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courses:rg:2012:encouraging-consistent-translation [2012/10/16 15:15]
dusek 		courses:rg:2012:encouraging-consistent-translation [2012/10/17 11:43]
dusek
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     * 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)
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       * Beware, this notion of grouping is not well-defined, does not create equivalence classes: "old hostages" = "new hostages" = "completely new hostages" but "old hostages" != "completely new hostages" (we hope this didn't actually happen)       * Beware, this notion of grouping is not well-defined, does not create equivalence classes: "old hostages" = "new hostages" = "completely new hostages" but "old hostages" != "completely new hostages" (we hope this didn't actually happen)
     * Cases where //only one translation variant prevails// are //discarded// (this is the case of "Korea")     * Cases where //only one translation variant prevails// are //discarded// (this is the case of "Korea")
 +
 +==== 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://en.wikipedia.org/wiki/TF-IDF|TF-IDF]] score
 +  * 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 (''f'') and their weights (''lambda''): 
 +    * ''score(H) = exp( sum( lambda_i * f_i(H)) )''
 +  * The feature weights are trained on a heldout data set using [[http://acl.ldc.upenn.edu/acl2003/main/pdfs/Och.pdf|MERT]] (or, here: [[http://en.wikipedia.org/wiki/Margin_Infused_Relaxed_Algorithm|MIRA]])
 +  * 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" ones; in terms of TF-IDF)
 +    * 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" term pair for each rule, again)
 +
 +**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

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