Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose UXLA a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios. In particular, UXLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language. At its core, UXLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on three diverse zero-resource cross-lingual transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to its success. Self-supervised learning in the form of pretrained language models (LM) has been the driving force in developing state-of-the-art NLP systems in recent years. These methods typically follow two basic steps, where a supervised task-specific finetuning follows a large-scale LM pretraining (Rad-ford et al., 2019). However, getting labeled data for every target task in every target language is difficult, especially for low-resource languages. Recently, the pretrain-finetune paradigm has also been extended to multi-lingual setups to train effective multi-lingual models that can be used for zero-shot cross-lingual transfer. Jointly trained deep multi-lingual LMs like mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) coupled  Equal contribution with supervised fine-tuning in the source language have been quite successful in transferring linguistic and task knowledge from one language to another without using any task label in the target language. The joint pretraining with multiple languages allows these models to generalize across languages. Despite their effectiveness, recent studies (Pires et al., 2019; K et al., 2020) have also highlighted one crucial limiting factor for successful cross-lingual transfer. They all agree that the cross-lingual generalization ability of the model is limited by the (lack of) structural similarity between the source and target languages. For example, for transferring mBERT from English, K et al. (2020) report about 23 . 6% accuracy drop in Hindi (structurally dissimilar) compared to 9% drop in Spanish (struc-turally similar) in cross-lingual natural language inference (XNLI). The difficulty level of transfer is further exacerbated if the (dissimilar) target language is low-resourced, as the joint pretraining step may not have seen many instances from this language in the first place. In our experiments (3.2), in cross-lingual NER (XNER), we report F1 reductions of 28.3% in Urdu and 30.4% in Burmese for XLM-R, which is trained on a much larger multilingual dataset than mBERT. One attractive way to improve cross-lingual generalization is to perform data augmentation (Simard et al., 1998), and train the model on examples that are similar but different from the labeled data in the source language. Formalized by the Vicinal Risk Minimization (VRM) principle (Chapelle et al., 2001), such data augmentation methods have shown impressive results in vision (Zhang et al., 2018; Berthelot et al., 2019). These methods enlarge the support of the training distribution by generating new data points from a vicinity distribution around each training example. For images, the vicinity of a training image can be defined by a set of operations like rotation and scaling, or by linear mixtures of features and labels (Zhang et al., 2018). However, when it comes to text, such unsupervised augmentation methods have rarely been successful. The main reason is that unlike images, linguistic units are discrete and a smooth change in their embeddings may not result in a plausible linguistic unit that has similar meanings. In NLP, to the best of our knowledge, the most successful augmentation method has so far been back-translation (Sennrich et al., 2016) which paraphrases an input sentence through round-trip translation. However, it requires parallel data to train effective machine translation systems, acquiring which can be more expensive for low-resource languages than annotating the target language data. Furthermore, back-translation is only applicable in a supervised setup and to tasks where it is possible to find the alignments between the original labeled entities and the back-translated entities, such as in question answering (Yu et al., 2018). Other related work includes contextual augmentation (Kobayashi, 2018), conditional BERT (Wu et al., 2018) and AUG-BERT (Shi et al., 2019). These methods use a constrained augmentation that alters a pretrained LM to a label-conditional LM for a specific task. Since they rely on labels, their application is limited by the availability of enough task labels. In this work, we propose UXLA , a robust u nsupervised cross l ingual a ugmentation framework for improving cross-lingual generalization of multilingual LMs. UXLA augments