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--- courses:rg:2012:encouraging-consistent-translation [2012/10/16 14:44]
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+++ courses:rg:2012:encouraging-consistent-translation [2012/10/16 15:15]
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 [[http://aclweb.org/anthology-new/N/N12/N12-1046.pdf|PDF]]
-===== Outline =====
+===== Outline -- discussion =====
+The list of discussed topics follows the outline of the paper:
+==== Sec. 2. Related Work ====
+**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
+    * Using Carpuat 2009's approach directly in the decoder would influence neighboring words through LM, so even using this in the decoder and not as post-editing leads to a different outcome
+**Human translators and one sense per discourse**
+  * This suggests that modelling human translators is the same as modelling one sense per discourse -- this is suspicious
+    * The authors do not state their evidence clearly.
+    * One sense is not the same as one translation
+==== Sec. 3. Exploratory analysis ====
+**Hiero**
+  * The idea would most probably work the same in normal phrase-based SMT, but the authors use hierarchical phrase-based translation (Hiero)
+    * Hiero is summarized in Fig. 1: the phrases may contain non-terminals (''X'', ''X1'' etc.), which leads to a probabilistic CFG and bottom-up parsing
+  * The authors chose the ''cdec'' implementation of Hiero (which is implemented in several systems: Moses, cdec, Joshua etc.)
+    * The choice was probably arbitrary, other systems would yield similar results
+**Forced decoding**
+  * This means that the decoder is given source //and// target sentence and has to provide the rules/phrases that map from the source to the target
+    * The decoder might be unable to find the appropriate rules (for unseen words)
+    * It is a different decoder mode, for which it must be adjusted
+    * Forced decoding is much more informative for Hiero translations than for "plain" phrase-based ones, since there are many different parse trees that yield the same target string, and not as much phrases
+**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:
+    * 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)
+    * Cases where //only one translation variant prevails// are //discarded// (this is the case of "Korea")

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