Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision. In this paper, we formalize the implicit similarity function induced by this approach, and show that it is susceptible to non-paraphrase pairs sharing a single ambiguous translation. Based on these insights, we design an alternative similarity metric that mitigates this issue by requiring the entire translation distribution to match, and implement a relaxation of it through the Information Bottleneck method. Our approach incorporates an adversarial term into MT training in order to learn representations that encode as much information about the reference translation as possible, while keeping as little information about the input as possible. Paraphrases can be generated by decoding back to the source from this representation, without having to generate pivot translations. In addition to being more principled and efficient than round-trip MT, our approach offers an adjustable parameter to control the fidelity-diversity trade-off, and obtains better results in our experiments. Paraphrase generation aims to generate alternative surface forms expressing the same semantic content as the original text (Madnani and Dorr, 2010), with applications in language understanding and data augmentation (Zhou and Bhat, 2021). One popular approach is to use an MT system to translate the input text into a pivot language and back (Wiet-ing and Gimpel, 2018; Mallinson et al., 2017; Roy and Grangier, 2019). While it intuitively makes sense that translating to another language and back should keep the meaning of a sentence intact while changing its surface form, it is not clear what exactly would be considered a paraphrase by such a system. round-trip MT system can be naturally decomposed as P ( x p | x s ) = P ( x p ) S ( x p , x s ) , where S is a symmetric similarity metric over the paraphrase space and P ( x p ) the probability of x p . We argue that this similarity function is not appropriate in the general case, as it can assign a high score to sentence pairs that share an ambiguous translation despite not being paraphrases of each other. This phenomenon is illustrated in Figure 2, where x s and x p share a confounding translation without gender marker. So as to address this issue, we design an alternative similarity function that requires the entire translation distribution to match, and develop a relaxation of it through the Information Bottleneck (IB) method. We implement this approach using an adversarial learning system depicted in Figure 1. 1621 He will go to school l ir a la escuela x s y j y i He will go to school x s x p She will go to school Ir a la escuela Figure 2: Confounding translation problem in round-trip MT. Ir a la escuela does not mark the gender of the subject due to ellipsis, and it is thus a valid translation of both He will go to school and She will go to school . Our model combines an encoder that, for a given sentence, removes the information that is not relevant to predict its translation, and a decoder that reconstructs a paraphrase from this encoding. In addition to being more principled, our approach is more efficient than round-trip MT at inference, can be tuned to favor fidelity or diversity, and achieves a better trade-off between the two. Our code is freely available 1 . We next review the paraphrase generation literature (2.1), and describe the information bottleneck method (2.2), which is the basis of our proposal. Early work on paraphrasing focused on retrieval methods, either extracting plausible sentences from large corpora for generation (Barzilay and McKe-own, 2001; Bannard and Callison-Burch, 2005), or identifying paraphrase pairs from weakly aligned corpora to create paraphrase datasets (Coster and Kauchak, 2011; Dolan et al., 2004). More recently, neural approaches for paraphrase generation have dominated the field. We classify these methods according to the type of supervision they use. Monolingual corpora. These systems are trained in an unsupervised fashion using unlabeled monolingual corpora. They usually employ an information bottleneck, with the goal of encoding semantic information in the latent space. Approaches include Variational Autoencoders (VAE) (Bowman et al., 2016), VAEs with Vector Quantization (Roy and Grangier, 2019), and latent bag-of-words models (Fu et al., 2019). Huang and Chang (2021) disentangle semantic and syntactic content in the latent space through a bag of words 1 https://github.com/aitorormazabal/ paraphrasing-from-parallel representation, which allows for syntactically controllable generation. Parallel corpora. These systems are trained on pairs of parallel sentences in two languages. Most of these methods are based on round-trip MT, where a sentence is translated to a pivot language and back in order to obtain a paraphrase. Hu et