data from the unlabeled training examples in the target language as well as from the virtual input samples generated from the vicinity distribution of the source and target language sentences. With the augmented data, it performs simultaneous self-learning with an effective distillation strategy to learn a strongly adapted cross-lingual model from noisy (pseudo) labels for the target language task. We propose novel ways to generate virtual sentences using a multilingual masked LM (Conneau et al., 2020), and get reliable task labels by simultaneous multilingual co-training. This co-training employs a two-stage co-distillation process to ensure robust transfer to dissimilar and/or low-resource languages. We validate the effectiveness and robustness of UXLA by performing extensive experiments on three diverse zero-resource cross-lingual transfer tasksXNER, XNLI, and PAWS-X, which posit different sets of challenges, and across many (14 in total) language pairs comprising languages that are similar/dissimilar/low-resourced. UXLA yields impressive results on XNER, setting SoTA in all tested languages outperforming the baselines by a good margin. The relative gains for UXLA are particularly higher for structurally dissimilar and/or low-resource languages: 28.54%, 16.05%, and 9.25% absolute improvements for Urdu, Burmese, and Arabic, respectively. For XNLI, with only 5% labeled data in the source, it gets comparable results to the baseline that uses all the labeled data, and surpasses the standard baseline by 2.55% on average when it uses all the labeled data in the source. We also have similar find-ings in PAWS-X. We provide a comprehensive analysis of the factors that contribute to UXLA 's performance. We open-source our framework at https://ntunlpsg.github.io/project/uxla/ . While recent cross-lingual transfer learning efforts have relied almost exclusively on multi-lingual pretraining and zero-shot transfer of a fine-tuned source model, we believe there is a great potential for more elaborate methods that can leverage the unlabeled data better. Motivated by this, we present UXLA , our unsupervised data augmentation framework for zero-resource cross-lingual task adaptation. Figure 1 gives an overview of UXLA . Let D s = ( X s , Y s ) and D t = ( X t ) denote the training data for a source language s and a target language t , respectively. UXLA augments data from various origins at different stages of training. In the initial stage (epoch 1), it uses the augmented training samples from the target language ( D (cid:48) t ) along with the original source ( D s ). In later stages (epoch 2-3), it uses vicinal sentences generated from the vicinity distribution of source and target examples:  ( x s n | x s n ) and  ( x t n | x t n ) , where x sn  X s and x tn  X t . It performs self-training on the augmented data to acquire the corresponding pseudo labels. To avoid confirmation bias with self-training where the model accumulates its own errors, it simultaneously trains three task models to generate virtual training data through data augmentation and filtering of potential label noises via multi-epoch co-teaching (Zhou and Li, 2005). In each epoch, the co-teaching process first performs co-distillation , where two peer task models are used to select reliable training examples to train the third model. The selected samples with pseudo labels are then added to the target task Figure 1: Training flow of UXLA . model's training data by taking the agreement from the other two models, a process we refer to as co-guessing . The co-distillation and co-guessing mechanism ensure robustness of UXLA to out-of-domain distributions that can occur in a multilingual setup, e.g., due to a structurally dissimilar and/or low-resource target language. Algorithm 1 gives a pseu-docode of the overall training method. Each of the task models in UXLA is an instance of XLM-R finetuned on the source language task (e.g., English NER), whereas the pretrained masked LM parameterized by  mlm ( i.e., before fine-tuning) is used to define the vicinity distribution  ( x n | x n ,  mlm ) around each selected example x n . In the following, we describe the steps in Algorithm 1. We first train three instances of the XLM-R model (  (1) ,  (2) ,  (3) ) with an additional task-specific linear layer on the source language (English) labeled data. Each model has the same architecture (XLM-R large) but is initialized with different random seeds. For token-level prediction tasks ( e.g., NER), the token-level representations are fed into the classification layer, whereas for sentence-level tasks ( e.g., XNLI), the [CLS] representation is used as input to the classification layer. Training with confidence penalty Our goal is to train the task models so that they can be used reliably for self-training on a target language that is potentially dissimilar and low-resourced. In such situations, an overly confident (overfitted) model may produce more noisy pseudo labels, and the noise will then accumulate as the training progresses. Overly confident predictions may also impose difficulties on our distillation methods (2.3) in isolating good samples from noisy ones. However, training with the standard cross-entropy (CE) loss may result in overfitted models that produce overly confident predictions (low entropy), especially when the class distribution is not balanced. We address this by adding a negative entropy term H to the CE loss as follows. where x is the representation that goes to the output layer, and y c and p c ( x ) are respectively the ground truth label and model predictions with respect to class c . Such regularizer of output distribution has been shown to be effective for training large models (Pereyra et al., 2017). We also report significant gains with confidence penalty in 3. Appendix B shows visualizations on why confidence penalty is helpful for distillation. Our augmentated sentences come from two different sources: the original target language samples X t , and the virtual samples generated from the vicinity distribution of the source and target samples:  ( x sn | x sn ,  mlm ) and  ( x tn | x tn ,  mlm ) with x sn  X s and x tn  X t . It has been shown that contextual LMs pretrained on large-scale datasets capture useful linguistic features and can be used to generate fluent grammatical texts (Hewitt and Manning, 2019). We use XLM-R masked LM (Conneau et al., 2020) as our vicinity model  mlm , which is trained on massive multilingual corpora (2.5 TB of Common-Crawl data in 100 languages). The Algorithm 1 UXLA : a robust unsupervised data augmentation framework for cross-lingual NLP Input: source In order to generate samples around each selected example, we first randomly choose P % of the input tokens. Then we successively (one at a time) mask one of the chosen tokens and ask XLM-R masked LM to predict a token in that masked position, i.e., compute  ( x m | x,  mlm ) with m being the index of the masked token. For a specific mask, we sample S candidate words from the output distribution, and generate novel sentences by following one of the two alternative approaches. (i) Successive max In this approach, we take the most probable output token ( S = 1 ) at each prediction step, o  m = arg max o  ( x m = o | x,  mlm ) . A new sentence is constructed by P % newly generated tokens. We generate  (diversification factor) virtual samples for each original example x , by randomly masking P % tokens each time. (ii) Successive cross In this approach, we divide each original (multi-sentence) sample x into two parts and use successive max to create two sets of augmented samples of size  1 and  2 , respectively. We then take the cross of these two sets to generate  1   2 augmented samples. Augmentation of sentences through successive max or cross is carried out within the GEN-LM (generate via LM) module in Algorithm 1. For tasks involving a single sequence ( e.g., XNER), we directly use successive max. Pairwise tasks like XNLI and PAWS-X have pairwise dependencies: dependencies between a premise and a hypothesis in XNLI or dependencies between a sentence and its possible paraphrase in PAWS-X. To model such dependencies, we use successive cross, which uses cross-product of two successive max applied independently to each component. Due to discrete nature of texts, VRM based augmentation methods that are successful for images such as MixMatch (Berthelot et al., 2019) that generates new samples and their labels as simple linear interpolation, have not been successful in NLP. The meaning of a sentence can change entirely even with minor variations in the original sentence. For example, consider the following example generated by our vicinity model. Here, EU is an Organization whereas the newly predicted word Trump is a Person (different name type). Therefore, we need to relabel the augmented sentences no matter whether the original sentence has labels (source) or not (target). However, the relabeling process can induce noise, especially for dissimilar/low-resource languages, since the base task model may not be adapted fully in the early training stages. We propose a 2-stage sample distillation process to filter out noisy augmented data. Stage 1: Distillation