On a Kernel Regression Approach to Machine Translation

Transcription

1 On a Kernel Regression Approach to Machine Translation Nicolás Serrano, Jesús Andrés-Ferrer, and Francisco Casacuberta Instituto Tecnológico de Informática {nserrano,jandres,fcn}@iti.upv.es Abstract. We present a machine translation framework based on Kernel Regression techniques. The translation process is modeled as a stringto-string mapping. For doing so, first both source and target strings are mapped to a natural vector space obtaining feature vectors. Afterwards, a translation mapping is defined from the source feature vector to the target feature vector. This translation mapping is learnt by linear regression. Once the target feature vector is obtained, we use a multi-graph search to find all the possible target strings whose mappings correspond to the translated feature vector. We present experiments in a small but relevant task showing encouraging results. 1 Introduction Machine translation deals with the problem of mapping sentences x from a source language X to a target language Y. Due to the complexity of the translation problem, the relationships between these two languages cannot be properly enumerated as a set of rules. Consequently, several authors have approached this problem as a statistical pattern recognition problem [1]. However, we present a new method for machine translation based on the work in [2], where a regression framework for learning transductions is proposed. Similar methods have been applied to many fields such as text categorization [4] or machine translation [5]. In our approach, both source and target strings are mapped to natural feature vectors. Then a translation mapping is learnt between feature vector domains. Given a source string x, the translation process consists in mapping it to a feature vector u and then, mapping this vector u to its corresponding target feature vector v. The latter mapping, the so-called translation mapping is learnt by regression techniques. Finally, the pre-image set for the target feature vector v must be found. This problem is referred as the Pre-image problem. The focus of this paper is to solve this problem in a regression-based machine translation framework. Some previous works such as [5], have explore the idea of learning the translation as a regression problem. These works do not handle the pre-image problem directly but use the model as a score to the standard statistical machine translation systems. Specifically, they use a phrased-based statistical machine translation model [6] and use the kernel regression model as score to the phrased-based

2 2 N. Serrano, J. Andres-Ferrer and F. Casacuberta search. This approach does not make the best of the regression approach, as proposed in [2], losing some of its main advantages. On the contrary, the pre-image search proposed in [2] is adapted in this work to the peculiarities in the machine translation problem. This aim is achieved by building the DeBruijn Graph [7] for a target feature vector and then finding eulerian paths within it. Nevertheless, due to the high dimensionality of the feature vectors, severe problems arise. 2 The Training Process The aim of machine translation is to learn a mapping from a source language X to a target language Y, i.e. f : X Y, where X is the source vocabulary and Y is the target vocabulary. In statistical machine translation the optimal translation function f is designed based on Bayes decision theory [1] where p(y x) is approximated by f(x) = arg maxp(y x) (1) y Y exp(c(x, y)) p(y x) = y Y exp(c(x, y )) (2) since the exponential function is monotonically increasing, Eq. (1) simplifies to f(x) = argmaxc(x, y) (3) y Y where C(x, y) is a score function, which is modelled by a feature set {h k } K 1 of theoretically and heuristically motivated functions [8] C(x, y) = k λ k h k (x, y) (4) where λ k are the model parameters that weight the feature function relevance. In state-of-art translation systems [9], these feature functions are mainly modelled by logarithms of probability models, such as the phrase-based models [6]. However, we propose a method for learning the translation function f based on regression techniques. In order to configure the regression framework, the source strings x X are mapped to the natural domain u U N D1 via a source mapping function φ X, i.e. u = φ X (x). Similarly, the target strings y Y are mapped to another natural domain, v V N D2, via a target mapping function φ Y, i.e. φ Y (y). Although both source and target mappings are not required to be of the same type, we will henceforth assume so. Then, we define the mappings, φ X and φ Y, as the function, φ n, that generates a n- gram count vector from a given string, x and y respectively. More accurately, φ n (x) = { x u1,..., x un } with u i standing for the i-th n-gram in lexicografic

