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Table of Contents
Encouraging Consistent Translation Choices
Ferhan Ture, Douglas W. Oard, and Philip Resnik
NAACL 2012
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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
butold hostages
!=completely new hostages
(we hope this didn't actually happen)