Chinese and Spanish have different morphology structures, which poses a big challenge for translating between this pair of languages. In this paper, we analyze several strategies to better generalize from the Chinese non-morphology-based language to the Spanish rich morphologybased language. Strategies use a first-step of Spanish morphology-based simplifications and a second-step of fullform generation. The latter can be done using a translation system or classification methods. Finally, both steps are combined either by concatenation in cascade or integration using a factored-based style. Ongoing experiments (based on the United Nations corpus) and their results are described.Postprint (published version

Ruiz Costa-Jussà, Marta

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Proceedings of the ACL 2015 Fourth Workshop on Hybrid Approaches to Translation (HyTra), pages 56–60,Beijing, China, July 31, 2015. c2015 Association for Computational LinguisticsOngoing Study for Enhancing Chinese-Spanish Translationwith Morphology StrategiesMarta R. Costa-jussa`1Centro de Investigacio´n en Computacio´n, Instituto Polite´cnico Nacional, Mexico1marta@nlp.cic.ipn.mxAbstractChinese and Spanish have different mor-phology structures, which poses a bigchallenge for translating between this pairof languages. In this paper, we analyzeseveral strategies to better generalize fromthe Chinese non-morphology-based lan-guage to the Spanish rich morphology-based language. Strategies use a first-stepof Spanish morphology-based simplifica-tions and a second-step of fullform gen-eration. The latter can be done using atranslation system or classification meth-ods. Finally, both steps are combined ei-ther by concatenation in cascade or inte-gration using a factored-based style. On-going experiments (based on the UnitedNations corpus) and their results are de-scribed.1 IntroductionThe structure of Chinese and Spanish differs atmost linguistic levels, e.g. morphology, syntaxand semantics. In this paper, we are focusingon reducing the gap between both languages atthe level of morphology. On the one hand, Chi-nese is an isolating language, which means hav-ing a low morpheme per word ratio. On the otherhand, Spanish is a fusional language, which meanshaving a tendency to overlay many morphemes.The challenge when translating between Chineseand Spanish is bigger in the direction from Chi-nese to Spanish, given that the same Chinese wordcan generate multiple Spanish words. For exam-ple, the Chinese word fa`n (in transcribed Pinyin)can be translated by comer, como, comı´, comere´1which correspond to several tense flexions of thesame verb and also by comes, comiste, comera´s2,1to eat, I eat, I ate, I will eat2you eat, you ate, you will eatall of which also correspond to several person flex-ions of the same verb. This poses a challengein Statistical Machine Translation (SMT) becausetranslations are learnt by co-ocurrence of words inboth languages. When a word has multiple transla-tions, it generates sparsity in the translation model.In this study, we experiment with differentstrategies to add morphology knowledge in a stan-dard phrase-based SMT system (Koehn et al.,2003) for the Chinese-to-Spanish translation di-rection. However, the presented techniques couldbe used for other pairs involving isolating and fu-sional languages. The rest of the paper is or-ganized as follows. Section 2 reports a briefoverview of the related work both in using mor-phology knowledge in SMT and in translatingfrom Chinese-to-Spanish. Section 3 explains thetheoretical framework of phrase-based SMT at ahigh level and the details of each strategy to intro-duce morphology in the mentioned system. Sec-tion 4 describes the experiments and first resultsobtained for each theoretical strategy presented.Finally, Section 5 concludes this ongoing researchand outlines the future research directions.2 Related WorkThere are numerous studies which deal with mor-phology in the field of SMT. Without aiming atcompleteness, we cite works that:• Preprocess the data to make the structure ofboth languages more similar by means of en-riching (Avramidis and Koehn, 2008; Ueffingand Ney, 2003) or segmentation techniques inagglutinative (S.Virpioja et al., 2007) or fu-sional languages (Costa-jussa`, 2015a)• Modify models (Koehn and Hoang, 2007)• Post-process the data (Toutanova et al., 2008;Bojar and Tamchyna, 2011; Formiga et al.,2013).56The research work in this area is being very ac-tive, e.g. PhD proposals using strategies based ondeep learning (Gutierrez-Vasques, 2015).Previous works on the Chinese-Spanish lan-guage pair focus on compiling corpus and us-ing pivot stategies (Costa-jussa` et al., 2012) andon building a Rule-Based Machine Translation(RBMT) system (Costa-jussa` and Centelles, InPress 2015). A