TRANSLITERATION USING INDICATOR AND HYBRID GENERATIVE FEATURES

• US 20110218796A1
• Filed: 03/05/2010
• Published: 09/08/2011
• Est. Priority Date: 03/05/2010
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Suzuki, Hisami, Cherry, Colin Andrew
• Application Number:
  US12/717,968
• Publication Number:
  US 20110218796A1
• Time in Patent Office: Days
• US Class Current: 704/2
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06F 40/42 Data-driven translation
• CPC Class Codes: G06F 40/44 Statistical methods, e.g. probability models
• CPC Class Codes: G06F 40/53 Processing of non-Latin text kana-to-kanji conversion G06F40/129; vowelisation G06F40/232

Abstract

Described is a transliteration engine/substring decoder that back-transliterates an input string from a source language into an output string in a target language. The transliteration engine may be based upon discriminately weighted indicator features and/or generative models in which the decoder'"'"'s discriminative parameters are learned. The training data may be based on source-target pairs, which may be transformed into derivations. Features extracted from these derivations include indicator features and hybrid generative model features.

Claims

1. In a computing environment, a method performed on at least one processor, comprising:

receiving a source string;

transliterating the source string using one or more discriminatively trained models into a target string; and

outputting the target string.

2. The method of claim 1 wherein the source string is received from a machine translator, and wherein the target string is combined with translated text from the machine translator into translated output text.

3. The method of claim 1 further comprising, training one or more discriminatively trained generative models, including transforming source-target pairs into derivations.

4. The method of claim 3 wherein transforming the source-target pairs into derivations comprises aligning one or more characters of a source string with one or more characters of a target string.

5. The method of claim 1 further comprising, training one or more discriminatively trained generative models via perceptron training.

6. The method of claim 1 wherein transliterating the source string comprises decoding by performing operations on source substrings, with each operation producing one or more target characters.

7. In a computing environment, a system, comprising, a transliteration engine that processes an input string in one language into an output string in another language, the transliteration engine including a decoder that uses one or more generative models, the models corresponding to weighted probabilities, with the weights learned as parameters via discriminative training based upon training data.

8. The system of claim 7 wherein the transliteration engine is coupled to a machine translator to transliterate strings that the machine translator does not translate, or wherein the transliteration engine is used in a spelling application, or wherein the transliteration engine is both coupled to a machine translator to transliterate strings that the machine translator does not translate and is used in a spelling application.

9. The system of claim 7 wherein the transliteration engine is used in computing edit distance between two strings.

10. The system of claim 7 further comprising, an aligner that transforms source-target pairs into derivations that are used for the discriminative training.

11. The system of claim 7, wherein the discriminative training is based upon perceptron training technology, maximum entropy training technology, or multiple additive regression tree training technology.

12. The system of claim 7 wherein the discriminative training uses features, comprising indicator features and hybrid generative model features.

13. The system of claim 7 wherein the features include one or more emission-related features, one or more transition-related features, or one or more lexicon features, or any combination of one or more emission-related features, one or more transition-related features, or one or more lexicon features.

14. The system of claim 7 wherein the discriminative training uses indicator features, including channel indicators, language model indicators or lexicon indicators, or any combination of channel indicators, language model indicators or lexicon indicators.

15. The system of claim 7 wherein the discriminative training uses generative features, including one or more channel models, one or more language models, or one or more dictionary models, or any combination of one or more channel models, one or more language models, or one or more dictionary models.

16. The system of claim 7 wherein the discriminative training uses lexicon indicators corresponding to frequencies of generated target words.

17. The system of claim 7 wherein the discriminative training uses a feature that indicates a new word being introduced, a target word frequency feature, a target character count feature, or an operation count feature, or any combination of a feature that indicates a new word being introduced, a target word frequency feature, a target character count feature, or an operation count feature.

18. One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising, discriminatively training generative models for to tune parameters for transliteration, including learning relative weights of probabilities for generative features extracted from training data corresponding to derivations, the generative features comprising hybrid generative models, the probabilities representing emission information, emission information and lexicon related information, and using the discriminatively training generative models in transliteration of a source string to a target string.

19. The one or more computer-readable media of claim 18 having further computer-executable instructions, comprising, extracting indicator features from the training data.

20. The one or more computer-readable media of claim 18 further comprising, transforming source-target pairs in the training data into the training data corresponding to the derivations.