A Hierarchical Phrase-Based Model for Statistical Machine

TranslationAuthor: David Chiang

Presented by Achim RuoppFormulas/illustrations/numbers extracted from referenced

papers

Outline

• Phrase Order in Phrase-based Statistical MT

• Using synchronous CFGs to solve the issue

• Integrating the idea into an SMT system• Results• Conclusions• Future work• My Thoughts/Questions

Phrase Order in Phrase-based Statistical MT

• Example from [Chiang2005]:

Phrase Order in Phrase-based Statistical MT

• Translation of the example with a phrase-based SMT system (Pharao, [Koehn2004])[Aozhou] [shi] [yu] [Bei Han] [you] [bangjiao]1

[de shaoshu guojia zhiyi]

[Australia] [is] [dipl. rels.]1 [with] [North Korea] [is] [one of the few countries]

• Uses learned phrase translations• Accomplishes local phrase-reordering• Fails on overall reordering of phrases• Not only applicable to Chinese, but also

Japanese (SOV order), German (scrambling)

Idea: Rules for Subphrases

• Motivation“phrases are good for learning reorderings of words, we

can use them to learn reorderings of phrases as well”• Rules with “placeholders” for subphrases

– <yu [1] you [2], have [2] with [1]>• Learned automatically from bitext without

syntactical annotation• Formally syntax-based but not linguistically

syntax-based• “the result sometimes resembles a syntactician’s

grammar but often does not”

Synchronous CFGs

• Developed in the 60’s for programming-language compilation [Aho1969]

• Separate tutorial by Chiang describing them [Chiang2005b]

• In NLP synchronous CFGs have been used for – Machine translation – Semantic interpretation

Synchronous CFGs

• Like CFGs, but production have two right hand sides– Source side– Target side– Related through linked non-terminal symbols

• E.g. VP → <V[1] NP[2],NP[2] V[1]>• One-to-one correspondence• Non-terminal of type X is always linked to same

type• Productions applied in parallel to both sides to

linked non-terminals

Synchronous CFGs

Synchronous CFGs• Limitations

– No Chomsky normal form• Has implications for complexity of decoder

– Only limited closure under composition– Sister-reordering only

Model

• Using the log-linear model [Och2002]– Presented by Bill last week

Model – Rule FeaturesiXXw

ii

),(),( • P(γ|α) and P(α|γ)• Lexical weights Pw(γ|α) and Pw(α|γ)

– Estimation how we words in α translate to words in γ

• Phrase penalty exp(1)– Allows model to learn longer/shorter derivations

• Exception: glue rule weights– w(S → <X[1],X[1] >) =1– w(S → <S[1]X[2],S[1]X[2]>) = exp(-λg)– Λg controls model’s preference for hierarchical

phrases over serial phrase combination

Model – Additional Features

)exp()()()(,,

eeprwDw wpDjir

lmlm

• Separated out from rule weights– Notational convenience– Conceptually cleaner (necessary for polynominal-time

decoding)

• Derivation D– Set of triples <r,i,j>: apply grammar rule r for rewriting

a non-terminal in span f(D) from i to j– Ambiguous

Training

• Training is starting from a symetrical, word-aligned corpus

• Adopted from [Och2004] and [Koehn2003] – How to get from a one-directional alignment to

a symetrical alignment– How to find initial phrase pairs

• alternative would be Marcu & Wong 2002 that Ping presented [Marcu2002]

Training

Training

• Scheme leads unfortunately – To a large number of rules– With false ambiguity

• Grammar is filtered to– Balance grammar size and performance– Five filter criteria e.g.

• produce only two non-terminals• Initial phrase length limited to 10

Decoding

• Our good old friend - the CKY parser• Enhanced with

– Beam search– Postprocessor to map French derivations to English

derivations

)(maxarg

)(..Dwe

fDftDs

Results

• Baseline– Pharao [Koehn2003], [Koehn2004]– Minimum error rate training on BLEU measure

• Hierarchical model– 2.2 Million rules after filtering down from 24 Million– 7.5% relative improvement

• Additional constituent feature– Additional feature favoring syntactic parses– Trained on 250k sentences Penn Chinese Treebank– Improved accuracy only in development set

Learned Feature Weights

• Word = word penalty• Phr = phrase penalty (pp)

• λg penalizes glue rules much less than λpp does regular rules– i.e. “This suggests that the model will prefer serial combination

of phrases, unless some other factor supports the use of hierarchical phrases ”

Conclusions

• Hierarchical phrase pairs that can be learned data without syntactically annotation

• Hierarchical phrase pairs improve translation accuracy significantly

• Added syntactic information (constituent feature did not provide statistically significant gain

Future Work

• Move to more syntactically motivated grammar

• Reducing grammar size to allow more aggressive training settings

My Thoughts/Questions

• Really interesting approach to bring “syntactic” information into SMT

• Example sentence was not translated correctly– Missing words are problematic

• Can phrase reordering be also learned by lexicalized phrase reordering models [Och2004]?

• Why did constituent feature only improve accuracy in development set, but not in test set?

• Does data sparseness influence the learned feature weights?

• What syntactical features are already built into Pharao?

References• [Aho1969] Aho, A. V. and J. D. Ullman. 1969. Syntax directed translations and the

pushdown assembler. Journal of Computer and System Sciences, 3:37–56.• [Chiang2005]: Chiang, David. 2005. A Hierarchical Phrase-Based Model for

Statistical Machine Translation. In Proceedings of ACL 2005, pages 263–270. • [Chiang2005b]:

http://www.umiacs.umd.edu/~resnik/ling645_fa2005/notes/synchcfg.pdf• [Koehn2003]: Koehn, Philipp. 2003. Noun Phrase Translation. Ph.D.• thesis, University of Southern California.• [Koehn2004]: Koehn, Phillip. 2004. Pharaoh: a beam search decoder for phrase-

based statistical machine translation models. In Proceedings of the Sixth Conference of the Association for Machine Translation in the Americas, pages 115–124.

• [Marcu2002]: Marcu, Daniel and William Wong. 2002. A phrasebased, joint probability model for statistical machine translation. In Proceedings of the 2002 Conference on

• Empirical Methods in Natural Language Processing (EMNLP), pages 133–139.• [Och 2002]: Och, Franz Josef and Hermann Ney. 2002. Discriminative training and

maximum entropy models for statistical machine translation. In Proceedings of the 40th Annual Meeting of the ACL, pages 295–302.

• [Och2004]: Och, Franz Josef, Hermann Ney. 2004. The alignment template approach to statistical machine translation. Computational Linguistics, 30:417–449.