Grammatical Agreement in SMT

Institut für Anthropomatik1 24.06.13 Simon Hummel – Lehrstuhl Prof. Waibel
Grammatical Agreement in SMT
Seminar Sprach-zu-Sprach-Übersetzung
SS 2013

Inflection
– Modification of a word
– signals grammatical variants (tense, gender, case, …)
– e.g. walk vs. Walked
Agreement
– Inflection for related words in a sentence has to agree
– e.g. das Haus vs. die Haus
Some languages are weakly inflected (e.g. English)
Some are highly inflected (e.g. German, Arabic, …)
Inflection and Agreement

Local Agreement Errors
Ref:
the-carF
goF
with-speed
Hypo:
the-carF
goM
with-speed
Long-distance Agreement Errors
Ref: celle qui parle , c’est ma femme
oneF
who speak , is my wifeF
Hypo: celui qui parle est ma femme
oneM
who speak is my spouseF
Agreement Errors

Approaches for SMT
Morphological Generation
– Create raw stems and modify with predicted inflection
Agreement Constraints
– Use SCFG of target and add constraints to it
Class-based Agreement Model
– Use morphological word classes “Noun+Def+Sg+Fem”

Morphological Generation: Idea
“Generating Complex Morphology for Machine Translation” (Minkov
and Toutanova, 2007)
Convert MT output to stem sequence
Predict an inflection for every stem
Reflect meaning and comply with agreement rules

Morphological Generation: Lexicons
Morphology analysis and generation
Operations:
– Stemming
– Inflection
– Morphological analysis
Create manually
Create automatically from data
Here: assumed as given

Morphological Generation: Inflection Prediction
Maximum Entropy Markov model (2nd
order)
Features:
– Monolingual
– Bilingual
– Lexical
– Morphological
– Syntactic
p(̄y∣̄x)=∏t=1
n
p(yt∣ yt−1 , yt−2 , xt ) , yt ∈It

Morphological Generation: Evaluation
English-Russian and English-Arabic
Technical (software manual) domain
Input: Aligned sentence pairs of reference translations (no output of MT
System) → reduce noise
Accuracy (%) results

Morphological Generation: Conclusion
Needed resources:
– Large corpus of aligned sentence pairs
– Lexicons (source and target) with the three operations
+ Better accuracy than simple LM (even with small training data)
+ Easy to add to existing MT system
- Expensive creation of lexicons

Constraints: Idea
“Agreement Constraints for Statistical Machine Translation into
German” (Williams and Koehn, 2011)
String-to-tree model
Synchronous grammar for target language
Adding learned constraints and probabilities
Evaluation of constraints during decoding

Constraints: Feature Structure
Feature structure
Unification

Constraints: Grammar
Synchronous grammar learned from parallel corpus
Extended by constraints at target-side
Sample rule/constraint:
NP-SB → the X1
cat | die AP1
Katze

Constraints: Training
Propagation rules to
capture NP/PP agreements:
Applied bottom-up

Constraints: Decoding
Model:
Every element of rule/constraint has a feature structure
Constraint evaluation: Each hypothesis stores set of feature structures
corresponding to its root rule element
Recombination of hypotheses is possible
̂t=arg max
t
p(t∣s)
p(t∣s)=
1
Z
∑
i=1
n
λi hi (s ,t)

Constraints: Evaluation
English-German
Europarl and News Commentary
Parsing: BitPar; Alignment: GIZA++; SCFG rules: Moses toolkit
Treebank for target
Grammar: ~140 m rules
BLEU scores and p-values for three test sets

Constraints: Conclusion
Needed resources:
– Parallel corpus
– Heuristics for constraint extraction
+ Improvement in translation accuracy
- Improvement is quite small

Class-Based: Idea
1. Segmentation
2. Tagging
3. Scoring
“A Class-Based Agreement Model for Generating Accurately Inflected
Translations” (Green and DeNero, 2012)
During Decoding
Target-Side
Three Steps:

Class-Based: Segmentation
Train conditional random field
Features:
Centered 5-character window
During decoding
Not as preprocessing step
Labels:
I: Continuation (Inside)
O: Outside (whitespace)
B: Beginning
F: Non-native chars

Class-Based: Tagging
Train CRF on full sentences with gold classes
Features:
– Current and previous words, affixes, etc.
Labels:
– Morphological classes
→ Gender, number, person, definiteness
– e.g. 89 classes for Arabic
Example:
'the car'
Tagged: “Noun+Def+Sg+Fem”

Class-Based: Scoring
Scoring of word sequences not comparable across hypotheses
→ Scoring class sequences with generative model
Simple bigram LM over gold class sequences (add-1 smoothed)
τ' =arg max
τ
p(τ∣̂s)
q(e)= p(τ')=∏i=1
I
p(τ'i∣τ'i−1)

Class-Based: Evaluation
English-Arabic
Training data: variety of sources (e.g. web)
Development and Test: NIST sets (Newswire and mixed genre
[broadcast news, newsgroups, weblog])
Phrase-based decoder
BLEU score for newswire sets
BLEU score for mixed genre sets

Class-Based: Conclusion
Needed resources:
– Treebank for target (existing for many languages)
– Large target corpus
+ Improves translation quality
+ Easy to integrate in existing MT system
- Increases decoding time
- Not very good for mixed genres

Green, S. and DeNero, J. (2012). “A Class-Based Agreement Model for
Generating Accurately Inflected Translations”. In: ACL.
Williams, P. and Koehn, P. (2011). “Agreement Constraints for Statistical
Machine Translation into German”. In: Sixth Workshop on Statistical
Machine Translation
Minkov, E. and Toutanova, K. (2007) “Generating Complex Morphology
for Machine Translation”. In: ACL.
References

Grammatical Agreement in SMT

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