prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What evidence do the authors present that the model can capture some biases in data annotation and collection?
Context 1: Introduction
People are increasingly using social networking platforms such as Twitter, Facebook, YouTube, etc  to communicate their opinions and share information  Although the interactions among users on these platforms can lead to constructive conversations, they have been increasingly exploited for the propagation of abusive language and the organization of hate-based activities BIBREF0, BIBREF1, especially due to the mobility and anonymous environment of these online platforms  Violence attributed to online hate speech has increased worldwide  For example, in the UK, there has been a significant increase in hate speech towards the immigrant and Muslim communities following the UK's leaving the EU and the Manchester and London attacks  The US also has been a marked increase in hate speech and related crime following the Trump election  Therefore, governments and social network platforms confronting the trend must have tools to detect aggressive behavior in general, and hate speech in particular, as these forms of online aggression not only poison the social climate of the online communities that experience it, but can also provoke physical violence and serious harm BIBREF1 
Recently, the problem of online abusive detection has attracted scientific attention  Proof of this is the creation of the third Workshop on Abusive Language Online or Kaggle’s Toxic Comment Classification Challenge that gathered 4,551 teams in 2018 to detect different types of toxicities (threats, obscenity, etc )  In the scope of this work, we mainly focus on the term hate speech as abusive content in social media, since it can be considered a broad umbrella term for numerous kinds of insulting user-generated content  Hate speech is commonly defined as any communication criticizing a person or a group based on some characteristics such as gender, sexual orientation, nationality, religion, race, etc  Hate speech detection is not a stable or simple target because misclassification of regular conversation as hate speech can severely affect users’ freedom of expression and reputation, while misclassification of hateful conversations as unproblematic would maintain the status of online communities as unsafe environments BIBREF2 
To detect online hate speech, a large number of scientific studies have been dedicated by using Natural Language Processing (NLP) in combination with Machine Learning (ML) and Deep Learning (DL) methods BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF0  Although supervised machine learning-based approaches have used different text mining-based features such as surface features, sentiment analysis, lexical resources, linguistic features, knowledge-based features or user-based and platform-based metadata BIBREF8, BIBREF9, BIBREF10, they necessitate a well-defined feature extraction approach  The trend now seems to be changing direction, with deep learning models being used for both feature extraction and the training of classifiers  These newer models are applying deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), etc BIBREF6, BIBREF0 to enhance the performance of hate speech detection models, however, they still suffer from lack of labelled data or inability to improve generalization property 
Here, we propose a transfer learning approach for hate speech understanding using a combination of the unsupervised pre-trained model BERT BIBREF11 and some new supervised fine-tuning strategies  As far as we know, it is the first time that such exhaustive fine-tuning strategies are proposed along with a generative pre-trained language model to transfer learning to low-resource hate speech languages and improve performance of the task  In summary:
We propose a transfer learning approach using the pre-trained language model BERT learned on English Wikipedia and BookCorpus to enhance hate speech detection on publicly available benchmark datasets  Toward that end, for the first time, we introduce new fine-tuning strategies to examine the effect of different embedding layers of BERT in hate speech detection 
Our experiment results show that using the pre-trained BERT model and fine-tuning it on the downstream task by leveraging syntactical and contextual information of all BERT's transformers outperforms previous works in terms of precision, recall, and F1-score  Furthermore, examining the results shows the ability of our model to detect some biases in the process of collecting or annotating datasets  It can be a valuable clue in using pre-trained BERT model for debiasing hate speech datasets in future studies 
Previous Works
Here, the existing body of knowledge on online hate speech and offensive language and transfer learning is presented 
Online Hate Speech and Offensive Language: Researchers have been studying hate speech on social media platforms such as Twitter BIBREF9, Reddit BIBREF12, BIBREF13, and YouTube BIBREF14 in the past few years  The features used in traditional machine learning approaches are the main aspects distinguishing different methods, and surface-level features such as bag of words, word-level and character-level $n$-grams, etc  have proven to be the most predictive features BIBREF3, BIBREF4, BIBREF5  Apart from features, different algorithms such as Support Vector Machines BIBREF15, Naive Baye BIBREF1, and Logistic Regression BIBREF5, BIBREF9, etc  have been applied for classification purposes  Waseem et al  BIBREF5 provided a test with a list of criteria based on the work in Gender Studies and Critical Race Theory (CRT) that can annotate a corpus of more than $16k$ tweets as racism, sexism, or neither  To classify tweets, they used a logistic regression model with different sets of features, such as word and character $n$-grams up to 4, gender, length, and location  They found that their best model produces character $n$-gram as the most indicative features, and using location or length is detrimental  Davidson et al  BIBREF9 collected a $24K$ corpus of tweets containing hate speech keywords and labelled the corpus as hate speech, offensive language, or neither by using crowd-sourcing and extracted different features such as $n$-grams, some tweet-level metadata such as the number of hashtags, mentions, retweets, and URLs, Part Of Speech (POS) tagging, etc  Their experiments on different multi-class classifiers showed that the Logistic Regression with L2 regularization performs the best at this task  Malmasi et al  BIBREF15 proposed an ensemble-based system that uses some linear SVM classifiers in parallel to distinguish hate speech from general profanity in social media 
