gscl2013.phrase tagset mapping for french and english treebanks and its application in machine...

Phrase Tagset Mapping for French and EnglishTreebanks and Its Application in Machine

Translation Evaluation

Aaron Li-Feng Han, Derek F. Wong, Lidia S. ChaoLiangye He, Shuo Li, and Ling Zhu

University of Macau, Department of Computer and Information ScienceAv. Padre Toms Pereira Taipa, Macau, China

[email protected], {derekfw,lidiasc}@umac.mo{wutianshui0515,leevis1987,imecholing}@gmail.com

Abstract. Many treebanks have been developed in recent years for dif-ferent languages. But these treebanks usually employ different syntactictag sets. This forms an obstacle for other researchers to take full advan-tages of them, especially when they undertake the multilingual research.To address this problem and to facilitate future research in unsupervisedinduction of syntactic structures, some researchers have developed a u-niversal POS tag set. However, the disaccord problem of the phrase tagsets remains unsolved. Trying to bridge the phrase level tag sets of mul-tilingual treebanks, this paper designs a phrase mapping between theFrench Treebank and the English Penn Treebank. Furthermore, one ofthe potential applications of this mapping work is explored in the ma-chine translation evaluation task. This novel evaluation model developedwithout using reference translations yields promising results as comparedto the state-of-the-art evaluation metrics. 1

Keywords: Natural language processing, Phrase tagset mapping, Mul-tilingual treebanks, Machine translation evaluation.

1 Introduction

To promote the development of syntactic analysis technology, treebanks for dif-ferent languages were developed during the past years, such as the English PennTreebank [1][2], the German Negra Treebank [3], the French Treebank [4], theChinese Sinica Treebank [5], etc. These treebanks use their own syntactic tagset-s, with the number of tags ranging from tens (e.g. the English Penn Treebank)to hundreds (e.g. the Chinese Sinica Treebank). These differences make it incon-venient for other researchers to take full advantages of the treebanks, especiallywhen they undertake the multilingual or cross-lingual research. To bridge thegap between these treebanks and to facilitate future research, such as the unsu-pervised induction of syntactic structures, Petrov et al. [6] developed a universal

1 The final publication is available at http://www.springerlink.com

2 A.L.F. Han, D.F. Wong, L.S. Chao, L. He, S. Li, L. Zhu

part-of-speech (POS) tagset and the POS tags mapping between multilingualtreebanks. However, the disaccord problem in the phrase level tags remains un-solved. Trying to bridge the phrase level tags of multilingual treebanks, this paperdesigns a mapping work of phrase tags between French Treebank and EnglishPeen Treeebank (I and II). Furthermore, this mapping work has been appliedinto the machine translation evaluation task (one of the potential applications)to show its advantages.

2 Designed Phrase Mapping

The designed phrase tags mapping between English and French treebanks isshown in Table 1. There are 9 universal phrasal categories covering the 14 phrasetags of Peen Treebank I, 26 phrase tags in Penn Treebank II and 14 phrase tagsin the French Treebank. The brief analysis of the phrase tags mapping will beintroduced as below.

Table 1. Phrase tagset mapping for French and English Treebanks

Universal tags English PennTreebank I [1]

English PennTreebank II [7]

French Tree-bank [4]

Tags Meaning

NP NP, WHNP NP, NAC, NX,WHNP, QP

NP Noun phrase

VP VP VP VN, VP, VPpart,VPinf

Verbal phrase

AJP ADJP ADJP, WHAD-JP

AP Adjectival phrase

AVP ADVP, WHAD-VP

ADVP, WHAVP,PRT

AdP Adverbial phrase

PP PP, WHPP PP, WHPP PP Prepositionalphrase

S S, SBAR, SBAR-Q, SINV, SQ

S, SBAR, SBAR-Q, SINV, SQ,PRN, FRAG,RRC

SENT, Ssub, Sin-t, Srel, S

(sub-)Sentence

CONJP CONJP Conjunctionphrase

COP UCP COORD Coordinatedphrase

X X X, INTJ, LST Others

The universal phrase tag NP (noun phrases) covers: the French tag NP (nounphrases); the English tags NP (noun phrases), NAC (not a constituent, used toshow the scope of certain prenominal modifiers within an NP), NX (used withincertain complex NPs to mark the head of the NP), WHNP (wh-noun phrase,