al. (2019) add lexical constraints to the MT decoding procedure to obtain better paraphrases. Mallinson et al. (2017) generate not one but multiple pivot sentences and use a fusion-in-decoder strategy. Paraphrase corpora. These systems are trained in a supervised manner over pairs or clusters of paraphrases. When such data is available, training a regular sequence-to-sequence model is a strong baseline (Egonmwan and Chali, 2019). Kumar et al. (2019) add submodular optimization to improve paraphrase diversity. Some VAE-based methods also leverage paraphrase clusters to learn a latent representation that disentangles meaning and form (Iyyer et al., 2018; Kumar et al., 2020; Hosking and Lapata, 2021; Chen et al., 2019). Most of these methods require a syntactic exemplar for generation, and assume that all surface forms are valid for all sentences. Hosking and Lapata (2021) do away with this assumption in the context of question paraphrasing, predicting a valid syntactic embedding from a discrete set at test time. While it is paraphrase corpora that offers the strongest supervision, such data is hard to obtain and usually restricted to narrow domains like Quora Question Pairs, WikiAnswers and Twitter (Hosking and Lapata, 2021; Kumar et al., 2019; Egonmwan and Chali, 2019). In contrast, parallel corpora is widely available, while offering a stronger training signal than monolingual corpora. For that reason, round-trip MT is a common choice when paraphrases are needed for downstream tasks (Xie et al., 1622 2020; Artetxe et al., 2020), as well as a common baseline in the paraphrasing literature (Hosking and Lapata, 2021; Roy and Grangier, 2019). 2 Our work focuses on this class of systems, identifying the limitations of round-trip MT and proposing a more principled alternative. Given two random variables X, Y , the Information Bottleneck (IB) method (Tishby et al., 1999) seeks to learn a representation T ( X ) that minimizes the Mutual Information (MI) between T and X , while preserving a minimum MI between T and Y . That is, the objective I ( X, T ) s.t. I ( T, Y )   is minimized. Since the MI is usually impossible to calculate exactly for neural representations, a common approach is to use variational methods, that turn the estimation problem into an optimization one. This can be done by adding a neural decoder on top of the representation, and training the entire system end-to-end (Poole et al., 2019). This is the approach we follow in this work. Let X be a random variable representing a sequence in the source language, and Y be a random variable representing its translation into a pivot language. 3 Given an input sequence x s  X , we can use round-trip MT to generate a paraphrase x p  X by translating x s into the pivot language and back, according to the forward and backward translation models P ( y | x s ) and P ( x p | y ) . As such, we can formulate the probability of round-trip MT generating a particular paraphrase x p by marginalizing over the set of possible pivot translations: P ( x p | x s ) = (cid:88) y  YP ( y | x s ) P ( x p | y ) (1) In what follows, we will characterize the paraphrases produced by this approach, i.e. the properties that x p needs to meet in relation to x s for P ( x p | x s ) to be high. 4 2 Round-trip MT has also been used to generate synthetic paraphrase corpora (Wieting and Gimpel, 2018). 3 For convenience, we will also use X and Y to refer to the set of source and target language sequences, and abbreviate probabilities of the form P ( X = x ) as P ( x ) . 4 Some round-trip MT systems do not consider all possible translations into the pivot language, but only a subset of them (Mallinson et al., 2017). In that case, the sum in Eq. 1 goes over y  { y 1 , ..., y K } , and we need to introduce a partition Z = (cid:80) y { y 1 ,...,y K } P ( y | x s ) to normalize the probabilities. However, the fundamental analysis in this section still applies. Refer to Appendix A for more details. By applying Bayes' rule, we can rewrite Eq. 1 as follows: P ( x p | x s ) = P ( x p ) (cid:88) y  YP ( y | x s ) P ( y | x p ) P ( y ) (cid:124) (cid:123)(cid:122) (cid:125) SMT ( x p ,x s ) (2) The sum on the right hand side can be interpreted as a symmetric similarity function, SMT ( x p , x s ) = SMT ( x s , x p ) = (cid:80) y P ( y | x s ) P ( y | x p ) P ( y ) , which measures the likelihood of two sentences to be actual paraphrases. The probability of x p given x s then becomes P ( x p | x s ) = P ( x p ) SMT ( x p , x s ) , which is the similarity between x s and x p , weighted by the marginal probability of x p . But when are x s and x p considered similar according to the above definition of SMT ( x s , x p ) ? Intuitively, SMT is a measure of the overlap between the conditional distributions that x s and x p induce over Y . This will be highest