As one of the first attempts in neural network models, Djuric et al  BIBREF16 proposed a two-step method including a continuous bag of words model to extract paragraph2vec embeddings and a binary classifier trained along with the embeddings to distinguish between hate speech and clean content  Badjatiya et al  BIBREF0 investigated three deep learning architectures, FastText, CNN, and LSTM, in which they initialized the word embeddings with either random or GloVe embeddings  Gambäck et al  BIBREF6 proposed a hate speech classifier based on CNN model trained on different feature embeddings such as word embeddings and character $n$-grams  Zhang et al  BIBREF7 used a CNN+GRU (Gated Recurrent Unit network) neural network model initialized with pre-trained word2vec embeddings to capture both word/character combinations (e  g , $n$-grams, phrases) and word/character dependencies (order information)  Waseem et al  BIBREF10 brought a new insight to hate speech and abusive language detection tasks by proposing a multi-task learning framework to deal with datasets across different annotation schemes, labels, or geographic and cultural influences from data sampling  Founta et al  BIBREF17 built a unified classification model that can efficiently handle different types of abusive language such as cyberbullying, hate, sarcasm, etc  using raw text and domain-specific metadata from Twitter  Furthermore, researchers have recently focused on the bias derived from the hate speech training datasets BIBREF18, BIBREF2, BIBREF19  Davidson et al  BIBREF2 showed that there were systematic and substantial racial biases in five benchmark Twitter datasets annotated for offensive language detection  Wiegand et al  BIBREF19 also found that classifiers trained on datasets containing more implicit abuse (tweets with some abusive words) are more affected by biases rather than once trained on datasets with a high proportion of explicit abuse samples (tweets containing sarcasm, jokes, etc ) 
Transfer Learning: Pre-trained vector representations of words, embeddings, extracted from vast amounts of text data have been encountered in almost every language-based tasks with promising results  Two of the most frequently used context-independent neural embeddings are word2vec and Glove extracted from shallow neural networks  The year 2018 has been an inflection point for different NLP tasks thanks to remarkable breakthroughs: Universal Language Model Fine-Tuning (ULMFiT) BIBREF20, Embedding from Language Models (ELMO) BIBREF21, OpenAI’ s Generative Pre-trained Transformer (GPT) BIBREF22, and Google’s BERT model BIBREF11  Howard et al  BIBREF20 proposed ULMFiT which can be applied to any NLP task by pre-training a universal language model on a general-domain corpus and then fine-tuning the model on target task data using discriminative fine-tuning  Peters et al  BIBREF21 used a bi-directional LSTM trained on a specific task to present context-sensitive representations of words in word embeddings by looking at the entire sentence  Radford et al  BIBREF22 and Devlin et al  BIBREF11 generated two transformer-based language models, OpenAI GPT and BERT respectively  OpenAI GPT BIBREF22 is an unidirectional language model while BERT BIBREF11 is the first deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus  BERT has two novel prediction tasks: Masked LM and Next Sentence Prediction  The pre-trained BERT model significantly outperformed ELMo and OpenAI GPT in a series of downstream tasks in NLP BIBREF11  Identifying hate speech and offensive language is a complicated task due to the lack of undisputed labelled data BIBREF15 and the inability of surface features to capture the subtle semantics in text  To address this issue, we use the pre-trained language model BERT for hate speech classification and try to fine-tune specific task by leveraging information from different transformer encoders 
Methodology
Here, we analyze the BERT transformer model on the hate speech detection task  BERT is a multi-layer bidirectional transformer encoder trained on the English Wikipedia and the Book Corpus containing 2,500M and 800M tokens, respectively, and has two models named BERTbase and BERTlarge  BERTbase contains an encoder with 12 layers (transformer blocks), 12 self-attention heads, and 110 million parameters whereas BERTlarge has 24 layers, 16 attention heads, and 340 million parameters  Extracted embeddings from BERTbase have 768 hidden dimensions BIBREF11  As the BERT model is pre-trained on general corpora, and for our hate speech detection task we are dealing with social media content, therefore as a crucial step, we have to analyze the contextual information extracted from BERT' s pre-trained layers and then fine-tune it using annotated datasets  By fine-tuning we update weights using a labelled dataset that is new to an already trained model  As an input and output, BERT takes a sequence of tokens in maximum length 512 and produces a representation of the sequence in a 768-dimensional vector  BERT inserts at most two segments to each input sequence, [CLS] and [SEP]  [CLS] embedding is the first token of the input sequence and contains the special classification embedding which we take the first token [CLS] in the final hidden layer as the representation of the whole sequence in hate speech classification task  The [SEP] separates segments and we will not use it in our classification task  To perform the hate speech detection task, we use BERTbase model to classify each tweet as Racism, Sexism, Neither or Hate, Offensive, Neither in our datasets  In order to do that, we focus on fine-tuning the pre-trained BERTbase parameters  By fine-tuning, we mean training a classifier with different layers of 768 dimensions on top of the pre-trained BERTbase transformer to minimize task-specific parameters 
Methodology ::: Fine-Tuning Strategies
Different layers of a neural network can capture different levels of syntactic and semantic information  The lower layer of the BERT model may contain more general information whereas the higher layers contain task-specific information BIBREF11, and we can fine-tune them with different learning rates  Here, four different fine-tuning approaches are implemented that exploit pre-trained BERTbase transformer encoders for our classification task  More information about these transformer encoders' architectures are presented in BIBREF11  In the fine-tuning phase, the model is initialized with the pre-trained parameters and then are fine-tuned using the labelled datasets  Different fine-tuning approaches on the hate speech detection task are depicted in Figure FIGREF8, in which $X_{i}$ is the vector representation of token $i$ in a tweet sample, and are explained in more detail as follows:
