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A Finite-State Approach to Machine Translation

Srinivas Bangalore Giuseppe Riccardi

AT&T Labs-Research

{srini,dsp3}@research.att.com

NAACL 2001, Pittsburgh, June 6, 2001

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Overview

• Motivation• Stochastic Finite State Machines• Learning Machine Translation Models• Case study

– MT for Human-Machine Spoken Dialog

• Experiments and Results

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Motivation• Finite State Transducers (FST)

– Unified formalism to represent symbolic transductions– Calculus for combining FSTs

• Learnability– Automatically train transductions from (parallel) corpora

• Speech-to-Speech Machine Translation chain– Combining speech and language constraints

• Previous Approaches to FST-MT: Knight and Al-Onaizan 1998, Vilar et.al. 1999, Ney 2000, Nederhof 2001

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Stochastic Machine Translation

• Noisy-channel paradigm (IBM)

• Stochastic Finite State Transducer Model

)()|(maxargˆTTS

WT WPWWPW

T

Problem Reordering Lexical )|(maxargˆ

Problem Choice Lexical ),(maxargˆ

)ˆ(TT

WRWT

TSW

ST

WPW

WWPW

STT

T

MT SW TW

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Pairing and Aligning• Input: Source-Target language sentence pairs• Sentence Alignment (Alshawi, Bangalore and Douglas,

1998)

• Output: – Alignment between source-target substrings– Dependency trees for source and target strings

Spanish : ajá quiero usar mi tarjeta de crédito

English : yeah I wanna use my credit card

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Learning SFST from Bi-language

• Bi-language: each token consists of a source language word with its target language word.

• Ordering of tokens: source language order or target language order

• ajá quiero usar mi tarjeta de crédito• yeah I wanna use my credit card

• (ajá,yeah) (I) (quiero,wanna) (usar,use) (mi,my) (tarjeta,card) (de,

(crédito,credit)

SW

TW

)W,F(W TS

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Learning Bilingual Phrases• Effective translation of text chunks (e.g.

collocations)• Learn bilingual phrases

– Joint entropy minimization on bi-language corpus

• Phrase-segmented bi-language corpus– (ajá,yeah) (quiero,I wanna) (usar,use) (mi,my) (tarjeta de crédito, card credit)

• Local Reordering of phrases tarjeta de crédito

card credit credit card

Lexical Choice

LocalReordering

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Local Reordering• Locally reordered phrase=min(S TLM)

S is the “sausage” FSM

TLM is an n-gram target language model– “credit card” is the more likely phrase

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Lexical Choice Model• Train variable N-gram language model (Riccardi

1995) on bi-language corpus.– simple N-gram models– phrase-based N-gram models

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Lexical Reordering• Output of the lexical choice transducer:

sequence of target language phrases.– I’d this to my home phone to charge like

• Words in phrases are in target language word order.

• However, phrases need to be reordered in target language word order.

• Reordered: – I'd like to charge this to my home phone

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Lexical Reordering Models• Alignment of JEnglish-English sentence

pairs.JEnglish: I’d this to my home phone to charge

like

English: I’d like to charge this to my home phone

I’d

this

like

charge

home

my phone

to to

I’d

this

like

charge

home

my phone

toto

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Lexical Reordering Models (contd)

• Dependency tree represented as a bracketed string with reordering instructions.

….. :[ :[ to:to :] :-1 charge:charge :] :+1 like:like

• Train variable N-gram language model on the bracketed corpus

• Output of FST: strings with reordering instructions.[ [ to ] -1 charge ] +1 like

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Lexical Reordering Models (contd)

• Instructions are composed with “interpreter” FST to form target language sentence.

• Finite-state approximation:– Well-formedness of brackets checked for a

bounded depth with a weighted FSM– Weights are estimated from the bracketed

training corpus

• Alternate approach: Approximation of a CFG (Nederhof 2001)

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ASR-based Speech Translation

Alignment

Lexical Choice

Phrase Learning

Lexical Reordering

)W,F(W TS

)W,(W TS

a

b

Acoustic Model Training

)Speech,(WS

Lexicon FSM

)(Lexicon

A

L

c Speech Recognizerc b a L A

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MT Evaluation

• Application-independent evaluation– Translation Accuracy – Based on string alignment