by single-model The first stage of distillation involves predictions from a single model for which we propose two alternatives: ( i ) Distillation by model confidence: In this approach, we select samples based on the model's prediction confidence. This method is similar in spirit to the selection method proposed by Ruder and Plank (2018a). For sentence-level tasks ( e.g., XNLI), the model produces a single class distribution for each training example. In this case, the model's confidence is computed by p  = max c { 1 ...C } p c ( x ) . For token-level sequence labeling tasks ( e.g., NER), the model's confidence is computed by: p  = 1 T (cid:80) Tt =1 (cid:8) max c { 1 ...C } p c ( x t ) (cid:9) , where T is the length of the sequence. The distillation is then done by selecting the top  % samples with the highest confidence scores. ( ii ) Sample distillation by clustering: We propose this method based on the finding that large neural models tend to learn good samples faster than noisy ones, leading to a lower loss for good samples and higher loss for noisy ones (Han et al., 2018; Arazo et al., 2019). We use a 1d two-component Gaussian Mixture Model (GMM) to model per-sample loss distribution and cluster the samples based on their goodness . GMMs provide flexibility in modeling the sharpness of a distribution and can be easily fit using Expectation-Maximization (EM) (See more on Appendix C). The loss is computed based on the pseudo labels predicted by the model. For each sample x , its goodness probability is the posterior probability p ( z = g | x ,  GMM ) , where g is the component with smaller mean loss. Here, distillation hyperparameter  is the posterior probability threshold based on which samples are selected. Stage 2: Distillation by model agreement In the second stage of distillation, we select samples by taking the agreement (co-guess) of two different peer models  ( j ) and  ( k ) to train the third  ( l ) . Formally, AGREEMENT (cid:0) D ( k ) , D ( j ) ) = { ( X ( k ) , Y ( k ) ) : Y ( k ) = Y ( j ) } s.t. k (cid:54) = j 2.4 Data Samples Manipulation UXLA uses multi-epoch co-teaching. It uses D s and D (cid:48) t in the first epoch. In epoch 2, it uses  D t (tar-get virtual), and finally it uses all the four datasets D s , D (cid:48) t ,  D t , and  D s (line 22 in Algorithm 1). The datasets used at different stages can be of different sizes. For example, the number of augmented samples in  D s and  D t grow polynomially with the successive cross masking method. Also, the co-distillation produces sample sets of variable sizes. To ensure that our model does not overfit on one particular dataset, we employ a balanced sampling strategy. For N number of datasets {D i } Ni =1 with probabilities, { p i } Ni =1 , we define the following multinomial distribution to sample from: p i = f i (cid:80) Nj =1 f j , where f i = n i (cid:80) Nj =1 n j (2) where  is the sampling factor and n i is the total number of samples in the i th dataset. By tweaking  , we can control how many samples a dataset can provide in the mix. We consider three tasks in the zero-resource cross-lingual transfer setting. We assume labeled training data only in English, and transfer the trained model to a target language. For all experiments, we report the mean score of the three models that use different seeds. XNER: We use the standard CoNLL datasets (Sang, 2002; Sang and Meulder, 2003) for English (en), German (de), Spanish (es) and Dutch (nl). We also evaluate on Finnish (fi) and Arabic (ar) datasets collected from Bari et al. (2020). Note that Arabic is structurally different from English, and Finnish is from a different language family. To show how the models perform on extremely low-resource languages, we experiment with three structurally different languages from WikiANN (Pan et al., 2017) of different (unlabeled) training data sizes: Urdu (ur-20k training samples), Bengali (bn-10K samples), and Burmese (my-100 samples). XNLI We use the standard dataset (Conneau et al., 2018). For a given pair of sentences, the task is to predict the entailment relationship between the two sentences, i.e. , whether the second sentence ( hypothesis ) is an Entailment , Contradiction , or Model en es nl de ar fi Supervised Results LSTM-CRF (Bari et al., 2020) 89.77 84.71 85.16 78.14 75.49 84.21 XLM-R (Conneau et al., 2020) 92.92 89.72 92.53 85.81   XLM-R (our imp.) 92.9 89.2 92.9 86.2 86.8 92.4 Zero-Resource Baseline mBERT cased (our imp.) 91.13 74.76 79.58 70.99 45.48 65.95 XLM-R (our imp.) 92.23 79.29 80.87 73.40 49.04 75.57 XLM-R (ensemble) 92.76 80.62 81.46 75.40 52.30 76.85 Our Method