1  BERT based fine-tuning: In the first approach, which is shown in Figure FIGREF8, very few changes are applied to the BERTbase  In this architecture, only the [CLS] token output provided by BERT is used  The [CLS] output, which is equivalent to the [CLS] token output of the 12th transformer encoder, a vector of size 768, is given as input to a fully connected network without hidden layer  The softmax activation function is applied to the hidden layer to classify 
2  Insert nonlinear layers: Here, the first architecture is upgraded and an architecture with a more robust classifier is provided in which instead of using a fully connected network without hidden layer, a fully connected network with two hidden layers in size 768 is used  The first two layers use the Leaky Relu activation function with negative slope = 0 01, but the final layer, as the first architecture, uses softmax activation function as shown in Figure FIGREF8 
3  Insert Bi-LSTM layer: Unlike previous architectures that only use [CLS] as the input for the classifier, in this architecture all outputs of the latest transformer encoder are used in such a way that they are given as inputs to a bidirectional recurrent neural network (Bi-LSTM) as shown in Figure FIGREF8  After processing the input, the network sends the final hidden state to a fully connected network that performs classification using the softmax activation function 
4  Insert CNN layer: In this architecture shown in Figure FIGREF8, the outputs of all transformer encoders are used instead of using the output of the latest transformer encoder  So that the output vectors of each transformer encoder are concatenated, and a matrix is produced  The convolutional operation is performed with a window of size (3, hidden size of BERT which is 768 in BERTbase model) and the maximum value is generated for each transformer encoder by applying max pooling on the convolution output  By concatenating these values, a vector is generated which is given as input to a fully connected network  By applying softmax on the input, the classification operation is performed 
Experiments and Results
We first introduce datasets used in our study and then investigate the different fine-tuning strategies for hate speech detection task  We also include the details of our implementation and error analysis in the respective subsections 
Experiments and Results ::: Dataset Description
We evaluate our method on two widely-studied datasets provided by Waseem and Hovey BIBREF5 and Davidson et al  BIBREF9  Waseem and Hovy BIBREF5 collected $16k$ of tweets based on an initial ad-hoc approach that searched common slurs and terms related to religious, sexual, gender, and ethnic minorities  They annotated their dataset manually as racism, sexism, or neither  To extend this dataset, Waseem BIBREF23 also provided another dataset containing $6 9k$ of tweets annotated with both expert and crowdsourcing users as racism, sexism, neither, or both  Since both datasets are overlapped partially and they used the same strategy in definition of hateful content, we merged these two datasets following Waseem et al  BIBREF10 to make our imbalance data a bit larger  Davidson et al  BIBREF9 used the Twitter API to accumulate 84 4 million tweets from 33,458 twitter users containing particular terms from a pre-defined lexicon of hate speech words and phrases, called Hatebased org  To annotate collected tweets as Hate, Offensive, or Neither, they randomly sampled $25k$ tweets and asked users of CrowdFlower crowdsourcing platform to label them  In detail, the distribution of different classes in both datasets will be provided in Subsection SECREF15 
Experiments and Results ::: Pre-Processing
We find mentions of users, numbers, hashtags, URLs and common emoticons and replace them with the tokens <user>,<number>,<hashtag>,<url>,<emoticon>  We also find elongated words and convert them into short and standard format; for example, converting yeeeessss to yes  With hashtags that include some tokens without any with space between them, we replace them by their textual counterparts; for example, we convert hashtag “#notsexist" to “not sexist"  All punctuation marks, unknown uni-codes and extra delimiting characters are removed, but we keep all stop words because our model trains the sequence of words in a text directly  We also convert all tweets to lower case 
Experiments and Results ::: Implementation and Results Analysis
For the implementation of our neural network, we used pytorch-pretrained-bert library containing the pre-trained BERT model, text tokenizer, and pre-trained WordPiece  As the implementation environment, we use Google Colaboratory tool which is a free research tool with a Tesla K80 GPU and 12G RAM  Based on our experiments, we trained our classifier with a batch size of 32 for 3 epochs  The dropout probability is set to 0 1 for all layers  Adam optimizer is used with a learning rate of 2e-5  As an input, we tokenized each tweet with the BERT tokenizer  It contains invalid characters removal, punctuation splitting, and lowercasing the words  Based on the original BERT BIBREF11, we split words to subword units using WordPiece tokenization  As tweets are short texts, we set the maximum sequence length to 64 and in any shorter or longer length case it will be padded with zero values or truncated to the maximum length 
We consider 80% of each dataset as training data to update the weights in the fine-tuning phase, 10% as validation data to measure the out-of-sample performance of the model during training, and 10% as test data to measure the out-of-sample performance after training  To prevent overfitting, we use stratified sampling to select 0 8, 0 1, and 0 1 portions of tweets from each class (racism/sexism/neither or hate/offensive/neither) for train, validation, and test  Classes' distribution of train, validation, and test datasets are shown in Table TABREF16 
As it is understandable from Tables TABREF16(classdistributionwaseem) and TABREF16(classdistributiondavidson), we are dealing with imbalance datasets with various classes’ distribution  Since hate speech and offensive languages are real phenomena, we did not perform oversampling or undersampling techniques to adjust the classes’ distribution and tried to supply the datasets as realistic as possible  We evaluate the effect of different fine-tuning strategies on the performance of our model  Table TABREF17 summarized the obtained results for fine-tuning strategies along with the official baselines  We use Waseem and Hovy BIBREF5, Davidson et al  BIBREF9, and Waseem et al  BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model  The evaluation results are reported on the test dataset and on three different metrics: precision, recall, and weighted-average F1-score  We consider weighted-average F1-score as the most robust metric versus class imbalance, which gives insight into the performance of our proposed models  According to Table TABREF17, F1-scores of all BERT based fine-tuning strategies except BERT + nonlinear classifier on top of BERT are higher than the baselines  Using the pre-trained BERT model as initial embeddings and fine-tuning the model with a fully connected linear classifier (BERTbase) outperforms previous baselines yielding F1-score of 81% and 91% for datasets of Waseem and Davidson respectively  Inserting a CNN to pre-trained BERT model for fine-tuning on downstream task provides the best results as F1- score of 88% and 92% for datasets of Waseem and Davidson and it clearly exceeds the baselines  Intuitively, this makes sense that combining all pre-trained BERT layers with a CNN yields better results in which our model uses all the information included in different layers of pre-trained BERT during the fine-tuning phase  This information contains both syntactical and contextual features coming from lower layers to higher layers of BERT 
