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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 11:43]
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@@ Line 33: / Line 33: @@
 **The choice and filtering of "cases"**
   * The "cases" in Table 1 are selected according to the //possibility// of different translations (i.e. each case has at least two translations of the source seen in the training data; the translation counts are from the test data, so it is OK that e.g. "Korea" translates as "Korea" all the time)
-  * Table 1 is unfiltered -- only some of the "cases" are then considered relevant
+  * Table 1 is unfiltered -- only some of the "cases" are then considered relevant:
-    * 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, 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")
+==== 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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