Cohesive Constraints in a Beam Search Phrase-based Decoder Nguyen Bach, Stephan Vogel Carnegie Mellon University
Colin Cherry Microsoft Research
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Overview • Apply cohesive constraints during decoding process to consider the source dependency structures • Introduce extensions of the cohesive constraints. • Analyze the impact of cohesive constraints across language pairs with different reordering models • Applied to English-Spanish , English-Iraqi and ChineseEnglish translation tasks – Significant improvements on English-Spanish – Stable improvements on other pairs 2
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Two Questions • How to determine the largest subtree that needs to be completed before the translation process can move elsewhere in the tree? – Interruption Check: use left and right most tokens of the previous translated source phrase and climb up the tree
• If a violation happens, how to constrain the decoder to penalize cohesion violated translation hypothesis? – Interruption Check : Binary event 8
Exhaustive Interruption Check • Interruption Check only penalizes the cohesion violation 1 time • Should penalties persist as long as violations remain unresolved? • Exhaustive Interruption Check keeps punishing a cohesion violation until it is fixed.
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Cohesion Violation Penalties • Interruption Check and Exhaustive Interruption Check: binary event • Are some violations worse than others? • Penalize a cohesion violation by the number of untranslated words under the largest subtree – Interruption Check -> Interruption Count – Exhaustive Interruption Check -> Exhaustive Interruption Count 11
Rich Interruption Constraints begins /VBZ
begins
OBJ
SBJ election
election /NN
tomorrow
NMOD the
presidential
states
the /DT
NMOD
tomorrow /NN NMOD
presidential /JJ PMOD
of
the
united
of /IN
the /DT
states /NNS NMOD
NMOD
united /VBN
• Penalize a cohesion violation by 4 constraints – – – –
Cohesive constraints obtained improvements over the standard phrase-based decoder. 15
How does the performance of the dependency parser affect cohesive constraints?
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BLEU
The Role of Dependency Parser on English-Spanish 33.2 33 32.8 32.6 32.4 32.2 32 31.8 31.6 31.4 31.2
M1 M2
• Train 2 MALT dependency parser models: M1 with 10% of treebank and M2 with all treebank. • Performance on CoNLL-07 dependency test set – M1: 19.41% – M2: 86.21%
• Apply to MT – M2 is better than M1
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• Are the improvements subsumed by a strong reordering model and system scale? • What if we translate from X->English?
Conclusions & Future Work • Conclusions – Cohesive constraints are helpful – The effectiveness was shown when using with a strong reordering model – Obtained improvements with 3 language pairs and also covered a wide range of training corpus sizes, ranging from 500K up to 11M sentence pairs
• Future work – A source side dependency reordering model: Learning reordering events of the phrases based on source subtree movements – A hierarchical source side dependency reordering model: extend Galley&Manning (2008).
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