when P ( y | x s ) P ( y | x p ) is as large as possible for as many y as possible. At the same time, P ( y | x s ) P ( y | x p ) will be high when both P ( y | x s ) and P ( y | x p ) are high, that is, when y is a probable translation of both x s and x p . This captures the intuition that two sentences are similar when they can be translated into the same text in the pivot language. But what if x s and x p have one particular high-probability translation y j in common, but differ in the rest? As illustrated in Figure 2, this can happen when y j is ambiguous in the target language and can mean both x s and x p , even if x s and x p are not equivalent (e.g., when x s uses the masculine form, x p the feminine form, and y j does not mark the gen-der). In this case, the sum (cid:80) y P ( y | x s ) P ( y | x p ) P ( y ) will be dominated by P ( y j | x s ) P ( y j | x p ) P ( y j ) , which will be high when both P ( y j | x s ) and P ( y j | x p ) are high. We can thus conclude that the implicit similarity function underlying round-trip MT is flawed, as it assigns a high score to a pair of sequences ( x s , x p ) that have an ambiguous translation in common. As a consequence, round-trip MT will generate x p as a paraphrase of x s with a high probability, even if the two sequences have a different meaning. As shown in the previous section, the implicit similarity function induced by round-trip MT is not adequate in the general case, as it assigns a high score to pairs of sequences that share a single translation, despite differing in the rest. So as to address 1623 this, we can define an alternative similarity function that requires the entire translation distribution to match: S ( x p , x s ) = (cid:40) 1 P ( y | x p ) = P ( y | x s )  y  Y 0 otherwise (3) and use it to replace SMT in Eq. 2 so that P ( x p | x s )  P ( x p ) S ( x p , x s ) . However, this definition is too strict, as it is virtually impossible that P ( y | x p ) and P ( y | x s ) are exactly the same for all y  Y . 5 In 4.1, we define a relaxation of it through the IB method, which introduces an adjustable parameter  to control how much we deviate from it. In 4.2, we characterize the paraphrases generated by this approach, showing that they are less susceptible to the problem of confounding translations described in the previous section. So as to implement the similarity function in Eq. 3, we will use the IB method to learn an encoding T for X such that the following holds: S ( x p , x s ) = P ( x p | T ( x s )) P ( x p ) Z ( x s ) (4) where Z ( x s ) is a normalizer that does not depend on the paraphrase candidate x p . As seen in 2.2, given a source variable X and a target variable Y , the IB method seeks to find an encoding T ( X ) that minimizes the MI with X (maximizing compression), while preserving a certain amount of information about Y : min TI ( X, T ) s.t I ( T, Y )  . This constrained minimization is achieved by introducing a Lagrange multiplier  and minimizing As    , all the information about Y is preserved and the IB method learns a minimal sufficient statistic T , that is, an encoding that satisfies I ( T, Y ) = I ( X, Y ) while achieving the lowest I ( T, X ) possible. The following theorem states that such a minimal sufficient statistic T induces the similarity function in Eq. 3 (proof in Appendix C): 5 One reason is that we use empirical estimates of P ( y | x p ) and P ( y | x s ) , which will deviate from the ground truth. Theorem 1. Suppose the random variable X represents a sentence in the source language, Y represents its translation, and T is a minimal sufficient statistic of X with respect to Y. Let x p and x s be a pair of sentences in the source language. Then, P ( x p | T ( x s )) = P ( x p ) S ( x p ,x s ) Z ( x s ) , where S is given by Equation 3, and Z is a normalizing factor that does not depend on x p . Thus, as    the IB method approximates the similarity metric S . In practice, when  is set to a fixed finite number, losing some information about the target variable is allowed, and a relaxation of the metric S is learned instead. We will next analyze the relaxation of S induced by the IB method. We will characterize what kind of sentences are considered paraphrases by it, showing that it is less susceptible to the problem of confounding translations found in round-trip MT (3). Derivations for the results in this section, as well as alternative bounds and broader discussion can be found in Appendix B. As seen in 4.1, we define paraphrase probabilities given an encoding T as P ( x p | T ( x s )) = P ( X = x p | T ( X ) = T ( x s )) , which can only be non-zero if T ( x p ) = T ( x s ) . This means that the encoding T will partition the source space into a collection of paraphrase clusters according to its value. Mathematically, given the equivalence relation