• Application-driven evaluation– “How May I Help You?”– Spoken dialog for call routing (14 call

types)– Classification based on salient phrase

detection

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Examples

• Yes I like to make this long distance call area code x x x x x x x x x x

• Yeah I need the area code for rockmart georgia

• Yeah I’m wondering if you could place this call for me I can’t seem to dial it it don’t seem to want to go through for me

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Evaluation Metric• Evaluation metric for MT is a complex issue.• String edit distance between reference string

and result string (length in words: R)– Insertions (I)– Deletions (D)– Moves = pairs of Deletions and Insertions (M)– Remaining Insertions (I') and Deletions (D')

• Translation Accuracy = 1 – (M + I' + D' + S) / R

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Experiments and Evaluation

• Data Collection: – The customer side of operator-customer

conversations transcribed– Transcriptions were then manually

translated into Japanese• Training Set: 12226 English-Japanese

sentence pairs• Test Set: 3253 sentences.• Different translation models

– Word n-gram and Phrase n-gram

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Translation Accuracy(English-Japanese on Text)

• After reordering is better than before reordering• Phrase n-grams better than simple n-grams

00.10.20.30.40.50.60.70.8

1gra

m

2gra

m

3gra

m

1phr

gram

2phr

gram

3phr

gram

before reordering

after reordering

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Translation Accuracy(English-Japanese on Text and Speech)

• Speech recognition accuracy 60%• Drop of about 10% between text translation and

speech translation

00.10.20.30.40.50.60.70.8

1gra

m

2gra

m

3gra

m

1phr

gram

2phr

gram

3phr

gram

Text

Speech

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Call-Classification Performance

• False Rejection Rate: Probability of rejecting a call, given that the call-type is one of the 14 call-types.

• Probability Correct: Probability of correctly classifying a call, given that the call is not rejected.

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MT evaluation on HMIHYClassification accuracy on original English text and Japanese-English translated text.

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DEMO

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Conclusion• Stochastic Finite State based approach is

viable and effective for limited domain MT.

• Finite-state model chain allows integration of speech and language constraints.

• Multilingual speech application enabled by MThttp://www.research.att.com/~srini/Projects/Anuvaad/home.html

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Translation using stochastic FSTs• Sequence of finite-state transductions

Japanese: 私は これを 私の 家の 電話に チャージ したいのですJEnglish: I’d this to my home phone to charge

like

English: I’d like to charge this to my home phone

I’d

this

like

charge

home

my phone

to to

I’d

this

like

charge

home

my phone

toto

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Lexical Choice Accuracy(Japanese-to-English Text Translation)

VNST order Recall R

Precision P

F-Measure2*P*R/(P+R)

Unigram 31.1 92.2 46.5Bigram 65.4 89.9 75.8Trigram 63.2 91.5 74.7Phrase Unigram

41.9 92.9 57.8

Phrase Bigram 66.7 89.3 76.4Phrase Trigram

65.3 89.9 75.7

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Translation Accuracy(English-Japanese on text)

VNST order Accuracy before Reordering

Accuracy after Reordering

Unigram 23.8 32.2

Bigram 56.9 69.4

Trigram 56.4 69.1

Phrase Unigram 44.0 46.8

Phrase Bigram 60.4 69.8

Phrase Trigram 58.9 66.7

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Translation AccuracyEnglish-Japanese on speech

(one-best ASR output)

VNST order Accuracy before Reordering

Accuracy after Reordering

Unigram 21.4 21.7

Bigram 48.9 55.7

Trigram 49.0 56.8

Phrase Unigram 39.3 39.6

Phrase Bigram 51.3 56.5

Phrase Trigram 50.9 56.9

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Biblio-J. Berstel “Transductions and Context Free Languages” Teubner Studienbüchner-G. Riccardi, R. Pieraccini and E. Bocchieri, "Stochastic Automata for Language Modeling", Computer Speech and Language, 10, pp. 265-293, 1996.-Fernando C. N. Pereira and Michael Riley. Speech Recognition by Composition of Weighted Finite Automata . Finite-State Language Processing. MIT Press, Cambridge, Massachusetts. 1997-S. Bangalore and G. Riccardi, "Stochastic Finite-State Models for Spoken Language Machine Translation", Workshop on Embedded Machine Translation Systems, NAACL, pp. 52-59, Seattle, May 2000.

More references on http://www.research.att.com/info/dsp3

 

 

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