Improving Domain-Specific Word Alignment with
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1. This indicates that separately modeling the general words and domain specific words can effectively improve the word alignment in a specific domain 4 Computer Assisted Translation System A direct application of the word alignment result to the GTMS is to get translations for sub sequences in the input sentence using the pre translated examples For each sentence there are many sub sequences GTMS tries to find translation examples that match the longest sub sequences so as to cover as much of the input sentence as possible without overlapping Figure 3 shows a sentence translated on the sub sentential level The three panels display the input sentence the example translations and the translation suggestion provided by the system respectively The input sentence is segmented to three parts For each part the GTMS finds one example to get a translation fragment according to the word alignment result By combining the three translation fragments the GTMS produces a correct translation suggestion RRUA CT AHL Without the word alignment information the conventional TMS cannot find translations for the input sentence because there are no examples closely matched with it Thus word alignment information can improve the translation accuracy of the GTMS which in turn reduces editing time of the translators and improves translation efficiency fre Translation Studio The system is supposed to have the CT scanner2. 1 Asa result the system was damaged GR KK RIR Te 2 The equipment is supposed to have a footswitch Rake KAAA MRR 3 This document describes the operating procedures for the CT scanner BM Mt CL EMBOLI ARTE 27 UE EAT T IE o RERUN A CHRD Figure 3 A Snapshot of the Translation System 5 Conclusion This paper proposes an approach to improve domain specific word alignment through alignment adaptation Our contribution is that our approach improves domain specific word alignment by adapting word alignment information from the general domain to the specific domain Our approach achieves it by training two alignment models with a large scale general bilingual corpus and a small scale domain specific corpus Moreover with the training data two translation dictionaries are built to select or modify the word alignment links and further improve the alignment results Experimental results indicate that our approach achieves a precision of 83 63 and a recall of 76 73 for word alignment on a user manual of a medical system resulting in a relative error rate reduction of 21 96 Furthermore the alignment results are applied to a computer assisted translation system to improve translation efficiency Our future work includes two aspects First we will seek other adaptation methods to further improve the domain specific word alignment results Second we will use the alignment adaptation results in other applications Refere
3. Improving Domain Specific Word Alignment for Computer Assisted Translation WU Hua WANG Haifeng Toshiba China Research and Development Center 5 F Tower W2 Oriental Plaza No 1 East Chang An Ave Dong Cheng District Beijing China 100738 wuhua wanghaifeng rdc toshiba com cn Abstract This paper proposes an approach to improve word alignment in a specific domain in which only a small scale domain specific corpus is available by adapting the word alignment information in the general domain to the specific domain This approach first trains two statistical word alignment models with the large scale corpus in the general domain and the small scale corpus in the specific domain respectively and then improves the domain specific word alignment with these two models Experimental results show a significant improvement in terms of both alignment precision and recall And the alignment results are applied in a computer assisted translation system to improve human translation efficiency 1 Introduction Bilingual word alignment is first introduced as an intermediate result in statistical machine translation SMT Brown et al 1993 In previous alignment methods some researchers modeled the alignments with different statistical models Wu 1997 Och and Ney 2000 Cherry and Lin 2003 Some researchers use similarity and association measures to build alignment links Ahrenberg et al 1998 Tufis and Barbu 2002 However All
4. another word alignment model using the small scale bilingual corpus in the specific domain 3 We build two translation dictionaries according to the alignment results in 1 and 2 respectively 4 For each sentence pair in the specific domain we use the two models to get different word alignment results and improve the results according to the translation dictionaries Experimental results show that our method improves domain specific word alignment in terms of both precision and recall achieving a 21 96 relative error rate reduction The acquired alignment results are used in a generalized translation memory system GTMS a kind of computer assisted translation systems Simard and Langlais 2001 This kind of system facilitates the re use of existing translation pairs to translate documents When translating a new sentence the system tries to provide the pre translated examples matched with the input and recommends a translation to the human translator and then the translator edits the suggestion to get a final translation The conventional TMS can only recommend translation examples on the sentential level while GTMS can work on both sentential and sub sentential levels by using word alignment results These GTMS are usually employed to translate various documents such as user manuals computer operation guides and mechanical operation manuals 2 Word Alignment Adaptation 2 1 Bi directional Word Alignment In statistical trans
