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courses:rg:reranking-by-multitask-learning [2010/10/18 10:42] ivanova |
courses:rg:reranking-by-multitask-learning [2010/10/22 13:56] vandas Basics of commentars and discussion after the reading |
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Kevin Duh, Katsuhito Sudoh, Hajime Tsukada, Hideki Isozaki, Masaaki Nagata | Kevin Duh, Katsuhito Sudoh, Hajime Tsukada, Hideki Isozaki, Masaaki Nagata | ||
[[http:// | [[http:// | ||
+ | [[http:// | ||
ACL 5th Workshop on Statistical Machine Translation (WMT) 2010 | ACL 5th Workshop on Statistical Machine Translation (WMT) 2010 | ||
- | ===== Before reading | + | ===== Suggestions for the presenter |
- | * < | + | It would be great to have an illustrative but simple example |
===== Comments ===== | ===== Comments ===== | ||
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+ | * < | ||
===== Opinions on the paper ===== | ===== Opinions on the paper ===== | ||
- | It would be great to have an illustrative but simple example of N-best list and also examples | + | TODO: suggestions |
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+ | Research group suggested that they extract only those features that has a nonzero weight in any of W. | ||
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+ | Comments by M. Popel: | ||
+ | Feature pruning using a treshold: When you have limited data, according to this work it worth to try a good feature than to set a treshold. | ||
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+ | We were arguing about the number | ||
+ | (I suppose that it is just number of input features, if they were really used is not clear.) | ||
+ | |||
+ | Every feature is only fired at the sentence where the conditions are met. | ||
+ | Example: 500 sentences, every sentence has just one N-best list. That means 500 weight vectors | ||
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+ | We argued about hashing the features together - in what way are they hashed? | ||
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