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courses:rg:2012:encouraging-consistent-translation [2012/10/17 11:40] dusek |
courses:rg:2012:encouraging-consistent-translation [2012/10/17 11:44] dusek |
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| The list of discussed topics follows the outline of the paper: | The list of discussed topics follows the outline of the paper: | ||
| ==== Sec. 2. Related Work ==== | ==== Sec. 2. Related Work ==== | ||
| - | |||
| **Differences from Carpuat 2009** | **Differences from Carpuat 2009** | ||
| * It is different: the decoder just gets additional features, but the decision is up to it -- Carpuat 2009 just post-edits the outputs and substitutes the most likely variant everywhere | * It is different: the decoder just gets additional features, but the decision is up to it -- Carpuat 2009 just post-edits the outputs and substitutes the most likely variant everywhere | ||
| Line 17: | Line 16: | ||
| * The authors do not state their evidence clearly. | * The authors do not state their evidence clearly. | ||
| * One sense is not the same as one translation | * One sense is not the same as one translation | ||
| - | ==== Sec. 3. Exploratory analysis ==== | ||
| + | ==== Sec. 3. Exploratory analysis ==== | ||
| **Hiero** | **Hiero** | ||
| * The idea would most probably work the same in normal phrase-based SMT, but the authors use hierarchical phrase-based translation (Hiero) | * The idea would most probably work the same in normal phrase-based SMT, but the authors use hierarchical phrase-based translation (Hiero) | ||
| Line 39: | Line 38: | ||
| ==== Sec. 4. Approach ==== | ==== Sec. 4. Approach ==== | ||
| - | |||
| The actual experiments begin only now; the used data is different. | The actual experiments begin only now; the used data is different. | ||
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| * They define 3 features that are designed to be biased towrds consistency -- or are they? | * They define 3 features that are designed to be biased towrds consistency -- or are they? | ||
| * If e.g. two variants are used 2 times each, they will have roughly the same score | * If e.g. two variants are used 2 times each, they will have roughly the same score | ||
| - | | + | * The BM25 function is a refined version of the [[http:// |
| - | | + | |
| * The exact parameter values are probably not tuned, left at a default value (and maybe they don't have much influence anyway) | * The exact parameter values are probably not tuned, left at a default value (and maybe they don't have much influence anyway) | ||
| * See NPFL103 for details on Information retrieval, it's largely black magic | * See NPFL103 for details on Information retrieval, it's largely black magic | ||
| Line 56: | Line 53: | ||
| * The resulting weights are not mentioned, but if the weight is < 0, will this favor different translation choices? | * The resulting weights are not mentioned, but if the weight is < 0, will this favor different translation choices? | ||
| - | **Differences in features** | + | **Meaning of the individual |
| * C1 indicates that a certain Hiero rule was used frequently | * C1 indicates that a certain Hiero rule was used frequently | ||
| * but rules are very similar, so we also need something less fine-grained | * but rules are very similar, so we also need something less fine-grained | ||
| - | * C2 is a target-side feature, just counts the target side tokens | + | * C2 is a target-side feature, just counts the target side tokens |
| * It may be compared to Language Model features, but is trained only on the target part of the bilingual training data. | * It may be compared to Language Model features, but is trained only on the target part of the bilingual training data. | ||
| + | * C3 counts occurrences of source-target token pairs (and uses the "most important" | ||
| + | **Requirements of the new features** | ||
| + | * They need two passes through the data | ||
| + | * You need to have document segmentation | ||
| + | * Since the frequencies are trained on the training set, you can just translate one document at a time, no need to have full sets of documents | ||