5. es 458 465 Dekai Wu 1997 Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora Computational Linguistics 23 3 377 403
6. f each translation pair because Dunning 1993 proved that log likelihood ratio performed very well on small scale data Thus we get the translation dictionary D by keeping those entries whose log likelihood ratio scores are greater than a threshold 2 3 Word Alignment Adaptation Algorithm Based on the bi directional word alignment we define SJ as SI SGOSF and UG as UG PG U PF SI The word alignment links in the set SZ are very reliable Thus we directly accept them as correct links and add them into the final alignment set WA Input Alignment set SI and UG 1 For alignment links in SZ we directly add them into the final alignment set WA 2 For each English word i in the UG we first find its different alignment links and then do the following a If there are alignment links found in dictionary D add the link with the largest probability to WA b Otherwise if there are alignment links found in dictionary D add the link with the largest log likelihood ratio score to WA c If both a and b fail but three links select the same target words for the English word i we add this link into WA d Otherwise if there are two different links for this word one target is a single word and the other target is a multi word unit and the words in the multi word unit have no link in WA add this multi word alignment link to WA Output Updated alignment set WA Figure 1 Word Alignment Adaptatio
7. lation models Brown et al 1993 only one to one and more to one word alignment links can be found Thus some multi word units cannot be correctly aligned In order to deal with this problem we perform translation in two directions English to Chinese and Chinese to English as described in Och and Ney 2000 The GIZA toolkit is used to perform statistical word alignment For the general domain we use SG and SG to represent the alignment sets obtained with English as the source language and Chinese as the target language or vice versa For alignment links in both sets we use 7 for English words and j for Chinese words SG 4 A ajjaj 0 SG 4 4 a a 29 Where a k i j is the position of the source word aligned to the target word in position k The set A k i j indicates the words aligned to the same source word k For example if a Chinese word in position j is connect to an English word in position i then a i And if a Chinese word in position j is connect to English words in position i and k then A i Based on the above two alignment sets we obtain their intersection set union set and subtraction set Intersection SG SG SG Union PG SG U SG Subtraction MG PG SG For the specific domain we use SF and SF to represent the word alignment sets in the two directions The symbols SF PF and MF represents the intersection set union set and the subt
8. n Algorithm For each source word in the set UG there are two to four different alignment links We first use translation dictionaries to select one link among them We first examine the dictionary D and then D to see whether there is at least an alignment link of this word included in these two dictionaries If it is successful we add the link with the largest probability or the largest log likelihood ratio score to the final set WA Otherwise we use two heuristic tules to select word alignment links The detailed algorithm is described in Figure 1 For information related to x ray safety refer to section 7 aan ef 3 iS j a A nye een Tietterit Ai A are See is eo se 2 GR IIA RS Dm eS Figure 2 Alignment Example Figure 2 shows an alignment result obtained with the word alignment adaptation algorithm For example for the English word x ray we have two different links in UG One is x ray X and the other is x ray X 4 2 And the single Chinese words 4f and 2 amp have no alignment links in the set WA According to the rule d we select the link x ray X H4 3 Evaluation We compare our method with three other methods The first method Gen Spec directly combines the corpus in the general domain and in the specific domain as training data The second method Gen only uses the corpus in the general domain as training data The third method Spec only uses
9. nces Lars Ahrenberg Magnus Merkel and Mikael Andersson 1998 A Simple Hybrid Aligner for Generating Lexical Correspondences in Parallel Tests In Proc of the 36 Annual Meeting of the Association for Computational Linguistics and the 17 International Conference on Computational Linguistics pages 29 35 Peter F Brown Stephen A Della Pietra Vincent J Della Pietra and Robert L Mercer 1993 The Mathematics of Statistical Machine Translation Parameter Estimation Computational Linguistics 19 2 263 311 Colin Cherry and Dekang Lin 2003 A Probability Model to Improve Word Alignment In Proc of the 41 Annual Meeting of the Association for Computational Linguistics pages 88 95 Ted Dunning 1993 Accurate Methods for the Statistics of Surprise and Coincidence Computational Linguistics 19 1 61 74 Sue J Ker Jason S Chang 1997 A Class based Approach to Word Alignment Computational Linguistics 23 2 313 343 Franz Josef Och and Hermann Ney 2000 Improved Statistical Alignment Models In Proc of the 38 Annual Meeting of the Association for Computational Linguistics pages 440 447 Michel Simard and Philippe Langlais 2001 Sub sentential Exploitation of Translation Memories In Proc of MT Summit VIII pages 335 339 Dan Tufis and Ana Maria Barbu 2002 Lexical Token Alignment Experiments Results and Application In Proc of the Third International Conference on Language Resources and Evaluation pag