Phrase Tagset Mapping for French and English Treebanks 3

containing some wh-word, e.g. who, none of which) and QP (quantifier phrase).There are no corresponding French tags that could be aligned to English tagQP. On the other hand the corresponding quantifier phrase is usually markedas NP in French, so the English tag QP is classified into the NP category in themapping, for instance:

English: (QP (CD 200) (NNS millions))French: (NP (D 200) (N millions))

The universal phrase tag VP (verb phrase) covers: the French tags VN (verbalnucleus), VP (infinitives and nonfinite clauses, including VPpart and VPinf); theEnglish tag VP (verb phrase). In English, the infinitive phrase (to + verb) islabeled as VP, so the French VP (infinitives and nonfinite clauses) is classifiedinto the same category, for instance (English infinitive):

(VP (TO to)

(VP (VB come)))

The universal phrase tag AJP (adjective phrase) covers: the French tag AP(adjectival phrases); the English tags ADJP (adjective phrase), WHADJP (wh-adjective phrase, e.g. how hot).

The universal phrase tag AVP (adverbial phrase) contains: the French tagAdP (adverbial phrases); the English tags ADVP (adverb phrase), WHAVP (wh-adverb phrase) and PRT (particle, a category for words that should be taggedRP). The English phrase tag PRT is classified into the adverbial phrase tagbecause it is usually used to label the word behind an English verb and there isno similar phrase tag in the French Treebank to align it, for instance (EnglishPRT):

(VP (VBG rolling)

(PRT (RP up)))

The universal phrase tag PP (prepositional phrase) covers: the French tag PP(prepositional phrases); the English tags PP (prepositional phrase) and WHPP(wh-prepositional phrase).

The universal phrase tag S (sentence and sub-sentence) covers: the Frenchtags SENT (sentences), S (finite clauses, including Ssub, Sint and Srel); theEnglish tags S (simple declarative clause), SBAR (clause introduced by a sub-ordinating conjunction), SBARQ (direct question introduced by a wh-word ora wh-phrase), SINV (declarative sentence with subject-aux inversion), SQ (sub-constituent of SBARQ excluding wh-phrase), PRN (parenthetical), FRAG (frag-ment) and RRC (reduced relative clause). The English tag FRAG marks thoseportions of text that appear to be clauses but lack too many essential elementsfor the exact structure to be easily determined; the RRC label is used only incases where “there is no VP and an extra level is needed for proper attachmentof sentential modifiers” [7]. FRAG and RRC tags are at the same level as SBAR,SINV, and SQ, so they are classified uniformly into S category in the mapping,for instance:

(FRAG (, ,)

(NP (JJ for) (NN example)))

(RRC


(ADVP (RB now)

(ADJP

(NP

(QP (JJR more) (IN than) (CD 100))

(NNS years))

(JJ old))))

The English tag PRN (parenthetical) is used to tag the parenthetical contentswhich are usually like sub-sentence and there is no such tag in the French phrasetagset, so PRN is also considered as under the category of sub-sentence in theuniversal tags, for instance:

(PRN (, ,)

(S

(NP (PRP it))

(VP (VBZ seems)))

(, ,))

(PRN (-LRB- -LRB-)

(NP

(NP (CD 204) (NNS boys))

(CC and)

(NP (CD 172) (NNS girls)))

(PP (IN from)

(NP (CD 13) (NNS schools)))

(-RRB- -RRB-))

The universal phrase tag COP (coordinated phrase) covers: the French tagCOORD (coordinated phrases); the English tag UCP (unlike coordinated phrase,labeling the coordinated phrases that belong to different categories).

The universal phrase tag CONJP (conjunction phrase) covers: the English tagCONJP (conjunction phrase). The French Treebank has no homologous tags thathave the similar function to the Penn Treebank phrase tag CONJP (conjunctionphrase), for instance:

(CONJP (RB as) (RB well) (IN as))

The universal phrase tag X (other phrases or unknown) covers: the Englishtags X (unknown or uncertain), INTJ (interjection), LST (list marker).