Experiments and Results ::: Error Analysis
Although we have very interesting results in term of recall, the precision of the model shows the portion of false detection we have  To understand better this phenomenon, in this section we perform a deep analysis on the error of the model  We investigate the test datasets and their confusion matrices resulted from the BERTbase + CNN model as the best fine-tuning approach; depicted in Figures FIGREF19 and FIGREF19  According to Figure FIGREF19 for Waseem-dataset, it is obvious that the model can separate sexism from racism content properly  Only two samples belonging to racism class are misclassified as sexism and none of the sexism samples are misclassified as racism  A large majority of the errors come from misclassifying hateful categories (racism and sexism) as hatless (neither) and vice versa  0 9% and 18 5% of all racism samples are misclassified as sexism and neither respectively whereas it is 0% and 12 7% for sexism samples  Almost 12% of neither samples are misclassified as racism or sexism  As Figure FIGREF19 makes clear for Davidson-dataset, the majority of errors are related to hate class where the model misclassified hate content as offensive in 63% of the cases  However, 2 6% and 7 9% of offensive and neither samples are misclassified respectively 
To understand better the mislabeled items by our model, we did a manual inspection on a subset of the data and record some of them in Tables TABREF20 and TABREF21  Considering the words such as “daughters", “women", and “burka" in tweets with IDs 1 and 2 in Table TABREF20, it can be understood that our BERT based classifier is confused with the contextual semantic between these words in the samples and misclassified them as sexism because they are mainly associated to femininity  In some cases containing implicit abuse (like subtle insults) such as tweets with IDs 5 and 7, our model cannot capture the hateful/offensive content and therefore misclassifies  It should be noticed that even for a human it is difficult to discriminate against this kind of implicit abuses 
By examining more samples and with respect to recently studies BIBREF2, BIBREF24, BIBREF19, it is clear that many errors are due to biases from data collection BIBREF19 and rules of annotation BIBREF24 and not the classifier itself  Since Waseem et al BIBREF5 created a small ad-hoc set of keywords and Davidson et al BIBREF9 used a large crowdsourced dictionary of keywords (Hatebase lexicon) to sample tweets for training, they included some biases in the collected data  Especially for Davidson-dataset, some tweets with specific language (written within the African American Vernacular English) and geographic restriction (United States of America) are oversampled such as tweets containing disparage words “nigga", “faggot", “coon", or “queer", result in high rates of misclassification  However, these misclassifications do not confirm the low performance of our classifier because annotators tended to annotate many samples containing disrespectful words as hate or offensive without any presumption about the social context of tweeters such as the speaker’s identity or dialect, whereas they were just offensive or even neither tweets  Tweets IDs 6, 8, and 10 are some samples containing offensive words and slurs which arenot hate or offensive in all cases and writers of them used this type of language in their daily communications  Given these pieces of evidence, by considering the content of tweets, we can see in tweets IDs 3, 4, and 9 that our BERT-based classifier can discriminate tweets in which neither and implicit hatred content exist  One explanation of this observation may be the pre-trained general knowledge that exists in our model  Since the pre-trained BERT model is trained on general corpora, it has learned general knowledge from normal textual data without any purposely hateful or offensive language  Therefore, despite the bias in the data, our model can differentiate hate and offensive samples accurately by leveraging knowledge-aware language understanding that it has and it can be the main reason for high misclassifications of hate samples as offensive (in reality they are more similar to offensive rather than hate by considering social context, geolocation, and dialect of tweeters) 
Conclusion
Conflating hatred content with offensive or harmless language causes online automatic hate speech detection tools to flag user-generated content incorrectly  Not addressing this problem may bring about severe negative consequences for both platforms and users such as decreasement of platforms' reputation or users abandonment  Here, we propose a transfer learning approach advantaging the pre-trained language model BERT to enhance the performance of a hate speech detection system and to generalize it to new datasets  To that end, we introduce new fine-tuning strategies to examine the effect of different layers of BERT in hate speech detection task  The evaluation results indicate that our model outperforms previous works by profiting the syntactical and contextual information embedded in different transformer encoder layers of the BERT model using a CNN-based fine-tuning strategy  Furthermore, examining the results shows the ability of our model to detect some biases in the process of collecting or annotating datasets  It can be a valuable clue in using the pre-trained BERT model to alleviate bias in hate speech datasets in future studies, by investigating a mixture of contextual information embedded in the BERT’s layers and a set of features associated to the different type of biases in data 
Context 2: Introduction