10. of these methods require a large scale bilingual corpus for training When the large scale bilingual corpus is not available some researchers use existing dictionaries to improve word alignment Ker and Chang 1997 However few works address the problem of domain specific word alignment when neither the large scale domain specific bilingual corpus nor the domain specific translation dictionary is available This paper addresses the problem of word alignment in a specific domain where only a small domain specific corpus is available In the domain specific corpus there are two kinds of words Some are general words which are also frequently used in the general domain Others are domain specific words which only occur in the specific domain In general it is not quite hard to obtain a large scale general bilingual corpus while the available domain specific bilingual corpus is usually quite small Thus we use the bilingual corpus in the general domain to improve word alignments for general words and the corpus in the specific domain for domain specific words In other words we will adapt the word alignment information in the general domain to the specific domain In this paper we perform word alignment adaptation from the general domain to a specific domain in this study a user manual for a medical system with four steps 1 We train a word alignment model using the large scale bilingual corpus in the general domain 2 We train
11. raction set respectively 2 2 When we train the statistical word alignment model with a large scale bilingual corpus in the general domain we can get two word alignment results for the training data By taking the intersection of the two word alignment results we build a new alignment set The alignment links in this intersection set are extended by iteratively adding Translation Dictionary Acquisition It is located at http www isi edu och GIZA html In this paper the union operation does not remove the replicated elements For example if set one includes two elements 1 2 and set two includes two elements 1 3 then the union of these two sets becomes 1 1 2 3 word alignment links into it as described in Och and Ney 2000 Based on the extended alignment links we build an English to Chinese translation dictionary D with translation probabilities In order to filter some noise caused by the error alignment links we only retain those translation pairs whose translation probabilities are above a threshold 6 or co occurring frequencies are above a threshold 6 When we train the IBM statistical word alignment model with a limited bilingual corpus in the specific domain we build another translation dictionary D with the same method as for the dictionary D But we adopt a different filtering strategy for the translation dictionary D We use log likelihood ratio to estimate the association strength o
12. the domain specific corpus as training data With these training data the three methods can get their own translation dictionaries However each of them can only get one translation dictionary Thus only one of the two steps a and b in Figure 1 can be applied to these methods The difference between these three methods and our method is that for each word our method has four candidate alignment links while the other three methods only has two candidate alignment links Thus the steps c and d in Figure 1 should not be applied to these three methods 3 1 Training and Testing Data We have a sentence aligned English Chinese bilingual corpus in the general domain which includes 320 000 bilingual sentence pairs and a sentence aligned English Chinese bilingual corpus in the specific domain a medical system manual which includes 546 bilingual sentence pairs From this domain specific corpus we randomly select 180 pairs as testing data The remained 366 pairs are used as domain specific training data The Chinese sentences in both the training set and the testing set are automatically segmented into words In order to exclude the effect of the segmentation errors on our alignment results we correct the segmentation errors in our testing set The alignments in the testing set are manually annotated which includes 1 478 alignment links 3 2 Overall Performance We use evaluation metrics similar to those in Och and Ney 2000 Ho
13. wever we do not classify alignment links into sure links and possible links We consider each alignment as a sure link If we use Sg to represent the alignments identified by the proposed methods and S to denote the reference alignments the methods to calculate the precision recall and f measure are shown in Equation 1 2 and 3 According to the definition of the alignment error rate AER in Och and Ney 2000 AER can be calculated with Equation 4 Thus the higher the f measure is the lower the alignment error rate is Thus we will only give precision recall and AER values in the experimental results precision Bansel ae ael Se 1 ISg Sc i reca Sc 2 2 Sg ASe fmeasure 3 Sq l 1Sc AER 1 again 1 fmeasure 4 ISg Se Method Precision Recall AER Ours 0 8363 0 7673 0 1997 Gen Spec 0 8276 0 6758 0 2559 Gen 0 8668 0 6428 0 2618 Spec 0 8178 0 4769 0 3974 Table 1 Word Alignment Adaptation Results We get the alignment results shown in Table 1 by setting the translation probability threshold to 6 0 1 the co occurring frequency threshold to 6 5 and log likelihood ratio score to 6 50 From the results it can be seen that our approach performs the best among others achieving much higher recall and comparable precision It also achieves a 21 96 relative error rate reduction compared to the method Gent Spec
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