mBERT cased +con-penalty 90.81 75.06 79.26 72.31 47.03 66.72 XLM-R+con-penalty 92.49 80.45 81.07 73.76 49.94 76.05 UXLA  83.05 85.21 80.33 57.35 79.75 UXLA (ensemble)  83.24 85.32 80.99 58.29 79.87 Table 1: F1 scores in XNER on the datasets from CoNLL and (Bari et al., 2020). Neutral with respect to the first one ( premise ). We experiment with Spanish, German, Arabic, Swahili (sw), Hindi (hi) and Urdu. PAWS-X The Paraphrase Adversaries from Word Scrambling Cross-lingual task (Yang et al., 2019) requires the models to determine whether two sentences are paraphrases. We evaluate on all the six (typologically distinct) languages: fr, es, de, Chinese (zh), Japanese (ja), and Korean (ko). Evaluation setup Our goal is to adapt a task model from a source language distribution to an unknown target language distribution assuming no labeled data in the target. In this scenario, there might be two different distributional gaps: ( i ) the generalization gap for the source distribution, and ( ii ) the gap between the source and target language distribution. We wish to investigate our method in tasks that exhibit such properties. We use the standard task setting for XNER, where we take 100% samples from the datasets as they come from various domains and sizes without any specific bias. However, both XNLI and PAWS-X training data come with machine-translated texts in target languages. Thus, the data is parallel and lacks enough diversity (source and target come from the same domain). Cross-lingual models trained in this setup may pick up distributional bias (in the label space) from the source. Artetxe et al. (2020) also argue that the translation process can induce subtle artifacts that may have a notable impact on models. Therefore, for XNLI and PAWS-X, we experiment with two different setups. First, to ensure distributional differences and non-parallelism, we use 5% of the training data from the source language and augment a different (nonparallel) 5% Model ur bn my Supervised Results XLM-R (our-impl) 97.1 97.8 76.8 Zero-Resource Results XLM-R (XTREME) 56.4 78.8 54.3 XLM-R (our imp.) 56.45 78.17 54.56 UXLA 84.99 82.68 70.61 Table 2: XNER results on WikiANN. data for the target language. We used a different seed each time to retrieve this 5% data. Second, to compare with previous methods, we also evaluate on the standard 100% setup. The evaluation is done on the entire test set in both setups. We will refer to these two settings as 5% and 100% . More details about model settings are in Appendix D. 3.2 Results XNER Table 1 reports the XNER results on the datasets from CoNLL and (Bari et al., 2020), where we also evaluate an ensemble by averaging the probabilities from the three models. We observe that after performing warm-up with conf-penalty (2.1), XLM-R performs better than mBERT on average by  3.8% for all the languages. UXLA gives absolute improvements of 3.76%, 4.34%, 6.94%, 8.31%, and 4.18% for es, nl, de, ar, and fi , respectively. Interestingly, it surpasses supervised LSTM-CRF for nl and de without using any target language labeled data. It also produces comparable results for es . In Table 2, we report the results on the three low-resource langauges from WikiANN. From these results and the results of ar and fi in Table 1, we see that UXLA is particularly effective for languages that are structurally dissimilar and/or low-resourced, especially when the base model is weak: Model en es de ar sw hi ur Supervised Results (TRANSLATE-TRAIN-ALL) XLM-R 89.1 86.6 85.7 83.1 78.0 81.6 78.1 Zero-Resource Baseline for Full (100%) English labeled training set XLM-R (XTREME) 88.7 83.7 82.5 77.2 71.2 75.6 71.7 XLM-R (our imp.) 88.87 84.34 82.78 78.44 72.08 76.40 72.10 XLM-R (ensemble) 89.24 84.73 83.27 79.06 73.17 77.23 73.07 XLM-R+con-penalty 88.83 84.30 82.86 78.20 71.83 76.24 71.62 UXLA  85.65 84.15 80.50 74.70 78.74 73.35 UXLA (ensemble)  86.12 84.61 80.89 74.89 78.98 73.45 Zero-Resource Baseline for 5% English labeled training set XLM-R (our imp.) 83.08 78.48 77.54 72.04 67.3 70.41 66.72 XLM-R (ensemble) 84.65 79.56 78.38 72.22 66.93 71.00 66.79 XLM-R+con-penalty 84.24 79.23 78.47 72.43 67.72 71.08 67.63 UXLA  81.53 80.88 77.42 72.31 74.70 70.84 UXLA (ensemble)  82.35 81.93 78.56 73.53 75.20 71.15 Table 3: Results in accuracy for XNLI. 28.54%, 16.05%, and 9.25% absolute improvements for ur, my and ar, respectively. XNLI-5% From Table 3, we see that the performance of XLM-R trained on 5% data is surprisingly good compared to the model trained on full data (see XLM-R (our imp.)), lagging by only 5.6% on average. In our single GPU implementation of XNLI, we could not