Active Learning for Statistical Phrase-based Machine Translation

Gholamreza HaffariJoint work with: Maxim Roy, Anoop Sarkar

Simon Fraser UniversityNAACL talk, Boulder, June 2009

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The Problem

• Statistical Machine Translation (SMT)

• MFE is a standard log-linear model and is composed of two main components:– Phrase tables

– Language model

• Good phrase tables are typically learned from large bilingual (F,E)-text

– What if we don’t have large bilingual text?

MFELanguage F Language E

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A Solution

• Suppose we are given a large monolingual text in the source language F

• Pay a human expert and ask him/her to translate these sentences into the target language E– This way, we will have a bigger bilingual text

• But our budget is limited !– We cannot afford to translate all monolingual

sentences

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A Better Solution

• Choose a subset of monolingual sentences for which:

if we had the translation,

the SMT performance would increase the most

• Only ask the human expert for the translation of these highly informative sentences

• This is the goal of Active Learning– Workshop on Active Learning for NLP

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Active Learning for SMT

Train

MFE

Bilingual text

FF EE

Monolingual text

DecodeTranslated text

FF EE

Translate by human

FF EE FF

SelectInformative Sentences

SelectInformative Sentences

Re-

For more details, see the paper

For more details, see the paper

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Outline

• General idea of active learning (AL) for statistical machine translation (SMT)

• Sentence Selection Strategies

– Similarity, Decoder’s Confidence– Hierarchical Adaptive Sampling– Sentence merit based on the translation units

• Experiments

– The simulated AL setting– The real AL setting

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Intuitive Underpinnings for Sent. Selection

• Sentences for which the model is not confident about their translations– Hopefully high confident translations are good ones

• Sentences similar to bilingual text are easy to translate by the model– Select the dissimilar ones to the bilingual text

• Cluster monolingual sentences– Choose some representative sentences for each

cluster

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Sentence Selection strategies

• Baseline: Randomly choose sentences from the pool of monolingual sentences

• Previous Work: Decoder’s confidence for the translations (Kato & Barnard, 2007)

• Our proposed methods:– Similarity to the bilingual training data – Reverse model– Hierarchical Adaptive Sampling (HAS)– Utility of the translation units

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Sentence Selection strategies

• Baseline: Randomly choose sentences from the pool of monolingual sentences

• Previous Work: Decoder’s confidence for the translations (Kato & Barnard, 2007)

• Our proposed methods:– Similarity to the bilingual training data Reverse modelHierarchical Adaptive Sampling (HAS)Utility of the translation units

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Reverse Model

Comparing– the original sentence, and– the final sentence

Tells us something about the value of the sentence

I will let you know about the issue later

Je vais vous faire plus tard sur la question

I will later on the question

MEF

Rev: MFE

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Hierarchical Adaptive Sampling

U0: Monolingual sentences

U1 U2

U2,2U2,1

Average Decoder’s Score Sort sentences wrt similarity to the Bilingual text

Sample sentences from these two nodes

MFE

Bilingual text

FF EE

(Dasgupta & Hsu, 2008)

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Utility of the Translation Units

Phrases are the basic units of translations in phrase-based SMT

I will let you know about the issue later

Monolingual Text6

6

18

3

Bilingual Text5

6

12

3

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The more frequent a phrase is in the monolingual text, the more important it is

The more frequent a phrase is in the bilingual text, the less important it is

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Generative Models for Phrases

Monolingual Text Bilingual Text

66183

Count

.25

.25

.05

.33

.12

Probability

561237

Count Probability

.21

.22

.05

.09

.14

.29

m b

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Averaged Probability Ratio Score

• For a monolingual sentence S– Consider , the bag of its phrases

– Score: Normalized probability ratio P(S| m)/P(S| b)

– We will refer to it as Geom-Phrase

• Dividing the phrase probabilities captures our intuition about the utility of the translation units

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Sentence Segmentation

• How to prepare the bag of phrases for a sentence S?

– For the bilingual text, we have the segmentation from the training phase of the SMT model

– For the monolingual text, we run the SMT model to produce the top-n translations and the corresponding segmentations

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Extensions of the Score

• Instead of using phrases, we may use n-grams

• We may alternatively use the following score

– We will refer to it as Arithmetic Average

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Sentence Selection strategies (Recap)

• Baseline: Randomly choose sentences from the pool of monolingual sentences

• Previous Work: Decoder’s confidence for the translations (Kato & Barnard, 2007)

• Our proposed methods:Similarity to the bilingual training data Reverse modelHierarchical Adaptive Sampling (HAS)Utility of the translation units

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Outline

• General idea of active learning (AL) for statistical machine translation (SMT)

• Sentence Selection Strategies

– Similarity, Decoder’s Confidence– Hierarchical Adaptive Sampling– Sentence merit based on the translation units

• Experiments

– The simulated AL setting– The real AL setting

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Experimental Setup

• Dataset size:

• We select 200 (or 100) sentences from the monolingual sentence set for 25 (or 5) iterations

• We use Portage from NRC as the underlying SMT system (Ueffing et al, 2007)

Bilingual text Monolingual Text test

Bangla-English 11K 20K 1K

Fr,Gr,Sp-English 5K 20K 2K

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The Simulated AL Setting

Geometric Phrase

Random

Decoder’s Confidence

Bet

ter

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The Real AL Setting

• Our human translator is different from the text author

– The methods are good at adapting to the new writing style

Geometric Phrase

Random

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Domain Adaptation• Now suppose the both test and monolingual text are

out-of-domain with respect to the bilingual text

– The ‘Decoder’s Confidence’ does a good job

– The ‘Geom 1-gram’ outperforms other methods since it quickly expands the lexicon set in an effective manner

Geom 1-gram

Random Random

Decoder’s Conf

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Analysis

• The coverage of the bilingual text is important but is not the only factor– Notice the Geom 1-gram and Geom-phrase methods

Cov

erag

e

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Analysis

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Conclusions

• We presented different sentence selection methods for SMT in an AL setting

• Using knowledge about the internal architecture of the SMT system is crucial

• Yet, we are after better sentence selection strategies– See our upcoming paper in ACL09

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Merci

Thank You

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Domain Adaptation

• Selecting sentences based on: – The ‘Confidence’ does a good job– The ‘1-gram’ outperforms other methods since it quickly

expands the lexicon set in an effective manner

Method Bleu% per% wer%

Geom 1-gram 14.92 34.83 46.06

Confidence 14.74 35.02 46.11

Random 14.11 35.28 46.47

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The Simulated AL Setting

Language Pair Geometric Average

Bleu% per% wer%

Random (Baseline)

Bleu% per% wer%

French-English 22.49 27.99 38.45 21.97 28.31 38.80

German-English 17.54 31.51 44.28 17.25 31.63 44.41

Spanish-English 23.03 28.86 39.17 23.00 28.97 39.21

• Using other measure other than BLEU– wer: word error rate– per: position independent word error rate