End-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automatic speech recognition (ASR) and machine translation (MT) systems  The lack of labeled data, however, has become a major blocker for bridging the performance gaps between end-to-end models and cascading systems  Several corpora have been developed in recent years  post2013improved introduced a 38-hour Spanish-English ST corpus by augmenting the transcripts of the Fisher and Callhome corpora with English translations  di-gangi-etal-2019-must created the largest ST corpus to date from TED talks but the language pairs involved are out of English only  beilharz2019librivoxdeen created a 110-hour German-English ST corpus from LibriVox audiobooks  godard-etal-2018-low created a Moboshi-French ST corpus as part of a rare language documentation effort  woldeyohannis provided an Amharic-English ST corpus in the tourism domain  boito2019mass created a multilingual ST corpus involving 8 languages from a multilingual speech corpus based on Bible readings BIBREF7  Previous work either involves language pairs out of English, very specific domains, very low resource languages or a limited set of language pairs  This limits the scope of study, including the latest explorations on end-to-end multilingual ST BIBREF8, BIBREF9  Our work is mostly similar and concurrent to iranzosnchez2019europarlst who created a multilingual ST corpus from the European Parliament proceedings  The corpus we introduce has larger speech durations and more translation tokens  It is diversified with multiple speakers per transcript/translation  Finally, we provide additional out-of-domain test sets 
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice BIBREF10 for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  It includes a total 708 hours of French (Fr), German (De), Dutch (Nl), Russian (Ru), Spanish (Es), Italian (It), Turkish (Tr), Persian (Fa), Swedish (Sv), Mongolian (Mn) and Chinese (Zh) speeches, with French and German ones having the largest durations among existing public corpora  We also collect an additional evaluation corpus from Tatoeba for French, German, Dutch, Russian and Spanish, resulting in a total of 9 3 hours of speech  Both corpora are created at the sentence level and do not require additional alignments or segmentation  Using the official Common Voice train-development-test split, we also provide baseline models, including, to our knowledge, the first end-to-end many-to-one multilingual ST models  CoVoST is released under CC0 license and free to use  The Tatoeba evaluation samples are also available under friendly CC licenses  All the data can be acquired at https://github com/facebookresearch/covost 
Data Collection and Processing ::: Common Voice (CoVo)
Common Voice BIBREF10 is a crowdsourcing speech recognition corpus with an open CC0 license  Contributors record voice clips by reading from a bank of donated sentences  Each voice clip was validated by at least two other users  Most of the sentences are covered by multiple speakers, with potentially different genders, age groups or accents 
Raw CoVo data contains samples that passed validation as well as those that did not  To build CoVoST, we only use the former one and reuse the official train-development-test partition of the validated data  As of January 2020, the latest CoVo 2019-06-12 release includes 29 languages  CoVoST is currently built on that release and covers the following 11 languages: French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian and Chinese 
Validated transcripts were sent to professional translators  Note that the translators had access to the transcripts but not the corresponding voice clips since clips would not carry additional information  Since transcripts were duplicated due to multiple speakers, we deduplicated the transcripts before sending them to translators  As a result, different voice clips of the same content (transcript) will have identical translations in CoVoST for train, development and test splits 
In order to control the quality of the professional translations, we applied various sanity checks to the translations BIBREF11  1) For German-English, French-English and Russian-English translations, we computed sentence-level BLEU BIBREF12 with the NLTK BIBREF13 implementation between the human translations and the automatic translations produced by a state-of-the-art system BIBREF14 (the French-English system was a Transformer big BIBREF15 separately trained on WMT14)  We applied this method to these three language pairs only as we are confident about the quality of the corresponding systems  Translations with a score that was too low were manually inspected and sent back to the translators when needed  2) We manually inspected examples where the source transcript was identical to the translation  3) We measured the perplexity of the translations using a language model trained on a large amount of clean monolingual data BIBREF14  We manually inspected examples where the translation had a high perplexity and sent them back to translators accordingly  4) We computed the ratio of English characters in the translations  We manually inspected examples with a low ratio and sent them back to translators accordingly  5) Finally, we used VizSeq BIBREF16 to calculate similarity scores between transcripts and translations based on LASER cross-lingual sentence embeddings BIBREF17  Samples with low scores were manually inspected and sent back for translation when needed 
We also sanity check the overlaps of train, development and test sets in terms of transcripts and voice clips (via MD5 file hashing), and confirm they are totally disjoint 
Data Collection and Processing ::: Tatoeba (TT)
Tatoeba (TT) is a community built language learning corpus having sentences aligned across multiple languages with the corresponding speech partially available  Its sentences are on average shorter than those in CoVoST (see also Table TABREF2) given the original purpose of language learning  Sentences in TT are licensed under CC BY 2 0 FR and part of the speeches are available under various CC licenses 
We construct an evaluation set from TT (for French, German, Dutch, Russian and Spanish) as a complement to CoVoST development and test sets  We collect (speech, transcript, English translation) triplets for the 5 languages and do not include those whose speech has a broken URL or is not CC licensed  We further filter these samples by sentence lengths (minimum 4 words including punctuations) to reduce the portion of short sentences  This makes the resulting evaluation set closer to real-world scenarios and more challenging 
We run the same quality checks for TT as for CoVoST but we do not find poor quality translations according to our criteria  Finally, we report the overlap between CoVo transcripts and TT sentences in Table TABREF5  We found a minimal overlap, which makes the TT evaluation set a suitable additional test set when training on CoVoST 
Data Analysis ::: Basic Statistics
