saab mansour and hermann ney human language technology and pattern recognition computer science...

Saab Mansour and Hermann NeyHuman Language Technology and Pattern Recognition

Computer Science DepartmentRWTH Aachen University, Aachen, Germany

NAACL-HLT 2013

Introduction

Domain-adaption 是利用某一個domain 內的 data 來提高 TM model 在test domain 的 performance.

TM adaption: 建立一個 general domain phrase table, 利用 in-domain data 修改 phrase probabilities.

Introduction

使用的 corpus 為 IWSLT(International Workshop On Spoken Language Translation) TED(Technology Entertainment Design) tasks 內的 Arabic-to-English 和 German-to-English.

Phrase Training

用 Forced alignment (FA) 來執行 phrase segmentation, alignment training 和 probability estimation.

用 SMT 來做 phrase training, 對一個training set y, 產生 heuristic-based phrase table Py

0, 經過 FA training(sentence 會被 segmentation 和alignment), 根據 output 來重新估計 phrase的機率值 , 產生新的 phrase table p’.

Adaption

對一個 training set y’, 產生 initial phrase table Py’

0, 對 yin(in-domain training data) 做 FA training, bias the probability to in-domain, procedure表示為 X-FA-IN.

用 leaving-one-out 來避免 over-fitting.

Experimental Setup

Training Corpora: Arabic-to-English:

In-domain: 90K TED sentences Other-domain: 7.9M sentences of United

Nation data German-to-English:

In-domain: 130K TED sentences Other-domain: 2.1M sentences from news-

commentary and europarl corpora

Experimental Setup – Translation System

Baseline system: built using SMT toolkit Jane 2.0

Measures: BLEU, TER. Arabic-English results are case

sensitive German-English results are case insensitive

Results

Heuristics: IN,OD,ALL standard phrase extraction using word-

alignment training and heuristic phrase extraction over the word alignment.

FA standard: IN-FA,OD-FA,ALL-FA standard FA phrase training where the same

training set is used for initial phrase table generation as well as the FA procedure.

FA adaptation: OD-FA0-IN, ALL-FA-IN FA based adaptation phrase training, where

the initial table is generated from some general data and the FA training is performed on the IN data to achieve adaptation.

Results - measures

BLEU: (Bilingual Evaluation Understudy) Candidate: the the the the the the the. Reference 1: The cat is on the mat. Reference 2: There is a cat on the mat.

Standard unigram precision: 7/7 Modified unigram precision: 2/7

Results - measures

TER: translation edit rate REF: SAUDI ARABIA denied THIS WEEK

information published in the AMERICAN new york times

HYP: THIS WEEK THE SAUDIS denied information published in the new york times

TER = 4/13 4 (1 Shift, 2 Substitutions, and 1 Insertion)

Mixture Modeling

Linear interpolation of IN and OD, IN and OD-FA0-IN, weight is uniform(0.5).

Conclusion

提出 phrase training procedure for adaptation using FA method.

對 Arabic-to-English 和 German-to-English TED lectures translation tasks, 都提高了 performance, BLEU 在development set 提高 0.6%, TER 分別在 test, eval sets 減少了 0.8% 和 0.6%

最後用 mixture model 來比較 , 結果顯示adapted OD table performance 較unadpated 的 OD table 好 .