high-level description of the state-of-the-art of the translation on this language pairis detailed in (Costa-jussa`, 2015b).Our work mixes several strategies but basicallyit goes in the direction of (Formiga et al., 2013)that focuses on solving the challenge of mor-phology as a post-processing classification prob-lem. The idea is to translate from Chinese to amorphology-based simplified Spanish and, then,re-generate the morphology by means of classi-fication algorithms. The competitive advantagefrom this strategy is the rise of algorithms basedon deep learning techniques that can achieve highsuccess rates, e.g. (Collobert et al., 2011).3 Theoretical FrameworkThe phrase-based SMT system (Koehn et al.,2003) is trained on a parallel corpus at the level ofsentences. It learns co-ocurrences and each tokenin the training set is considered as a different oneno matter if it is morphologically related. There-fore, in the extreme case where the word canto3is in the training set and the inflection of the sameverb cante´4 is not, the latter is going to be consid-ered an out-of-vocabulary word.Strategy 1. One well-known strategy to face thischallenge is to add a part-of-speech (POS) lan-guage model which evaluates the probability of thePOS-sequences instead of the word sequences.Strategy 2. This second strategy consists on do-ing a cascade of systems: first, translate fromsource to morphology-based simplified target; sec-ond, translate from this simplified target to full-form target as shown in Figure 3.One straightforward simplification in morphol-ogy can be adopting lemmas as shown in Table 1.Strategy 3. This third strategy is based onfactored-based translation (Koehn and Hoang,2007), which uses linguistic information of words,3I sing4I sangFigure 1: Illustration of the cascade strategy.e.g. lemmas and POS. The idea is that the trans-lation model based on words is used if the trans-lation of the word is available, and if not, lemmasand POS are used in combination with a model togenerate the final word. Figure 3 shows a typicalrepresentation of this factored strategy.Figure 2: Illustration of the factored strategy.Strategy 4. This fourth strategy is based on pre-vious work like (Formiga et al., 2013), where theidea is to do a first translation from source toa morphology-based simplified target and then,use a classifier to go from this simplified targetto the fullform target. See the schema of thisclassification-based strategy in 3.The main challenges in the last strategy are:1. Explore different simplifications of the targetlanguage in order to use the one with a highertrade-off between the highest oracle and thelowest classification complexity.2. Explore several classification algorithms.57Eslemmas decidir examinar el cuestio´n en el perı´odo de sesio´n el tema titular “ cuestio´n relativo a el derecho humano “EsNlemmas Decide examinar la cuestio´n en el perı´odo de sesio´n el tema titulado “ cuestio´n relativas a los derecho humanos ” .EsVlemmas decidir examinar la cuestio´n en el perı´odo de sesiones el tema titulado “ Cuestiones relativas a los derechos humanos ” .EsDlemmas Decide examinar el cuestio´n en el perı´odo de sesiones el tema titulado “ Cuestiones relativas a el derechos humanos ” .EsPlemmas Decide examinar la cuestio´n en el perı´odo de sesiones el tema titulado “ Cuestiones relativas a los derechos humanos ” .EsAlemmas Decide examinar la cuestio´n en el perı´odo de sesiones el tema titular “ Cuestiones relativo a los derechos humano ” .Estags VMIP3S0 VMN0000 DA0MS0 NCFS000 SPS00 DA0MS0 NCMS000 SPS00 NCFP000 DA0MS0 NCMS000 AQ0MS0 FpNCFP000 AQ0FP0 SPS00 DA0MS0 NCMP000 AQ0MP0 Fp FpEsnum decidir[VMIP3N0] examinar[VMN0000] el[DA0MN0] cuestio´n[NCFN000] en[SPS00] el[DA0MN0] perı´odo[NCMN000] de[SPS00]sesio´n[NCFN000] el[DA0MN0] tema[NCMN000] titular[AQ0MN0] “[Fp] cuestio´n[NCFN000] relativo[AQ0FN0] a[SPS00] el[DA0MN0]derecho[NCMN000] humano[AQ0MN0] “[Fp] .[Fp]Esgen decidir[VMIP3S0] examinar[VMN0000] el[DA0GS0] cuestio´n[NCGS000] en[SPS00] el[DA0GS0] perı´odo[NCGS000] de[SPS00]sesio´n[NCGS000] el[DA0GS0] tema[NCGS000] titular[AQ0GS0] “[Fp] cuestio´n[NCGS000] relativo[AQ0GS0] a[SPS00] el[DA0GS0]derecho[NCGS000] humano[AQ0GS0] “[Fp] .[Fp]Esnumgen decidir[VMIP3N0] examinar[VMN0000] el[DA0GN0] cuestio´n[NCGN000] en[SPS00] el[DA0GN0] perı´odo[NCGN000] de[SPS00]sesio´n[NCGN000] el[DA0GN0] tema[NCGN000] titular[AQ0GN0] “[Fp] cuestio´n[NCGN000] relativo[AQ0GN00] a[SPS00] el[DA0GN0]derecho[NCGN000] humano[AQ0GN0] “[Fp] .