrite of the at least one sentence in different words, and the request for the translation of the at least one sentence, until all sentences in the e-mail have been translated correctly; and

preventing the e-mail from being sent until every sentence in the e-mail has been determined to have been translated correctly.

14. The method according to claim 11 further comprising:

receiving a specification of a file to be translated;

receiving a specification of the specific subject of the file to be translated;

receiving a request specifying a language in which the selected file is written and the language to which the file is to be translated;

initiating a file translation process;

performing a translation error correction for the file.

15. The method according to claim 11, further comprising performing a sentence error correction and subject-specific domain accuracy improvement process including at least:

sending a sentence that was incorrectly translated to a human translator for translation, the sentence being from a specific bulk text material file or a specific e-mail that was submitted for translation, with one or more words that were translated incorrectly within the sentence highlighted;

receiving from the human translator a translation of the sentence that was incorrectly translated, therein creating a correctly translated parallel corpus source and target language sentences;

inputting the correctly translated parallel corpus source and target language sentences into a training system for the SMT, so that the same translation error will not occur again.

16. The method according to claim 11, further comprising performing a sentence error correction and subject-specific domain accuracy improvement process including at least:

sending a sentence that was incorrectly translated to a human translator for translation, the sentence being from a specific voice-to-voice interactive conversation that was submitted for translation, with one or more words that were translated incorrectly within the sentence highlighted;

receiving from the human translator a translation of the sentence that was incorrectly translated, therein creating a correctly translated parallel corpus source and target language sentences;

inputting the correctly translated parallel corpus source and target language sentences into a training system for the SMT, so that the same translation error will not occur again.

17. A method according to claim 11 further comprising:

sending a sentence to a human translator for translation, the sentence being from a subject-specific voice-to-voice interactive conversation, the sentence having been identified as being associated with a voice recognition error that occurred, thereby resulting in an inability of the voice recognition module to correctly transcribe a source sentence from voice to text;

playing an audio recording of a single sentence as spoken by a conversation participant during the voice-to-voice interactive conversation so as to enable the human translator to listen to the audio recording of the sentence and manually transcribe the source language sentence to text;

receiving from the human translator a translation of the sentence that was incorrectly translated, therein creating a correctly translated parallel corpus source and target language sentences;

inputting the correctly translated parallel corpus source and target language sentences into a training system for the SMT, so that the same translation error will not occur again.

18. The method according to claim 11, further comprising:

if it is determined that a sentence has been translated incorrectly, storing the sentence that was incorrectly translated in a location where a human translator has access, presenting an interface for the human translator with tools for accessing incorrectly translated sentences one at a time;

receiving, by the interface, a request to correctly translate an incorrectly translated sentence;

sending information for rendering the incorrectly translated sentence, the information including information for displaying the incorrectly translated sentence that was requested, highlighting one or more words that were translated incorrectly within the incorrectly translated sentence;

in response, receiving from the human translator a translation of the sentence that was incorrectly translated, therein creating a correctly translated parallel corpus source and target language sentences;

inputting the correctly translated parallel corpus source and target language sentences into a training system for the SMT, so that the same translation error will not occur again.

19. A method according to claim 15, further comprising computing an approximation of the average of the highest threshold values for each word with one or multiple meanings within each sentence used to generate a given subject-specific domain, the computing including at least:

deriving a statistically large quantity of sentence data relative to a size of the given subject-specific domain with sentence data relevant to the subject of the subject specific domain;

the statically large quantity being large enough to be statistically significant and therein representative of a true state of the subject specific domain;

accumulating the statistically large quantity of sentence data relating to the subject of the given subject-specific domain, and each sentence thereof is stored as a record in a file, said file being referred to herein as a “

Subject-Specific Domain Accuracy Improvement File”

(SSDAI file), removing from a specific SSDAI file sentences having Voice Recognition (VR) errors;

inputting to the SMT system the SSDAI file;

determining a average of the highest threshold values for each word with one or multiple meanings within each sentence in the SSDAI file;

1—

after the SMT system has translated a sentence contained in a SSDAI file record, a highest probability that a translation of a word is correct relating to each of individual word in the sentence are mathematically added to a first counter;

2—

the number of words in the SSDAI file sentence being processed is mathematically added to a second counter;

3—

after the translation processing of all sentences in the SSDAI file is complete, the first counter is divided by the second counter, resulting in an average highest percentage value for all words in the SSDAI, which, given a statistically large SSDAI file relative to a given subject-specific domain, is an approximation of the average of the highest threshold values for each word with one or multiple meanings within each sentence in the specific subject-specific domain.

20. A method according to claim 19, further comprising improving an accuracy of a subject-specific domain on an on-going progressive basis, wherein,

preparing for application run-time a specific SSDAI file relating specifically to the subject of a given subject-specific domain by utilizing a Bulk Text Material Translation System which utilizes a Statistical Machine Translation (SMT);

using the above mentioned specific SSDAI file as input, computing an approximation of an average of highest threshold values for each word with one or multiple meanings within each sentence used to generate a given Statistical Machine Translation (SMT) subject-specific domain and setting the user-defined threshold value to the approximation of the average of the highest threshold values for the above mentioned Batch Text Material Translation application run;

processing sentences that have been translated incorrectly during the above mentioned Batch Text Material application run by a sentence error correction and subject-specific domain accuracy improvement process;

continually raising the user defined threshold value in user defined intervals and repeating the above Batch Text Material Translation application run so as to identify further incorrectly translated sentences to be processed by the sentence error correction and subject-specific domain accuracy improvement process;

repeating the preparing, the using, the processing and the continually raising until the desired highest threshold value for the specific subject-specific domain has been achieved based on computing the approximation of the average of the highest threshold values for each word with one or multiple meanings within each sentence used to generate a specific Statistical Machine Translation (SMT) subject-specific domain.