3 Application in MT Evaluation

The conventional MT evaluation methods include the measuring of the close-ness of word sequences between the translated documents (hypothesis transla-tion) and the reference translations (usually offered by professional translators),such as the BLEU [8], NIST [9] and METEOR [10], and the measuring of theediting distance from the hypothesis translation to the reference translations,such as the TER [11]. However, the reference translations are usually expen-sive. Furthermore, the reference translations are in fact not available when the


translation task contains a large amount of data. The phrase tags mapping de-signed for French and English treebanks is employed to develop a novel evalu-ation approach for French-to-English MT translation without using the money-and-time-consuming reference translations. The evaluation is performed on theFrench (source) and English (target) documents.

3.1 Designed Methods

We assume that the translated (targeted) sentence should have a similar set ofphrase categories with the source sentence. For instance, if the source Frenchsentence has an adjective phrase and a prepositional phrase then the targetedEnglish sentence is most likely to have an adjective phrase and a preposition-al phrase, too. This design is inspired by the synonymous relation between thesource sentence and the target sentence. Admittedly, it is possible that we get twosentences that have the similar set of phrase categories but talk about differentthings. However, this evaluation approach is not designed for the general circum-stance. This evaluation approach is designed and applied under the assumptionthat the targeted sentences are indeed the translated sentences (usually by someautomatic MT systems) from the source document. Under this assumption, if thetranslated sentence retains the complete meaning of the source sentence, then itusually contains most (if not all) of the phrase categories in the source sentence.Furthermore, the Spearman correlation coefficient will be employed in the ex-periments to test this evaluation approach (to measure the correlation scores ofthis method derived by comparing with the human judgments). Following thecommon practice, human judgments are assumed as the Gold Standard [12][13].

Assume we have the following two sentences: sentence X is the source Frenchand sentence Y is the targeted English (translated by a MT system). Firstly,the two sentences are parsed by the corresponding language parsers respectively(this paper uses the Berkeley parsers [14]). Then the phrase tags of each sen-tence are extracted from the parsing results. Finally, the quality estimation ofthe translated sentence Y is conducted based on these two sequences of extractedphrase tags. See Figure 1 and Figure 2 for explanation (PhS and UniPhs rep-resent phrase sequence and the universal type of phrase sequence respectively).To make it both concise and content-rich, the phrase tag at the second level ofeach word and punctuation (counted from bottom up, namely the tokens in theparsing tree, i.e. just the immediate level above the POS tag level) is extracted.Thus, the extracted phrase sequence (PhS) has the same length as that of theparsed sentence.

3.2 Designed Formula

The precision and recall values are conventionally regarded as the importantfactors to reflect the quality of system performances in NLP literature. For in-stance, the BLEU metric employs uniformly weighted 4-gram precision scoresand METEOR employs both unigram precision and unigram recall combined


Fig. 1. The parsing results of French and English sentences.

Fig. 2. The converting of extracted phrase tags into universal categories.


with synonym and stem dictionaries. This paper designs a formula that contain-s weighted N2 gram precision, weighted N3 gram recall and N1 gram positionorder factors. Different n-gram precisions and recalls are combined respectivelywith weighted geometric means (using different weight parameters, different withBLEU). The whole formula is grouped using the weighted harmonic mean. Wedesign a factor, the position difference, due to the fact that there is indeed wordorder information within the phrase. However, the request on the sequence or-der becomes weak at phrase levels (e.g. some languages have free phrase order).According to the analysis above, the weight of position factor will be adjustedto be lower than the weights of precision and recall.

HPPR = Har(wPsN1PsDif, wPrN2Pre,wRcN3Rec)

=wPs + wPr + wRc

wPs

N1PsDif+ wPr

N2Pre + wRc

N3Rec

(1)

In the formula, Har means harmonic mean; the variables, N1PsDif , N2Preand N3Rec, represent N1 gram position difference factor, weighted N2 gramprecision and weighted N3 gram recall (all at corpus level) respectively. The pa-rameters wPs, wP r and wRc are the weights of the corresponding three variablesN1PsDif , N2Pre and N3Rec. Let’s see the introduction of the sentence-levelN1PsDif firstly.