reproduce the reported results of Conneau et al. (2020). However, our results resemble the reported XLM-R results of XTREME (Hu et al., 2020). We consider XTREME as our standard baseline for XNLI-100%. We observe that with only 5% labeled data in the source, UXLA gets comparable results to the XTREME baseline that uses 100% labeled data (lagging behind by only  0.7% on avg.); even for ar and sw , we get 0.22% and 1.11% improvements, respectively. It surpasses the standard 5% baseline by 4.2% on average. Specifically, UXLA gets absolute improvements of 3.05%, 3.34%, 5.38%, 5.01%, 4.29%, and 4.12% for es, de, ar, sw, hi, and ur , respectively. Again, the gains are relatively higher for low-resource and/or dissimilar languages despite the base model being weak in such cases. XNLI-100% Now, considering UXLA 's performance on the full (100%) labeled source data in Table 3, we see that it achieves SoTA results for all of the languages with an absolute improvement of 2.55% on average from the XTREME baseline. Specifically, UXLA gets absolute improvements of 1.95%, 1.68%, 4.30%, 3.50%, 3.24%, and 1.65% for es, de, ar, sw, hi, and ur , respectively. PAWS-X Similar to XNLI, we observe sizable improvements for UXLA over the baselines on PAWS-X for both 5% and 100% settings (Table 4). Specifically, in 5% setting, UXLA gets absolute gains of 5.33%, 5.94%, 5.04%, 6.85%, 7.00%, and 5.45% for de, es, fr, ja, ko, and zh , respectively, while in 100% setting, it gets 2.21%, 2.36%, 2.00%, 3.99%, 4.53%, and 4.41% improvements respectively. In general, we get an average improvements of 5.94% and 3.25% in PAWS-X-5% and PAWS-X-100% settings respectively. Moreover, our 5% setting outperforms 100% XLM-R baselines for es, ja, and zh . Interestingly, in the 100% setup, our UXLA (ensemble) achieves almost similar accuracies compared to supervised finetuning of XLM-R on all target language training dataset. In this section, we analyze UXLA by dissecting it and measuring the contribution of its each of the components . For this, we use the XNER task and analyze the model based on the results in Table 1. Model confidence vs. clustering We first analyze the performance of our single-model distillation methods (2.3) to see which of the two alternatives works better. From Table 5, we see that both perform similarly with model confidence being slightly better. In our main experiments (Tables 1-4) and subsequent analysis, we use model confidence for distillation. However, we should not rule out the clustering method as it gives a more general Model en de es fr ja ko zh Supervised Results (TRANSLATE-TRAIN-ALL) XLM-R (our impl.) 95.8 92.5 92.8 93.5 85.5 86.6 87.6 Zero-Resource Baseline for Full (100%) English labeled training set XLM-R (XTREME) 94.7 89.7 90.1 90.4 78.7 79.0 82.3 XLM-R (our imp.) 95.46 90.06 89.92 90.85 79.89 79.74 82.49 XLM-R (ensemble) 96.10 90.75 90.55 91.80 80.55 80.70 83.45 XLM-R+con-penalty 95.38 90.75 90.72 91.71 81.77 82.07 84.25 UXLA  92.27 92.28 92.85 83.88 84.27 86.90 UXLA (ensemble)  92.55 92.35 93.35 84.30 84.35 86.95 Zero-Resource Baseline for 5% English labeled training set XLM-R (our imp.) 91.15 83.72 84.32 85.08 73.65 72.60 77.22 XLM-R (ensemble) 92.05 84.05 84.65 85.75 74.30 71.95 77.50 XLM-R+con-penalty 91.85 86.15 86.38 85.98 76.03 75.43 79.15 UXLA  89.05 90.27 90.12 80.50 79.60 82.65 UXLA (ensemble)  89.25 90.85 90.25 81.15 80.15 82.90 Table 4: Results in accuracy for PAWS-X. solution to consider other distillation features ( e.g., sequence length, language) than model prediction scores, which we did not explore in this paper. Distillation factor  We next show the results for different distillation factor (  ) in Table 5. Here 100% refers to the case when no single-model distillation is done based on model confidence. We notice that the best results for each of the languages are obtained for values other than 100%, which indicates that distillation is indeed an effective step in UXLA . See Appendix B for more analysis on  . Two-stage distillation We now validate whether the second-stage distillation ( distillation by model agreement ) is needed. In Table 5, we also compare the results with the model agreement (shown as  ) to the results without using any agreement (  ). We observe better performance with model agreement in all the cases on top of the single-model distillation which validates its utility. Results with  = 100 , Agreement =  can be considered as the tri-training (Ruder and Plank, 2018b) baseline. Figure 2 presents the effect of different types of augmented data used by different epochs in our multi-epoch co-teaching framework. We observe