Basic statistics for CoVoST and TT are listed in Table TABREF2 including (unique) sentence counts, speech durations, speaker demographics (partially available) as well as vocabulary and token statistics (based on Moses-tokenized sentences by sacreMoses) on both transcripts and translations  We see that CoVoST has over 327 hours of German speeches and over 171 hours of French speeches, which, to our knowledge, corresponds to the largest corpus among existing public ST corpora (the second largest is 110 hours BIBREF18 for German and 38 hours BIBREF19 for French)  Moreover, CoVoST has a total of 18 hours of Dutch speeches, to our knowledge, contributing the first public Dutch ST resource  CoVoST also has around 27-hour Russian speeches, 37-hour Italian speeches and 67-hour Persian speeches, which is 1 8 times, 2 5 times and 13 3 times of the previous largest public one BIBREF7  Most of the sentences (transcripts) in CoVoST are covered by multiple speakers with potentially different accents, resulting in a rich diversity in the speeches
Context 3: Introduction
While most NLP resources are English-specific, there have been several recent efforts to build multilingual benchmarks  One possibility is to collect and annotate data in multiple languages separately BIBREF0, but most existing datasets have been created through translation BIBREF1, BIBREF2  This approach has two desirable properties: it relies on existing professional translation services rather than requiring expertise in multiple languages, and it results in parallel evaluation sets that offer a meaningful measure of the cross-lingual transfer gap of different models  The resulting multilingual datasets are generally used for evaluation only, relying on existing English datasets for training 
Closely related to that, cross-lingual transfer learning aims to leverage large datasets available in one language—typically English—to build multilingual models that can generalize to other languages  Previous work has explored 3 main approaches to that end: machine translating the test set into English and using a monolingual English model (Translate-Test), machine translating the training set into each target language and training the models on their respective languages (Translate-Train), or using English data to fine-tune a multilingual model that is then transferred to the rest of languages (Zero-Shot) 
The dataset creation and transfer procedures described above result in a mixture of original, human translated and machine translated data when dealing with cross-lingual models  In fact, the type of text a system is trained on does not typically match the type of text it is exposed to at test time: Translate-Test systems are trained on original data and evaluated on machine translated test sets, Zero-Shot systems are trained on original data and evaluated on human translated test sets, and Translate-Train systems are trained on machine translated data and evaluated on human translated test sets 
Despite overlooked to date, we show that such mismatch has a notable impact in the performance of existing cross-lingual models  By using back-translation BIBREF3 to paraphrase each training instance, we obtain another English version of the training set that better resembles the test set, obtaining substantial improvements for the Translate-Test and Zero-Shot approaches in cross-lingual Natural Language Inference (NLI)  While improvements brought by machine translation have previously been attributed to data augmentation BIBREF4, we reject this hypothesis and show that the phenomenon is only present in translated test sets, but not in original ones  Instead, our analysis reveals that this behavior is caused by subtle artifacts arising from the translation process itself  In particular, we show that translating different parts of each instance separately (e g  the premise and the hypothesis in NLI) can alter superficial patterns in the data (e g  the degree of lexical overlap between them), which severely affects the generalization ability of current models  Based on the gained insights, we improve the state-of-the-art in XNLI, and show that some previous findings need to be reconsidered in the light of this phenomenon 
Related work ::: Cross-lingual transfer learning 
Current cross-lingual models work by pre-training multilingual representations using some form of language modeling, which are then fine-tuned on the relevant task and transferred to different languages  Some authors leverage parallel data to that end BIBREF5, BIBREF6, but training a model akin to BERT BIBREF7 on the combination of monolingual corpora in multiple languages is also effective BIBREF8  Closely related to our work, BIBREF4 showed that replacing segments of the training data with their translation during fine-tuning is helpful  However, they attribute this behavior to a data augmentation effect, which we believe should be reconsidered given the new evidence we provide 
Related work ::: Multilingual benchmarks 
Most benchmarks covering a wide set of languages have been created through translation, as it is the case of XNLI BIBREF1 for NLI, PAWS-X BIBREF9 for adversarial paraphrase identification, and XQuAD BIBREF2 and MLQA BIBREF10 for Question Answering (QA)  A notable exception is TyDi QA BIBREF0, a contemporaneous QA dataset that was separately annotated in 11 languages  Other cross-lingual datasets leverage existing multilingual resources, as it is the case of MLDoc BIBREF11 for document classification and Wikiann BIBREF12 for named entity recognition  Concurrent to our work, BIBREF13 combine some of these datasets into a single multilingual benchmark, and evaluate some well-known methods on it 
Related work ::: Annotation artifacts 
Several studies have shown that NLI datasets like SNLI BIBREF14 and MultiNLI BIBREF15 contain spurious patterns that can be exploited to obtain strong results without making real inferential decisions  For instance, BIBREF16 and BIBREF17 showed that a hypothesis-only baseline performs better than chance due to cues on their lexical choice and sentence length  Similarly, BIBREF18 showed that NLI models tend to predict entailment for sentence pairs with a high lexical overlap  Several authors have worked on adversarial datasets to diagnose these issues and provide a more challenging benchmark BIBREF19, BIBREF20, BIBREF21  Besides NLI, other tasks like QA have also been found to be susceptible to annotation artifacts BIBREF22, BIBREF23  While previous work has focused on the monolingual scenario, we show that translation can interfere with these artifacts in multilingual settings 
Related work ::: Translationese 
