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courses:rg:transductive_learning_for_statistical_machine_translation [2010/12/08 21:25] jawaid |
courses:rg:transductive_learning_for_statistical_machine_translation [2010/12/08 21:58] jawaid |
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| ===== Comments ===== | ===== Comments ===== | ||
| - | * The Paper very well describes the transductive learning algorithm, **Algorithm 1** which is inspired by Yarowsky algorithm [1]. | + | |
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| + | * In algorithm 1, the translation model is estimated based on the sentence pairs in bilingual data L. Then a set of source language sentences, U, is translated based on the current model. A subset of good transaltions and their sources, Ti, is selected on each iteration and added to the training data. These sentence pairs are replaces in each iteration and only the original data, L, is fixed throughout algorithm. | ||
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| + | * Algorithm 1 is based on **Estimate**, | ||
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| + | * Estimate function estimates the model parameters or in other words perform training of the system. The authors used three different model for parameters estimation. **Full Re-training**, | ||
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| + | * Scoring function assign a score to each translation t. The scoring functions used in the paper are: **Length-normalized Score** and **Confidence Estimation**. | ||
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| + | * Selection function is used to create additional training data Ti which is used in next iteration i+1 by **Estimate** to augment the original bilingual data. The selection functions used in this paper are: **Importance Sampling**, **Selection using a Threshold** and **Keep All**. | ||
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| + | * Data filtering is performed on both bilingual and monolingual data to keep only that part of the data which is relevant to the test data. | ||
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| + | * They used three different evaluation metrics for evaluating translation qulaity: **BLEU** score, **mWER** (multi-reference word error rate) and **mPER** (multi-reference position-independent word error rate). | ||
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| - | * In algorithm 1, the translation model is estimated based on the sentence pairs in bilingual data L. Then a set of source language sentences, U, is translated based on the current model. A subset of good transaltions and their sources, Ti, is selected on each iteration and added to the training data. These sentence pairs are replaces in each iteration and only the original data, L, is fixed throughout algorithm. | ||
| - | * Algorithm 1 is based on **Estimate**, | ||
| ===== Suggested Additional Reading ===== | ===== Suggested Additional Reading ===== | ||
| * [1] D. Yarowsky. 1995. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In Proc. ACL | * [1] D. Yarowsky. 1995. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In Proc. ACL | ||
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| * [2] | * [2] | ||