that in every epoch, there is a significant boost in F1 scores for each of the languages. Arabic, being structural dissimilar to English, has a lower base score, but the relative improvements brought by UXLA are higher for Arabic, especially in epoch 2  Agreement es nl de ar fi Distillation by clustering 0.7  82.28 83.25 78.86 52.64 78.47 0.5  82.35 83.11 78.16 54.20 78.28 Distillation by model confidence 50%  82.52 82.46 75.95 52.00 77.51  81.66 82.26 77.19 52.97 77.77 80%  82.33 83.53 78.50 54.48 78.43  81.61 83.03 77.08 53.31 78.34 90%  81.90 82.80 79.03 52.41 78.66  81.21 82.77 77.28 52.20 77.93 100%  82.50 82.35 77.06 52.58 77.51  81.89 82.15 76.97 52.68 78.01 Table 5: Analysis of distillation on XNER. For all the three tasks, we get reasonable improvements over the baselines by training with confidence penalty (2.1). Specifically, we get 0.56%, 0.74%, 1.89%, and 1.18% improvements in XNER, XNLI-5%, PAWS-X-5%, and PAWS-X-100% respectively (Table 1,3,4). The improvements in XNLI-100% are marginal and inconsistent, which we suspect due to the balanced class distribution. From the results of ensemble models, we see that the ensemble boosts the baseline XLM-R. However, our regular UXLA still outperforms the ensemble baselines by a sizeable margin. Moreover, ensem-bling the trained models from UXLA further improves the performance. These comparisons ensure that the capability of UXLA through co-teaching and co-distillation is beyond the ensemble effect. Table 6 shows the robustness of the fine-tuned UXLA model on XNER task. After fine-tuning in a specific target language, the F1 scores in English remain almost similar (see first row). For some languages, UXLA adaptation on a different language also improves the performance. For example, Arabic gets improvements for all UXLA -adapted models (compare 50.88 with others in row 5). This indicates that augmentation of UXLA does not overfit on a target language. More baselines, analysis and visualizations are added in Appendix. Recent years have witnessed significant progress in learning multilingual pretrained models. Notably, mBERT (Devlin et al., 2019) extends (English) BERT by jointly training on 102 languages. XLM (Lample and Conneau, 2019) extends mBERT with a conditional LM and a translation LM (using parallel data) objectives. Conneau et al. (2020) train the largest multilingual language model XLM-R with RoBERTa (Liu et al., 2019). Wu and Dredze (2019), Keung et al. (2019), and Pires et al. (2019) evaluate zero-shot cross-lingual transferability of mBERT on several tasks and attribute its generalization capability to shared subword units. Pires et al. (2019) also found structural similarity ( e.g., word order) to be another important factor for successful cross-lingual transfer. K et al. (2020), however, show that the shared subword has a minimal contribution; instead, the structural similarity between languages is more crucial for effective transfer. Older data augmentation approaches relied on distributional clusters (Tckstrm et al., 2012). A number of recent methods have been proposed using contextualized LMs (Kobayashi, 2018; Wu et al., 2018; Shi et al., 2019; Ding et al., 2020; Liu et al., 2021). These methods rely on labels to perform label-constrained augmentation, thus not directly comparable with ours. Also, there are fundamental differences in the way we use the pretrained LM. Unlike them our LM augmentation is purely unsupervised and we do not perform any fine-tuning of the pretrained vicinity model. This disjoint characteristic gives our framework the flexibility to replace  lm even with a better monolingual LM for a specific target language, which in turn makes UXLA extendable to utilize stronger LMs that may come in the future. In a concurrent work (Mohiuddin et al., 2021), we propose a contextualized LM based data augmentation for neural machine translation and show its advantages over traditional back-translation gaining improved performance in low-resource scenarios. We propose a novel data augmentation framework, UXLA , for zero-resource cross-lingual task adaptation. It performs simultaneous self-training with data augmentation and unsupervised sample selection. With extensive experiments on three different cross-lingual tasks spanning many language pairs, we have demonstrated the effectiveness of UXLA . For the zero-resource XNER task, UXLA sets a new SoTA for all the tested languages. For both XNLI and PAWS-X tasks, with only 5% labeled data in the source, UXLA gets comparable results to the baseline that uses 100% labeled data.
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