Translated texts are known to have unique features like simplification, explicitation, normalization and interference, which are refer to as translationese BIBREF24  This phenomenon has been reported to have a notable impact in machine translation evaluation BIBREF25, BIBREF26  For instance, back-translation brings large BLEU gains for reversed test sets (i e  when translationese is on the source side and original text is used as reference), but its effect diminishes in the natural direction BIBREF27  While connected, the phenomenon we analyze is different in that it arises from translation inconsistencies due to the lack of context, and affects cross-lingual transfer learning rather than machine translation 
Experimental design
Our goal is to analyze the effect of both human and machine translation in cross-lingual models  For that purpose, the core idea of our work is to (i) use machine translation to either translate the training set into other languages, or generate English paraphrases of it through back-translation, and (ii) evaluate the resulting systems on original, human translated and machine translated test sets in comparison with systems trained on original data  We next describe the models used in our experiments (§SECREF6), the specific training variants explored (§SECREF8), and the evaluation procedure followed (§SECREF10) 
Experimental design ::: Models and transfer methods
We experiment with two models that are representative of the state-of-the-art in monolingual and cross-lingual pre-training: (i) Roberta BIBREF28, which is an improved version of BERT that uses masked language modeling to pre-train an English Transformer model, and (ii) XLM-R BIBREF8, which is a multilingual extension of the former pre-trained on 100 languages  In both cases, we use the large models released by the authors under the fairseq repository  As discussed next, we explore different variants of the training set to fine-tune each model on different tasks  At test time, we try both machine translating the test set into English (Translate-Test) and, in the case of XLM-R, using the actual test set in the target language (Zero-Shot) 
Experimental design ::: Training variants
We try 3 variants of each training set to fine-tune our models: (i) the original one in English (Orig), (ii) an English paraphrase of it generated through back-translation using Spanish or Finnish as pivot (BT-ES and BT-FI), and (iii) a machine translated version in Spanish or Finnish (MT-ES and MT-FI)  For sentences occurring multiple times in the training set (e g  premises repeated for multiple hypotheses), we use the exact same translation for all occurrences, as our goal is to understand the inherent effect of translation rather than its potential application as a data augmentation method 
In order to train the machine translation systems for MT-XX and BT-XX, we use the big Transformer model BIBREF29 with the same settings as BIBREF30 and SentencePiece tokenization BIBREF31 with a joint vocabulary of 32k subwords  For English-Spanish, we train for 10 epochs on all parallel data from WMT 2013 BIBREF32 and ParaCrawl v5 0 BIBREF33  For English-Finnish, we train for 40 epochs on Europarl and Wiki Titles from WMT 2019 BIBREF34, ParaCrawl v5 0, and DGT, EUbookshop and TildeMODEL from OPUS BIBREF35  In both cases, we remove sentences longer than 250 tokens, with a source/target ratio exceeding 1 5, or for which langid py BIBREF36 predicts a different language, resulting in a final corpus size of 48M and 7M sentence pairs, respectively  We use sampling decoding with a temperature of 0 5 for inference, which produces more diverse translations than beam search BIBREF37 and performed better in our preliminary experiments 
Experimental design ::: Tasks and evaluation procedure
We use the following tasks for our experiments:
Experimental design ::: Tasks and evaluation procedure ::: Natural Language Inference (NLI) 
Given a premise and a hypothesis, the task is to determine whether there is an entailment, neutral or contradiction relation between them  We fine-tune our models on MultiNLI BIBREF15 for 10 epochs using the same settings as BIBREF28  In most of our experiments, we evaluate on XNLI BIBREF1, which comprises 2490 development and 5010 test instances in 15 languages  These were originally annotated in English, and the resulting premises and hypotheses were independently translated into the rest of the languages by professional translators  For the Translate-Test approach, we use the machine translated versions from the authors  Following BIBREF8, we select the best epoch checkpoint according to the average accuracy in the development set 
Experimental design ::: Tasks and evaluation procedure ::: Question Answering (QA) 
Given a context paragraph and a question, the task is to identify the span answering the question in the context  We fine-tune our models on SQuAD v1 1 BIBREF38 for 2 epochs using the same settings as BIBREF28, and report test results for the last epoch  We use two datasets for evaluation: XQuAD BIBREF2, a subset of the SQuAD development set translated into 10 other languages, and MLQA BIBREF10 a dataset consisting of parallel context paragraphs plus the corresponding questions annotated in English and translated into 6 other languages  In both cases, the translation was done by professional translators at the document level (i e  when translating a question, the text answering it was also shown)  For our BT-XX and MT-XX variants, we translate the context paragraph and the questions independently, and map the answer spans using the same procedure as BIBREF39  For the Translate-Test approach, we use the official machine translated versions of MLQA, run inference over them, and map the predicted answer spans back to the target language 
Both for NLI and QA, we run each system 5 times with different random seeds and report the average results  Space permitting, we also report the standard deviation across the 5 runs 
NLI experiments
We next discuss our main results in the XNLI development set (§SECREF15, §SECREF16), run additional experiments to better understand the behavior of our different variants (§SECREF17, §SECREF22, §SECREF25), and compare our results to previous work in the XNLI test set (§SECREF30) 
NLI experiments ::: Translate-Test results
We start by analyzing XNLI development results for Translate-Test  Recall that, in this approach, the test set is machine translated into English, but training is typically done on original English data  Our BT-ES and BT-FI variants close this gap by training on a machine translated English version of the training set generated through back-translation  As shown in Table TABREF9, this brings substantial gains for both Roberta and XLM-R, with an average improvement of 4 6 points in the best case

ground_truth: The authors showed few tweets where neither and implicit hatred content exist but the model was able to discriminate

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How is the quality of the data empirically evaluated? 