x 1  x 2  T ( x 1 ) = T ( x 2 ) , only sentence pairs within the same equivalence class will have non-zero paraphrase probabilities. We then have the following theorem: Theorem 2. Suppose T is a solution of the IB optimization problem min TI ( X, T ) s.t I ( T, Y )   , and  = I ( X, Y )   . If A is the partition on X induced by T , we have: (cid:88) A A max x 1 ,x 2  AP ( x 1 ) P ( x 2 ) 2( P ( x 1 ) + P ( x 2 ))  D 1 ( PY | x 1 , PY | x 2 ) 2  , (7) where D 1 is the L 1 norm distance. It is easy to see that, when  = 0 , corresponding to  = I ( X, Y ) and    , this forces all distances to be zero. In that case, only sentences with identical translation distributions are considered paraphrases, in accordance with Theorem 1. In the general case, Theorem 2 states that the L 1 distance between the translation distributions of 1624 sentences that are considered paraphrases cannot be high, as it will be bounded by a function of  . While the SMT metric in  3 can be dominated by a high-probability term and effectively ignore differences in probability for the less likely translations, the L 1 norm gives equal importance to differences in probability for every translation candidate. Thanks to this, the resulting system will be less susceptible to the problem of confounding translations. In this section, we describe a practical implementation of the IB-based paraphrasing approach defined theoretically in 4. As illustrated in Figure 1, our system can be seen as an extension of a regular encoder-decoder MT architecture with an additional adversarial decoder, which is trained with an auto-encoding objective to reconstruct the original sentence x s from the encoder representation T ( x s ) . The encoder is trained to minimize the cross-entropy loss of the MT decoder, while maximizing the loss of the adversarial decoder. This way, the encoder is encouraged to remove as much information about x s as possible, while retaining the information that is necessary to predict its reference translation y . Thanks to this, T ( x s ) should capture the semantic content of x s (which is relevant to predict y ), without storing additional surface information (which is not relevant to predict y ). Once the model is trained, the adversarial decoder can be used to generate paraphrases of x s from this representation T ( x s ) . This adversarial architecture can be interpreted as an implementation of the IB method as follows. Following Poole et al. (2019), we start by adding a decoder q ( y | t ) on top of the encoder T ( x ) , and rewrite the I ( T, Y ) term as: I ( T, Y ) = EP ( y,t ) (cid:20) log q ( y | t ) P ( y ) (cid:21) + EP ( t ) [ KL ( P ( y | t ) || q ( y | t ))]  EP ( y,t ) (cid:20) log q ( y | t ) (cid:21) + h ( Y ) , (8) where equality will hold if q is the true conditional distribution q ( y | t ) = P ( y | t ) , and h is the differential entropy. If we parametrize T and q by a neural network encoder-decoder architecture the EP ( y,t ) (cid:20) log q ( y | t ) (cid:21) term in Eq. 8, can be rewritten as EP ( y,x ) (cid:20) log q ( y | T ( x )) (cid:21) , which is precisely the log likelihood of the data distribution of X, Y given by P . In other words, by training the encoder-decoder to maximize Eq. 8, we are implicitly maximizing the mutual information I ( T, Y ) . I ( X, T )  EP ( x,t ) (cid:20) log q ( x | t ) (cid:21) + h ( X ) = EP ( x ) (cid:20) log q ( x | T ( x )) (cid:21) + h ( X ) , (9) where equality will hold when q is the true conditional distribution and q ( x | T ( x )) = P ( x | T ( x )) . Thus, given an ideal decoder q that perfectly approximates the conditional distributions q ( x | T ( x )) and q ( y | T ( x )) , the IB minimization problem is equivalent to minimizing E p ( x ) (cid:20) log q ( x | T ( x )) (cid:21)   EP ( y,t ) (cid:20) log q ( y | t ) (cid:21) = EP ( x,y ) (cid:20) log q ( x | T ( x ))   log q ( y | T ( x )) (cid:21) . (10) In practice, we parametrize both the encoder T and the decoder q with transformer neural networks, and learn them from a parallel corpus. Since log q ( y | T ( x )) is a lower bound of I ( T, Y )  h ( Y ) , maximizing this term is theoretically sound. Minimizing EP ( x ) (cid:20) log q ( x | T ( x )) (cid:21) , on the other hand, amounts to minimizing a lower bound, which, while not as theoretically solid, is common practice in the variational optimization literature (Chen et al., 2018; Kim and Mnih, 2018). L ( T, q ) = EP ( x,y ) [   log q ( y | T ( x )) +(1   ) log q ( x | T ( x ))] =  LMT ( T, q )  (1   ) L Adv ( T ) , (11) where LMT is the regular MT loss of cross-entropy with the translation target, and L Adv is the cross-entropy with the source sentence (see Figure 1). 