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courses:rg:2012:encouraging-consistent-translation [2012/10/16 15:13] dusek |
courses:rg:2012:encouraging-consistent-translation [2012/10/17 11:45] 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 33: | Line 32: | ||
**The choice and filtering of " | **The choice and filtering of " | ||
* The " | * The " | ||
- | * Table 1 is unfiltered -- only some of the " | + | * Table 1 is unfiltered -- only some of the " |
- | * Cases that are too similar (less than 1/2 characters differ) are joined together | + | * Cases that are //too similar// (less than 1/2 characters differ) are //joined together// |
- | * Beware, this notion of grouping is not well-defined, | + | * Beware, this notion of grouping is not well-defined, |
+ | * Cases where //only one translation variant prevails// are // | ||
+ | |||
+ | ==== Sec. 4. Approach ==== | ||
+ | The actual experiments begin only now; the used data is different. | ||
+ | |||
+ | **Choice of features** | ||
+ | * 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 | ||
+ | * 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) | ||
+ | * See NPFL103 for details on Information retrieval, it's largely black magic | ||
+ | |||
+ | **Feature weights** | ||
+ | * The usual model in MT is scoring the hypotheses according to the feature values ('' | ||
+ | * '' | ||
+ | * The feature weights are trained on a heldout data set using [[http:// | ||
+ | * The resulting weights are not mentioned, but if the weight is < 0, will this favor different translation choices? | ||
+ | |||
+ | **Meaning of the individual features** | ||
+ | * C1 indicates that a certain Hiero rule was used frequently | ||
+ | * 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 (only the "most important" | ||
+ | * It may be compared to Language Model features, but is trained only on the target part of the bilingual tuning 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 tuning set (see Sec. 5), you can just translate one document at a time, no need to have full sets of documents | ||
+ | |||
+ | ==== Sec. 5. Evaluation and Discussion ==== |