N1PsDif = e−N1PD (2)

N1PD =1

Lengthhyp

Lengthhyp∑i=1

| PDi | (3)

| PDi |=| PsNhyp −MatchPsNsrc | (4)

The variables, N1PD, Lengthhyp and PDi, mean N1 gram position differencevalue of a sentence, hypothesis sentence length and position difference value ofthe ith tag in the hypothesis sentence respectively. The variables PsNhyp andMatchPsNsrc mean the position number of a tag in the hypothesis sentence andthe corresponding position number of the matching tag in the source sentence.If there is no successful match for the hypothesis tag, then:

| PDi |=| PsNhyp − 0 | (5)

To calculate the value of N1PsDif , we should first finish the N1 gram UniPhSalignment from the hypothesis to the source sentence (direction fixed). In thiscase, the n-gram alignment means that the candidate matches that also haveneighbor matches (neighbors are limited to the previous and following N1 tags)will be assigned higher priority. If there is no surrounding match for all potentialtags then the nearest match operation will be selected as a backup choice. Onthe other hand, if there are more than two candidates in the source sentence thatboth have the neighbor matching with the hypothesis tag, then the candidate


Fig. 3. The N1 gram alignment algorithm.

that has the nearest match with the hypothesis tag will also be selected. Thefinal result is restricted to one-to-one alignment. The alignment algorithm isshown in Figure 3. The variable lh and ls represent the length of hypothesissentence and source sentence respectively. The tagx and tagsy mean the phrasetag in hypothesis and source sentence respectively. In this paper, the N1gram isperformed using bigram (N1=2). After the alignment, an example for the N1PDcalculation of a sentence is shown in Figure 4. Then the value of the N1 gramPosition difference factor N1PsDif at the corpus level is the arithmetical meanof each sentence level score N1PsDifi.

N1PsDif =1

n

n∑i=1

N1PsDifi (6)

The values of the corpus-level weighted N2 gram precision N2Pre and weight-ed N3 gram recall N3Rec are calculated as follows:

N2Pre = exp(

N2∑n=1

wnlogPn) (7)

N3Rec = exp(

N3∑n=1

wnlogRn) (8)


Fig. 4. The N1PD calculation example.

The variables Pn and Rn mean the n-gram precision and n-gram recall thatare calculated as follows:

Pn =#matched ngram chunks

#ngram chunks of hypothesis corpus(9)

Rn =#matched ngram chunks

#ngram chunks of source corpus(10)

The variable #matched ngram chunks represents the number of matchedn-gram chunks between the hypothesis and source corpus (sum of sentence-levelmatches). Each n-gram chunk could be used only once, and an example is thebigram chunks matching in Figure 5.

Fig. 5. Bigram chunks matching example.

To calculate n-gram precision and recall values, we should know that if theyare designed at the sentence level then some of the sentence-level bigram or tri-gram precision and recall values will be 0 due to the fact that some hypothesissentences result in no bigram or trigram chunk matching with the source sen-tences (it is meaningless when the variable of logarithmic function is less thanor equal to 0). According to the analysis above, after the sentence-level n-gramchunks matching, we calculate the n-gram precision Pn and n-gram recall Rn

values at the corpus level as shown in the formula (this design is similar to BLEU;BLEU calculates the corpus level n-gram precisions). Thus the geometric meanof weighted N2 gram precision N2Pre and weighted N3 gram recall N3Rec willalso be at the corpus level.


4 Experiments

4.1 Evaluating method for evaluation

Spearman rank correlation coefficient (rs) is conventionally used as the evalu-ating method for the MT evaluation metrics [12][13]. The evaluation result thathas a higher correlation score, namely higher correlation with human judgments,will be regarded as having a better quality.

rsXY = 1−6∑n

i=1 d2i

n(n2 − 1)(11)

We assume two sequence−→X = {x1, x2, ..., xn} and

−→Y = {y1, y2, ..., yn}. First-

ly we get the corresponding rank sequences−→X = {x1, x2, ..., xn} and

−→Y =

{y1, y2, ..., yn}; then di is the difference-value between the coordinate variables,i.e. xi − yi.

4.2 Experiments and Results

The corpora used in the experiments are from the international workshop ofstatistical machine translation (WMT) which has been held annually by theACL’s special interest group of machine translation (SIGMT2). To avoid theoverfitting problem, the WMT 2011 corpora are used as the development set (fortuning the weights of factors in the formula to make the evaluation results closeto the human judgments). Then the WMT 2012 corpora are used as the testingset with the formula that has the same parameters tuned in the developmentstage.