6 We thus observe that the proposed adversarial training architecture approximates the IB method. The 6 We make the adversarial term a function of T only in the minimization objective, as the gradient from the adversarial term is only propagated to the encoder. The adversarial decoder is independently trained to predict the source from the encoded representation. setting    corresponds to   1 , where the optimal solution is a minimal sufficient statistic. During training, the expectation in Eq. 11 is approximated by sampling batches from the training data. Care must be taken when optimizing the loss, as we do not want to propagate gradients of the adversarial loss to the adversarial decoder. If we did, a trivial way to minimize (1   ) log q ( x | T ( x )) would be to make the decoder bad at recovering x , which would not encourage T ( x ) to encode as little information as possible. To prevent this, we use a percentage K of the batches to learn the adversarial decoder log q ( x | T ( x )) , where the encoder is kept frozen. The rest of the batches are used to optimize the full term   log q ( y | T ( x )) + (1   ) log q ( x | T ( x )) , but the gradients for the second term are only propagated to the encoder. We experiment with the following systems: Proposed. Our system described in 5. We share the weights between the MT decoder and the adversarial decoder, indicating the language that should be decoded through a special language ID token. Unless otherwise indicated, we use  = 0 . 73 and K = 0 . 7 , which performed best in the development set. 7  Round-trip MT. A baseline that uses two separate MT models to translate into a pivot language and back (see 3). We use mBART (Liu et al., 2020) to initialize both our proposed system and round-trip MT, and train them using the same hyper-parameters as in the original work. 8 In both cases, we use the English-French WMT14 dataset (Bojar et al., 2014) as our parallel corpus for training. 9 We report results for two decoding strategies: beam search with a beam size of 5, and top-10 sampling with a temperature of 0.9 (optimized in the development set). 10 7 We performed a grid search, where   { 0 . 7 , 0 . 73 , 0 . 8 } and K  { 0 . 7 , 0 . 8 } , and chose the checkpoint with best iBLEU with  = 0 . 7 . 3 e  5 maximum learning rate, and 100 K total steps. 9 We filter the dataset by removing sentence pairs with a source/target length ratio that exceeds 1 . 5 or are longer than 250 words. We consider two axes when evaluating paraphrases: fidelity (the extent to which the meaning of the input text is preserved) and diversity (the extent to which the surface form is changed). Following common practice, we use a corpus of gold paraphrases to automatically measure these. More concretely, given the source sentence s , the reference paraphrase r and the candidate paraphrase c , we use BLEU( c, r ) as a measure of fidelity, and BLEU( c, s )known as self-BLEUas a measure of diversity. An ideal paraphrase system would give us a high BLEU, with as low a self-BLEU as possible. Given that there is generally a tradeoff between the two, we also report iBLEU =  BLEU  (1   ) self-BLEU, which combines both metrics into a single score (Mallinson et al., 2017). Following Hosking and Lapata (2021), we set  = 0 . 7 . For development, we extracted 156 paraphrase pairs from the STS Benchmark dataset (Cer et al., 2017), taking sentence pairs with a similarity score above 4.5. For our final evaluation, we used the Multiple Translations Chinese (MTC) corpus (Huang et al., 2002), which comprises three sources of Chinese journalistic text translated into English by multiple translation agencies. We extract the translations of the first two agencies to obtain an test set of 993 paraphrase pairs, where one is the source and the other the reference paraphrase. The third sentence if kept as an additional paraphrase for estimating human performance. We report our main results in Table 1. As it can be seen, our proposed system outperforms all baselines in terms of iBLEU, indicating that it achieves 1626 l = 0.7 l = 0.7 l = 0.73 l = 0.73 l = 0.8 l = 0.8 MT Beam MT Samp 15 20 25 20 30 40 50 SelfBLEU (diversity) BLEU ( f i de li t y ) Methods MT Beam MT Samp Ours Beam Ours Samp Figure 3: Effect of varying the  parameter on the development set. a better trade-off between diversity and fidelity. This advantage comes from a large improvement in diversity as measured by self-BLEU, at a cost of a small drop in fidelity as measured by BLEU. Both for round-trip MT and our proposed system, beam search does better than sampling in terms of fidelity, at the cost of sacrificing in diversity. Finally, the human reference scores show ample room for improvement in both axes. While our proposed system achieves the best combined score, our results also show that different approaches behave differently in terms of diversity and fidelity. In practice, it would be desirable to have a knob to control the trade-off