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courses:rg:transductive_learning_for_statistical_machine_translation [2010/12/08 22:27] jawaid |
courses:rg:transductive_learning_for_statistical_machine_translation [2010/12/08 23:01] 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]. | + | * The Paper very well describes the transductive learning algorithm, **Algorithm 1** which is inspired by Yarowsky algorithm [1][2]. |
| * 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. | * 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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| * Experiments are performed on EuroParl and NIST corpus. On EuroParl corpus the selection and scoring was carried out using importance sampling with normalized score. Three different experiments are performed on the Europarl corpus which didn't produce significant improvement in the accuracy on output translation. Experiments on NIST data are performed using three different test sets where the output translation of each test set yield the best score when threshold-based selection method was combined with confidence estimation as scoring method. | * Experiments are performed on EuroParl and NIST corpus. On EuroParl corpus the selection and scoring was carried out using importance sampling with normalized score. Three different experiments are performed on the Europarl corpus which didn't produce significant improvement in the accuracy on output translation. Experiments on NIST data are performed using three different test sets where the output translation of each test set yield the best score when threshold-based selection method was combined with confidence estimation as scoring method. | ||
| - | * The main issue with this paper is that number of iterations that are selected to train the model are not described. And according | + | * The main issue with this paper is that number of iterations that are selected to train the model are not described. And according |
| - | * | + | * Adding examples in the paper on which improvement is achieved would have helped much better to understand the impact of this training scheme. |
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| + | * Ambiguous terminology is used for defining the feasibility setting in Table 3. ' | ||
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| ===== 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 | ||
| - | * [2] | + | * [2] S. Abney. 2004. Understanding the Yarowsky Algorithm. Comput. Ling., 30(3). |