3 On a Kernel Regression Approach to Machine Translation 3 order of the vocabulary X, and x u is the number of occurrences of u in x. For instance, the string x = aaabb of the language X = {a, b} will correspond to the bigram mapping output u = φ 2 (x) = (2, 1, 0, 1). The feature vector mapping u U N D1 is useful when comparing strings, since it helps to define a kernel between strings as follows: K n (x, x ) = φ n (x)φ n (x ) T = u Σ n x u x u (5) where K n (x, x ) ranges from the number of common n-grams if both strings are equal, to zero if they totally differ. Once the string-to-vector mapping is defined, the training problem is restated as a regression problem where a source-to-target mapping must to be found, i.e. finding the translation mapping h : U V. Given such a mapping h and two sentences, a source sentence x and its translation y; we define a string to target feature mapping, g : X V, as follows g(x) = h(φ X (x)) = φ Y (y) (6) Given a source string x, the proposed translation method consists in mapping it to U via φ X (x) and then, mapping it to V with the translation function h(x). Afterwards, given the target feature vector v obtained from the translation mapping, we compute its pre-image set φ 1 Y (v). Figure 1 depicts a general scheme of the whole translation process. X φ X( ) U f( ) g( ) h( ) Y φ Y ( ) φ 1 Y ( ) V Fig. 1. Machine translation kernel regression scheme 2.1 The Linear Regression The function h maps between two natural domains h : N D1 N D2. Since discrete regression problems yield complicated training algorithms, we learn an extension of this function h that approximates the natural domains by real domains, i.e. h : R D1 R D2. We further assume that our regression problem is linear, that is to say, that the mapping function h(u), and hereby its extension h, can be aproximated with a linear function, i.e. h(u) = Wu. Note that in this case the string-to-feature vector mapping g, is simplified to g(x) = Wφ X (x). Given a set of sentences, (x m, y m ) M 1, we can learn the optimal Ŵ matrix using

4 4 N. Serrano, J. Andres-Ferrer and F. Casacuberta the regularised least square as follows Ŵ = arg min W M Wφ X (x m ) φ Y (y m ) 2 + γ W 2 F (7) m=1 where F refers to the Frobenius norm and where γ > 0 is the regularization term. The aim of the regularisation term is to avoid the weights matrix W to contain large weights, which is a clear evidence of overtraining. The solution to Eq. (7) is unique and is found by differentiating the expression and equaling it to zero Ŵ = M Y (K X + γi) 1 M T X (8) where M Y = [φ Y (y 1 ),..., φ Y (y M )] is the D 2 M matrix of which j-th column vector is φ Y (y j ), where M X = [φ X (x 1 ),..., φ X (x M )] is the analogous for the source strings, and where K X is the Gram matrix associated to the kernel K n applied to the source samples, i.e. [K X ] ij = K n (x i, x j ) for all i, j such that 1 i, j M. 3 The decoding Problem In the proposed framework, the regression output for a given source string x, is a feature vector v which represents the occurrence counts of each target n-gram in its respective target translation y. Any target string y, that produces the same count vector v by means of the target mapping φ Y (y) is regarded as a possible translation of the source sentence x. Therefore, the decoding problem or the pre-image problem is theoretically constrained to a subdomain of Y. The decoding or pre-image problem is stated as the problem of finding which target sentences are represented as given a feature vector, i.e., to find the set (g(x)). The pre-image of g(x) is not unique since each reordering of the same counts leads to the same target feature vector v. To further understand the problem we give a simple example. We assume the target language in the example is given by Y = {a, b} and that the mapping function is φ 2, i.e. counting the number of possible bigrams. This implies that the count vector has four dimensions, one for each of the possible bigrams {aa, ab, ba, bb}. Since the dimensions are sorted in lexicographic order, the first dimension v 1 represents the occurrences of the bigram aa, the second dimension v 2, the occurrences of ab; and so on. If the regression output for a source sentence x is v = g(x) = {2, 1, 1, 0}, φ 1 Y then its pre-image set is φ 1 Y (v) = {aaaba, aabaa, baaab}. When dealing with natural feature vectors the pre-image problem has a wellknown solution [7]. First, it is needed to build the so-called DeBruijn graph as follows: all the (n 1)-gram sequences represent a different node in the graph, and edges are added going from a node, a 1 a 2... a n 1, to a node, a 2... a n 1 a n, if they have a positive weight, which is the count of the n-gram a 1... a n in v. In