There are 18 and 15 systems respectively in WMT 2011 and WMT 2012 pro-ducing the French-to-English translation documents, each document containing3003 sentences. Each year, there are hundreds of human annotators to evaluatethe MT system outputs, and the human judgments task usually costs hundred-s of hours of labor. The human judgments are used to validate the automaticmetrics. The system-level Spearman correlation coefficient of the different eval-uation results will be calculated as compared to the human judgments. Thestate-of-the-art evaluation metrics BLEU (measuring the closeness between thehypothesis and reference translations) and TER (measuring the editing distance)are selected for the comparison with the designed model HPPR.

The values of N2 and N3 are both selected as 3 due to the fact that the 4-gramchunk match usually results in 0 score. The tuned factor weights in the formulaare shown in Table 2. The experiment results on the development and testingcorpora are shown in Table 3 and Table 4 respectively (“Use Reference?” mean-s whether this metric uses reference translations). The experiment results onthe development and testing corpora show that HPPR without using referencetranslations has yielded promising correlation scores (0.63 and 0.59 respective-ly) with human judgments even though lower than the reference-aware metrics

2 http://www.sigmt.org/


Table 2. Tuned factor weights

Items Weight parameters Ratio

N2Pre w1 : w2 : w3 8:1:1

N3Rec w1 : w2 : w3 1:1:8

HPPR wPs : wPr : wRc 1:8:1

Table 3. Development results on WMT 2011 corpus

Metrics Use Reference? Spearman rs

BLEU Yes 0.85

TER Yes 0.77

HPPR No 0.63

BLEU and TER. Results also specify that there is still potential to improve theperformances of all the three metrics, even though that the correlation scoreswhich are higher than 0.5 are already considered as strong correlation [15] asshown in Table 5.

5 Discussion

To facilitate future research in multilingual or cross-lingual literature, this paperdesigns a phrase tags mapping between the French Treebank and the EnglishPenn Treebank using 9 commonly used phrase categories. One of the potentialapplications in the MT evaluation task is explored in the experiment section.This novel evaluation model could spare the reference translation corpora thatare conventionally used in the MT evaluation tasks. This evaluation approachis favorable especially for the circumstance where there are many translationsystems outputting a lot of translated documents and the reference translationsare very expensive or not easily available in time.

There are still some limitations in this work to be addressed in the future.First, the designed universal phrase categories may not be able to cover allthe phrase tags of other language treebanks, so this tagset could be expandedwhen necessary. Second, in the application of MT evaluation of the mappingwork, the designed formula contains the n-gram factors of position difference,

Table 4. Testing results on WMT 2012 corpus

Metrics Use Reference? Spearman rs

BLEU Yes 0.81

TER Yes 0.82

HPPR No 0.59


Table 5. Correlation scores analysis

Correlation Negative Positive

None -0.09 to 0 0 to 0.09

Small -0.3 to -0.1 0.1 to 0.3

Medium -0.5 to -0.3 0.3 to 0.5

Strong -1.0 to -0.5 0.5 to 1.0

precision and recall, which may not be sufficient or suitable for some of the otherlanguage pairs, so different measuring factors should be added or switched whenfacing new challenges.

The designed source codes of this paper can be freely downloadedfor research purpose, open source online. The source codes of HPPR mea-suring algorithm and French-English phrase tagset mapping are available here“https://github.com/aaronlifenghan/aaron-project-hppr”. 3

Acknowledgments. The authors wish to thank the anonymous reviewers formany helpful comments. The authors are grateful to the Science and TechnologyDevelopment Fund of Macau and the Research Committee of the Universityof Macau for the funding support for our research, under the reference No.017/2009/A and RG060/09-10S/CS/FST. The authors also wish to thank Mr.Yunsheng Xie who is a Master Candidate of Arts in English Studies from theDepartment of English in the Faculty of Social Sciences and Humanities at theUniversity of Macau for his kind help in improving the language quality.

References

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4. Abeille A., L. Clement, and F. Toussenel. 2003. Building a Treebank for French.Building and Using Parsed Corpora, In Abeille (Abeille, 2003), chapter 10 in ANNEAbeille, Treebanks. Kluwer Academic Publishers.

3 The final publication is available athttp://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-642-40721-5


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gscl2013.phrase tagset mapping for french and english treebanks and its application in machine...

Business

statistical

facilitate

phrase tagset

phrase tags

english penn

universal

tuned factor

machine translation