between the two, as one may want to favor diversity or fidelity depending on the application. One additional advantage of our approach over round-trip MT is that it offers an adjustable parameter  to control the trade-off between these two axes. So as to understand the effect of this parameter, we tried different values of it in the development set, and report the resulting curve in Figure 3 together with the MT baselines. BLEU and Self-BLEU scores of the best checkpoints for each  (0.7,0.73,0.8) and plot the results together with the MT baselines for our systems in Figure 3. As expected, higher values of  yield systems that tend to copy more, being more faithful but less diverse. Consistent with our test results, we find that, for a given value of  , beam search does better than sampling in terms of fidelity, but worse in terms of diversity, yet both decoding strategies can 0 20 40 60 80 Diversity Fidelity Fluency MT Beam MT Samp Ours Beam Ours Samp Figure 4: Human evaluation results (larger is better). be adjusted to achieve a similar trade-off. More importantly, we observe that both curves are above round-trip MT, the gap being largest for the sampling variant. We can thus conclude that our proposed approach does better than round-trip MT for a comparable trade-off between diversity and fidelity, while offering a knob to adjust this trade-off as desired. So as to better understand the behavior of our approach in comparison with round-trip MT, we carried out a human evaluation through Amazon Mechanical Turk. Following the setup of Hosking and Lapata (2021), we sample 200 sentences from the MTC corpus and generate a pair of paraphrases for each of them, randomly choosing two systems to generate them. We then ask human evaluators to compare the two sentences according to three criteria: diversity, fidelity and fluency. More details about the judging criteria can be found in Appendix D. Figure 4 reports the percentage of head-to-head comparisons that each system has won. The results that we obtain are consistent with the trends observed in 7.1. More concretely, we observe that the beam search variant of round-trip MT achieves the best results in terms of fluency and fidelity, but does worst in diversity, indicating a tendency to copy. Our method with beam search does slightly better than the sampling MT variant in terms of diversity and slightly worse in terms of fidelity indicating a tendency to favor diversity over fidelity while also being more fluent. Finally, the sampling variant of our method achieves the best diversity, but has the worst fidelity and fluency. ally analyzed some paraphrases, 11 and report some examples in Table 2. Just in line with our previous results, we observe that the beam search variant of round-trip MT tends to deviate the least from the original sentence, while the sampling variant of our method generates the most diverse paraphrases (e.g., changing sales of vehicles were not included to vehicles were not included in sales ). At the same time, we observe that this tendency to improve diversity can cause artifacts like paraphrasing named entities (e.g., changing National Youth League to National League of Youth ), which can partly explain the drop in fidelity. In this work, we have shown that the implicit similarity function present in round-trip MT is not appropriate in the general case, as it considers sentence pairs that share a single ambiguous translation to be paraphrases. We address this issue by designing an alternative similarity function that requires the entire translation distribution to match, and develop a relaxation of it through the IB method, which we prove to be less susceptible to the problem of confounding translations. We 11 We randomly sampled 20 sentences from MTC and chose four illustrative examples for Table implement this approach through adversarial learning, training an encoder to preserve as much information as possible about the reference translation, while encoding as little as possible about the source. Not only is our approach more principled than round-trip MT, but it is also more efficient at inference, as it does not need to generate an intermediate translation. In addition, it offers a knob to adjust the fidelity-diversity trade-off through the  parameter, and obtains strong results in our experiments, outperforming round-trip MT. Acknowledgments Aitor Ormazabal, Gorka Labaka, Aitor Soroa and Eneko Agirre were supported by the Basque Government (excellence research group IT1343-19 and DeepText project KK-2020/00088) and the Spanish MINECO (project DOMINO PGC2018-102041-B-I00 MCIU/AEI/FEDER, UE). Aitor Ormazabal was supported by a doctoral grant from the Spanish MECD. Computing infrastructure funded by UPV/EHU and Gipuzkoako Foru Aldundia.
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