5 On a Kernel Regression Approach to Machine Translation 5 2 a 1 1 b Fig.2. A DeBruijn graph for bigrams and the feature vector v = {2, 1,1, 0}. this graph, each Eulerian path 1 corresponds to a target string in the pre-image set. The DeBruijn graph of the proposed example is shown Fig. 2. At this point several problems arise in practice: The translation regresion is real-valued h instead of natural-valued h. Eulerian paths are not correctly defined in this case, since the DeBruijn technique cannot be directly applied in real-valued feature vectors. There are unknown source and target n-grams, those not appearing at the training samples. This makes the target feature vector v not to define a unique connected DeBruijn graph, but one with multiple isolated connected components. Even if we obtain all the possible eulerian paths within the graph, that represents all the possible translation, there is no way to select the proper translation corresponding to the source string. 3.1 The Aliasing Problem The result obtained from the regression is a real-valued g function where the meaning of each of the target vector dimensions v k is not clear. For instance, we take the example in which the target vocabulary is given by Y = {a, b} and a bigram mapping is used φ 2 ( ). In this example, the obtained target feature vector for a given source string could be v = (1.2, 0.5, 0.8, 0.25) instead of the perfect regression v = (2, 1, 1, 0). For deeply understanding the implication of any approximation, we must analyse the effect of variations in the natural-valued feature vector. Assuming that the correct regresion is given by v = (2, 1, 1, 0) then the pre-image set is φ 1 (v) = {abaa, aaba, baaab}. In the case in which the regresion produces v = (2, 1, 1, 1), the pre-image set changes to φ 1 (v) = {abbaa, aabba, baaabb, bbaaab}. Therefore, adding (or subtracting) 1 to any count dimension incurs in one extra (or less) word. The search method originally proposed in [2] is to round the real vector and then, build a DeBruijn graph from it and search eulerian paths whittin the graph. However, the best solution takes into account the previously discussed regression errors. For this aim we define the Levensthein loan 2 as the real increment or 1 A path inside a graph visiting each edge the number of times indicated by its weight 2 Named after the Levensthein distance [10]

6 6 N. Serrano, J. Andres-Ferrer and F. Casacuberta decrement that must be added to a given real vector in order to convert it to the natural domain. The Levensthein loan can be understood as the average Levensthein error of the correct hypothesis with respect to unknown reference. In order to find the pre-image for real-valued feature vectors, we build a weighted graph in a similar way the DeBruijn graph is built. The edges represent the real count of each n-gram according to the real-valued feature vector, instead of actual natural counts. During the search, the Levensthein loan is used to add or substract any necessary amount to the eulerian path simulating, in this way, a natural vector. In summary, the search algorithm looks for a path that uses the more of the weights in the graph and ask for the lowest possible loan. 3.2 The Unknown n-grams Problem In practice, it is common to find unknown n-grams when mapping strings to the feature vector count domain. This problem is stressed as the n becomes larger. Usually, this leads to DeBruijn graphs with isolated connected components and without eulerian paths covering the full sentence. A way to amend the problem is to apply backing-off to lower order models during the search. That is to say, when searching for a path, the search is simultaneously performed in different l-gram graphs (1 l n). Higher values of l are more restrictive during the search and also capture more structural dependencies but more number of parameters must be estimated in the regression. When the search at the highest order graph cannot continue building an eulerian path, the search backs off one order level with a penalisation factor, similarly to backing-off smoothing in language modeling [11]. This process goes on recursively until the unigram level if needed. 3.3 Adding Scores to the Search Lenvensthein loan score is not enough to select just one target string from the real-valued feature vector. Recall that even in the theoretical situation this would not be possible since several sentences can be built from different reordering of the same n-gram counts. These sentences have the same loan and therefore, we have no way to discriminate among them. Obviously, this problem is solved by adding an additional score to the target string y and consequently to each path in the pre-image search. Although, several scores can be proposed such as the probability given by statistical translations models, we have adopted in this first work a language model probability, specifically the n-gram language model [11]. In summary, the score in Eq. (3) for a given pair (x, y) is defined as follows for the proposed kernel regression model ( N ) C(x, y) = λ i sc i (x, y) + λ lm p n (y) + λ l exp ( y α) (9) i=1 where sc i is the Levensthein loan for the target string y at the i-th level graph; p n (y) is the language probability model for the target sentence y, and the last

