Inductive Detection of Language Features via Clustering ...jhclark/pubs/clark_ssst2.pdfInductive...

Inductive Detection of Language Features via Clustering Minimal Pairs

Toward Feature-Rich Grammars inMachine Translation

Jonathan ClarkRobert Frederking

Lori Levin

Language Technologies InstituteCarnegie Mellon University

Pittsburgh, PA

ACL SSST-2 - June 20, 2008

• Overview of Feature Detection

• Example Application: Feature-Rich Grammars

• The Process of Feature Detection

• Results

• Conclusions

Outline

Feature Detection((num sg)…)The dog eatsEl perro come

((num dl)…)The dogs eatLos perros comen

((num pl)…)The dogs eatLos perros comen

context: There are two dogs

Does this language distinguish singular, dual, or plural agents? If so, how?

Feature Detection

Feature Candidate Lexical Items

(num sg) come, el, perro

(num dl, pl) comen, los, perros

FeatureDetection

((num sg)…)The dog eatsEl perro come

Does this language distinguish singular, dual, or plural agents? If so, how?

✓Overview of Feature Detection

• Example Application: Feature-Rich Grammars

• Results

• Conclusions

Outline

Problem: Long-Distance Interactions

“A student named Irshad who was throwing flour in the water for the fish . . . ”

←12 words→

English → Urdu

Example Application: Feature-Rich Grammars

←12 words→

NPA VP

←12 words→

NPA VP

(num sg) (num sg)

←12 words→

NPA VP

(NPA num) = (VP num)

(num sg) (num sg)

←12 words→

NPA VP

(num sg) (num sg)

(num pl)

←12 words→

✓Example Application: Feature-Rich Grammars

• Results

• Conclusions

Outline

Inductive Feature Detection

Minimal Pair Clustering

Feature Value Clustering

Morpheme-Feature Pairing

Elicitation Corpus

Morpheme-Feature Pairs

Elicitation Corpus Entrycontext: Maria bakes cookies regularly or habitually. srcsent: Maria bakes cookies .

Elicitation Corpus Entrycontext: Maria bakes cookies regularly or habitually.srcsent: Maria bakes cookies .tgtsent: Maria hornea galletas . aligned: ((1,1),(2,2),(3,3),(4,4))

Elicitation Corpus Entrycontext: Maria bakes cookies regularly or habitually.srcsent: Maria bakes cookies .tgtsent: Maria hornea galletas . aligned: ((1,1),(2,2),(3,3),(4,4))fstruct: [f1]( [f2](actor ((gender f)(anim human)(num sg))) [f3](undergoer ((person 3) (num dl))) (tense pres))

Distributed in this year’s NIST Urdu MT Evaluation via the LDC’s LCTL Language Pack

Elicitation Corpus

The dogs see the catLos perros ven el gato((actor (num pl)(gender n)) (undergoer (num sg)(gender n)) (tense pres))

The dog sees the catEl perro ve el gato((actor (num sg)(gender n)) (undergoer (num sg)(gender n)) (tense pres))

Wildcard Feature Structure:((actor (num *)(gender n)) (undergoer (num sg)(gender n)) (tense pres))

The dogs see the catLos perros ven el gato((actor (num pl)(gender n)) (undergoer (num sg)(gender n)) (tense pres))

The dog sees the catEl perro ve el gato((actor (num sg)(gender n)) (undergoer (num sg)(gender n)) (tense pres))

Wildcard Feature Structure:((actor (num *)(gender n)) (undergoer (num sg)(gender n)) (tense pres))

Elicitation Corpus

Feature Value Clusterings1: The dog sees the cat El perro ve el gato ((actor (num sg)…))

s2:The dogs see the catLos perros ven el gato((actor (num dl)…))

{ (s1, s2, NEQ) }

pl s3: The dogs see the catLos perros ven el gato((actor (num pl)…))

{ (s1, s3, NEQ) }

{ (s1, s3, EQ) }

s1: The dog sees the cat El perro ve el gato ((actor (num sg)…))

{ (s1, s2, NEQ) }

pl s3: The dogs see the catLos perros ven el gato((actor (num pl)…))

{ (s1, s3, NEQ) }

{ (s1, s3, EQ) }

{ (s1, s2, NEQ) }

s3: The dogs see the cat Los perros ven el gato ((actor (num dl)…))

{ (s1, s3, EQ) }

{ (s1, s2, NEQ), (s1, s3, NEQ) }

Elicitation Corpus

{ (s1, s3, EQ) }

{ (s1, s2, NEQ), (s1, s3, NEQ) }

{ (s1, s3, EQ) }

{ (s1, s2, NEQ), (s1, s3, NEQ) }

{ el, perro, ve } { los, perros, ven }

✓The Process of Feature Detection

• Results

• Conclusions

Outline

Evaluation

Elicitation Corpus

Experiment• Analyzed LCTL Urdu-English Elicitation Corpus

(~3000 sentences)

• Evaluated by Urdu native speaker knowledgeable in linguistics

Judgement Morpheme-Feature Pairings

ExampleOutput

Correct 68 “hai” → (tense pres)

Ambiguous 29 “raha” → (tense pres)

Incorrect 109 “nahin” → (tense pres)

TOTAL 206

Experiment

• Found 68 Correct Morpheme-Feature Pairs

• = 53 Word Types

• In an Urdu corpus of 17M tokens and 200k types, the 53 types selected by feature detection cover:

• 0.02% of Types (Vocabulary)

• 38.6% of Tokens

AnnotatedRemaining

Tokens inBlind Test Set

✓The Process of Feature Detection

✓Results

• Conclusions

Outline

Elicitation Corpus Future Work

Morphological Segmentation

Presented MethodFeature Candidate Lexical Items

(num sg) come, el, perro

(num dl, pl) comen, los, perros

Future WorkFeature Candidate Morphemes

(num sg) VB+Ø, el, N+o

(num dl, pl) VB+n, los, N+os

Filtering

Feature Constraint Learning

Filtering

Feature Constraint Learning

Structural MT Integration

Other Applications

• Factored MT

• Data Selection via active learning for synchronous grammar induction

• Aid for linguistics field work

Conclusion

• We now have

• Feature-annotations for lexical items that convey grammatical meanings

• Significant coverage

• Structural MT systems stand to benefit by incorporating this morphosyntactic information

Inductive Detection of Language Features via Clustering Minimal Pairs:

Toward Feature-Rich Grammars in Machine Translation

Jonathan ClarkRobert Frederking

Lori Levin

Language Technologies InstituteCarnegie Mellon University

Pittsburgh, PA

Questions?

Inductive Detection of Language Features via Clustering ...jhclark/pubs/clark_ssst2.pdfInductive...

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