Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | Next revision Both sides next revision | ||
|
courses:rg:reranking-by-multitask-learning [2010/10/18 17:39] popel |
courses:rg:reranking-by-multitask-learning [2010/10/22 13:56] vandas Basics of commentars and discussion after the reading |
||
|---|---|---|---|
| Line 14: | Line 14: | ||
| ===== Opinions on the paper ===== | ===== Opinions on the paper ===== | ||
| + | |||
| + | TODO: suggestions to solve/ | ||
| + | |||
| + | Research group suggested that they extract only those features that has a nonzero weight in any of W. | ||
| + | |||
| + | 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. | ||
| + | |||
| + | We were arguing about the number of features used in sets. It is unlikely that they could somehow get the fixed number of features. | ||
| + | (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 | ||
| + | |||
| + | We argued about hashing the features together - in what way are they hashed? | ||