7 On a Kernel Regression Approach to Machine Translation 7 term represents a word-bonus score to counteract the language model bias towards short sentences. 4 Experimental Results We have carried out experiments on the categorized EuTrans corpus [12]. This corpus comprises tourist requests at hotel front desks. Categorized EuTrans consists in pair of sentences divided into three sets: 9000 sentences to train, 1000 sentences were left out for validation and 3000 sentences for testing. There are 420 source and 230 target categories. The corpora perplexity is 3.95 and 4.73 for the target and source language respectively. Three kernel n-gram models where trained: one built from bigrams consisting on 2145 source bigrams and 929 target bigrams, another built from trigrams consisting on 5048 source trigrams and 2102 target trigrams; and the concatenation of both as described in 3.2. We trained increasing sizes of n-gram language models from 2 to 5 estimated by the SRILM toolkit [13]. The score weights, λ, in Eq. (9) were adjusted by the Downhill Simplex algorithm [14] for optimizing the BLEU score [15] on the validation set. The proposed system was compared with the Moses [9] baseline system, in which we have limited the phrase length to 7 allowing reordering and optimizing the parameters on a validation set. The Table 1. Results on categorized EuTrans. N stands for the order of the kernel whilst the LM order row stands for the order of the n-gram language model (0 no LM). Model N = 2 N = 3 N = 3 + (N = 2) Backoff LM order BLEU Moses baseline system scored 92.3 points of BLEU compared to the 95.5 points of our best system. A practical behaviour analysis of the proposed method is enclosed in the table 1. The results on this simple task are encouraging since almost all results surpass the baseline system. It can be observed that adding language information considerably improves the bigram system. However the trigram system is not benefited from the language model information, probably because of the corpus simplicity. Finally as expected, the search smoothing with back-off improves the results. 5 Conclusions and Future Work Kernel regression models are a new and encouraging approach to the machine translation field. In this work we have proposed a complete regression framework to machine translation by proposing a regression-based search. In contrast, other works perform the search by means of a phrase-based decoding. In addition, we

8 8 N. Serrano, J. Andres-Ferrer and F. Casacuberta have explored the idea of adding language information to rank the target strings among all the pre-image set. In a future work, different models apart from the n-gram language model will be added to rank the target strings, such as IBM word models and phrased-based models. Nevertheless several problems arise when complex corpus are used. To deal with them, further optimizations such as perform the model estimation through Cholesky incomplete decomposition or subset selection techniques are left for further research. Other possible improvements in the process would be using different kernel functions for comparing strings and use quadratic regression instead of linear regression as the regression model. Acknowledgments This work has been supported by the Spanish MEC under FPU scholarship AP , the valencian Conselleria d Empresa i Cincia grant CTBPRA/2005/004, grant Consolider Ingenio 2010 CSD by the EC (FEDER) and the Spanish MEC under grant TIN CO2-01. References 1. P.F. Brown et al. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistic. 19(2). pp (1993) 2. C. Cortes, M. Mohri, and J. Weston. A general regression technique for learning transductions. Proc. of the 22nd international conference on Machine learning, C. Cortes, M. Mohri, and J. Weston. A General Regression Framework for Learning String-to-String Mappings. In Predicting Structured Data. The MIT Press, N. Cancedda, E. Gaussier, C. Goutte, J. Render: Word-Sequence Kernels. Journal of Machine Learning Research 3. pp (2003) 5. Z. Wang and J. Shawe-Taylor: Kernel Regression Based Machine Translation. NAACL HLT 2007, Companion Volume, pp P. Koehn, F.J. Och and D. Marcu. Statistical phrase based translation. In Proceedings of HLT/NACL. (2003) 7. J.L. Gross and J. Yellen. Handbook of Graph Theory. Published by CRC Press. Pp ISBN: Franz Josef Och, Hermann Ney. The alignment template approach to statistical machine translation. Computational Linguistics, vol. 30(4), pp P. Koehn. H. Hoang. A. Birch and C. Callison-Burch. Moses: Open Source Toolkit for Statistical Machine Translation. Proc. of ACL 07 pp V. I. Levenshtein, Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10, pp (1966) 11. J.T. Goodman.An empirical study of smoothing techniques for language modelling. In Proc. of ACL 96, pp F. Casacuberta et al. Some approaches to statistical and finite-state speech-tospeech tranlation. Computer Speech and Language 18. pp (2004) 13. SRILM - An Extensible Language Modeling Toolkit, in Proc. Intl. Conf. Spoken Language Processing, Denver, Colorado, September J. A. Nelder and R. Mead A Simplex Method for Function Minimization The Computer Journal Vol 7, pp (1965) 15. Papineni, K., Roukos, S., Ward, T., and Zhu, W. J. (2002). BLEU: a method for automatic evaluation of machine translation in ACL-2002: pp