[Fp]Es Decide examinar la cuestio´n en el perı´odo de sesiones el tema titulado “ Cuestiones relativas a los derechos humanos ” .Table 1: Example of Spanish simplification into lemmas and different variations.Figure 3: Illustration of the classification-basedstrategy.In this paper, we study the first challenge of ex-ploring different simplifications. However, we donot face the classification challenge, which is leftto further work. It would be interesting to use deeplearning knowledge which is leading to large im-provements in natural language processing (Col-lobert et al., 2011).4 Ongoing ExperimentsIn this section we show experiments and resultswith the four strategies proposed in the previoussection.As discussed in the literature, there are notmany parallel corpora available for Chinese-Spanish (Costa-jussa` et al., 2012). In this work,we use the data set from the United Nations(Rafalovitch and Dale, 2009). The training cor-pus contains about 60,000 sentences (and around2 million words) and the development and test cor-pus contain 1,000 sentences each one. The base-line system is standard phrase-based SMT trainedwith Moses (Koehn et al., 2007), with the defaultparameters.Table 2 shows results for the strategies 1, 2 and3 in terms of BLEU (Papineni et al., 2002). Fromthe BLEU scores, we see that strategy 1 givesslight improvements, but strategies 2 and 3 do not.Strategy System BLEUBaseline 32.291 +LMpos 32.542 Cascade 31.80Zh2Eslemmas 36.40Eslemmas2Es 71.793 +Generation 32.11Table 2: BLEU scores for Zh2Es translation taskand different morphology strategies.Table 3 shows several oracles for strategy 4with different morphology-based simplificationsof Spanish. Best oracles are for lemmas. Then, weexplore other simplifications, including lemmatiz-ing only: nouns (N), verbs (V), determiners (D),posesives (P) or adjectives (A). Non of these alter-natives approach the best oracle from lemmatizingall words.However, the interesting results are obtainedwhen simplifying by number (num) and/or gen-der (gen). When simplifying number or gender,note that we use the information of lemmas andtags. When generalizing number, note that insteadof using the information of singular (S) or plural(P) in the POS tag with the respective S or P, weuse the generic N. Therefore, we generalize the in-formation of number. Similarly when generalizinggender or both (numgen).58Oracles get closer to the lemmas simplificationwhen only simplifying both number and gender inSpanish. This finding is relevant in the sense thatit simplifies the classification task in the furtherwork that we are considering.System OraclesBaseline 32.29Zh2Eslemmas 36.40Zh2EsNlemmas 32.44Zh2EsVlemmas 33.07Zh2EsDlemmas 33.53Zh2EsPlemmas 32.22Zh2EsAlemmas 24.50Zh2Esnum 34.05Zh2Esgen 33.36Zh2Esnumgen 35.80Table 3: Oracles for different generalizations. Inbold, the most interesting finding.Table 1 shows examples of all simplificationspresented in previous Table 3. Note that simpli-fications in number and gender use lemmas plusPOS tags to omit just the corresponding informa-tion that will need to be recovered in the classifi-cation stage.5 Conclusions and Further WorkThis paper presents an ongoing work on enhacinga standard phrase-based SMT system by dealingwith morphology. We have reported several strate-gies including adding POS language modeling, ex-perimenting with cascade systems and factored-based translation models. Only the first one re-ported improvements over the baseline. An addi-tional strategy consists of studying different Span-ish simplifications and then, generating the full-form with classification techniques. Experimentsshow that simplification only in gender and num-ber almost achieves improvements as good as thesimplification on lemmas. This is an interestingresult that reduces the level of complexity for theclassification task. As further work, we will useclassification techniques based on deep learning.AcknowledgementsThis work has been supported in part bySpanish Ministerio de Economı´a y Competitivi-dad, contract TEC2012-38939-C03-02 as well asfrom the European Regional Development Fund(ERDF/FEDER) and the Seventh Framework Pro-gram of the European Commission through the In-ternational Outgoing Fellowship Marie Curie Ac-tion (IMTraP-2011-29951).ReferencesE. Avramidis and P. Koehn. 2008. Enriching mor-phologically poor languages for statistical machinetranslation. In Proc. of the conference of the As-sociation for Computational Linguistics and HumanLanguage Technology (ACL-HLT), pages 763–770.O. Bojar and A. Tamchyna. 2011. Forms wanted:Training smt on monolingual data. In Workshopof Machine Translation and Morphologically-RichLanguages.R. Collobert, J. Weston, L. Bottou, M. Karlen,K. Kavukcuoglu, and P. Kuksa. 2011. Naturallanguage processing (almost) from scratch. 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Ongoing study for enhancing chinese-spanish translation with morphology strategies

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Ongoing study for enhancing chinese-spanish translation with morphology strategies

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