Context 2:  For example, there are over 1,000 speakers and over 10 accents in the French and German development / test sets  This enables good coverage of speech variations in both model training and evaluation 
Data Analysis ::: Speaker Diversity
As we can see from Table TABREF2, CoVoST is diversified with a rich set of speakers and accents  We further inspect the speaker demographics in terms of sample distributions with respect to speaker counts, accent counts and age groups, which is shown in Figure FIGREF6, FIGREF7 and FIGREF8  We observe that for 8 of the 11 languages, at least 60% of the sentences (transcripts) are covered by multiple speakers  Over 80% of the French sentences have at least 3 speakers  And for German sentences, even over 90% of them have at least 5 speakers  Similarly, we see that a large portion of sentences are spoken in multiple accents for French, German, Dutch and Spanish  Speakers of each language also spread widely across different age groups (below 20, 20s, 30s, 40s, 50s, 60s and 70s) 
Baseline Results
We provide baselines using the official train-development-test split on the following tasks: automatic speech recognition (ASR), machine translation (MT) and speech translation (ST) 
Baseline Results ::: Experimental Settings ::: Data Preprocessing
We convert raw MP3 audio files from CoVo and TT into mono-channel waveforms, and downsample them to 16,000 Hz  For transcripts and translations, we normalize the punctuation, we tokenize the text with sacreMoses and lowercase it  For transcripts, we further remove all punctuation markers except for apostrophes  We use character vocabularies on all the tasks, with 100% coverage of all the characters  Preliminary experimentation showed that character vocabularies provided more stable training than BPE  For MT, the vocabulary is created jointly on both transcripts and translations  We extract 80-channel log-mel filterbank features, computed with a 25ms window size and 10ms window shift using torchaudio  The features are normalized to 0 mean and 1 0 standard deviation  We remove samples having more than 3,000 frames or more than 256 characters for GPU memory efficiency (less than 25 samples are removed for all languages) 
Baseline Results ::: Experimental Settings ::: Model Training
Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing  For MT, we use a Transformer base architecture BIBREF15, but with 3 encoder layers, 3 decoder layers and 0 3 dropout  We use a batch size of 10,000 frames for ASR and ST, and a batch size of 4,000 tokens for MT  We train all models using Fairseq BIBREF20 for up to 200,000 updates  We use SpecAugment BIBREF21 for ASR and ST to alleviate overfitting 
Baseline Results ::: Experimental Settings ::: Inference and Evaluation
We use a beam size of 5 for all models  We use the best checkpoint by validation loss for MT, and average the last 5 checkpoints for ASR and ST  For MT and ST, we report case-insensitive tokenized BLEU BIBREF22 using sacreBLEU BIBREF23  For ASR, we report word error rate (WER) and character error rate (CER) using VizSeq 
Baseline Results ::: Automatic Speech Recognition (ASR)
For simplicity, we use the same model architecture for ASR and ST, although we do not leverage ASR models to pretrain ST model encoders later  Table TABREF18 shows the word error rate (WER) and character error rate (CER) for ASR models  We see that French and German perform the best given they are the two highest resource languages in CoVoST  The other languages are relatively low resource (especially Turkish and Swedish) and the ASR models are having difficulties to learn from this data 
Baseline Results ::: Machine Translation (MT)
MT models take transcripts (without punctuation) as inputs and outputs translations (with punctuation)  For simplicity, we do not change the text preprocessing methods for MT to correct this mismatch  Moreover, this mismatch also exists in cascading ST systems, where MT model inputs are the outputs of an ASR model  Table TABREF20 shows the BLEU scores of MT models  We notice that the results are consistent with what we see from ASR models  For example thanks to abundant training data, French has a decent BLEU score of 29 8/25 4  German doesn't perform well, because of less richness of content (transcripts)  The other languages are low resource in CoVoST and it is difficult to train decent models without additional data or pre-training techniques 
Baseline Results ::: Speech Translation (ST)
CoVoST is a many-to-one multilingual ST corpus  While end-to-end one-to-many and many-to-many multilingual ST models have been explored very recently BIBREF8, BIBREF9, many-to-one multilingual models, to our knowledge, have not  We hence use CoVoST to examine this setting  Table TABREF22 and TABREF23 show the BLEU scores for both bilingual and multilingual end-to-end ST models trained on CoVoST  We observe that combining speeches from multiple languages is consistently bringing gains to low-resource languages (all besides French and German)  This includes combinations of distant languages, such as Ru+Fr, Tr+Fr and Zh+Fr  Moreover, some combinations do bring gains to high-resource language (French) as well: Es+Fr, Tr+Fr and Mn+Fr  We simply provide the most basic many-to-one multilingual baselines here, and leave the full exploration of the best configurations to future work  Finally, we note that for some language pairs, absolute BLEU numbers are relatively low as we restrict model training to the supervised data  We encourage the community to improve upon those baselines, for example by leveraging semi-supervised training 
Baseline Results ::: Multi-Speaker Evaluation
In CoVoST, large portion of transcripts are covered by multiple speakers with different genders, accents and age groups  Besides the standard corpus-level BLEU scores, we also want to evaluate model output variance on the same content (transcript) but different speakers  We hence propose to group samples (and their sentence BLEU scores) by transcript, and then calculate average per-group mean and average coefficient of variation defined as follows:
and
where $G$ is the set of sentence BLEU scores grouped by transcript and $G^{\prime } = \lbrace g | g\in G, |g|>1, \textrm {Mean}(g) > 0 \rbrace $ 
$\textrm {BLEU}_{MS}$ provides a normalized quality score as oppose to corpus-level BLEU or unnormalized average of sentence BLEU  And $\textrm {CoefVar}_{MS}$ is a standardized measure of model stability against different speakers (the lower the better)  Table TABREF24 shows the $\textrm {BLEU}_{MS}$ and $\textrm {CoefVar}_{MS}$ of our ST models on CoVoST test set  We see that German and Persian have the worst $\textrm {CoefVar}_{MS}$ (least stable) given their rich speaker diversity in the test set and relatively small train set (see also Figure FIGREF6 and Table TABREF2)  Dutch also has poor $\textrm {CoefVar}_{MS}$ because of the lack of training data  Multilingual models are consistantly more stable on low-resource languages  Ru+Fr, Tr+Fr, Fa+Fr and Zh+Fr even have better $\textrm {CoefVar}_{MS}$ than all individual languages 
Conclusion
We introduce a multilingual speech-to-text translation corpus, CoVoST, for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  We also provide baseline results, including, to our knowledge, the first end-to-end many-to-one multilingual model for spoken language translation  CoVoST is free to use with a CC0 license, and the additional Tatoeba evaluation samples are also CC-licensed 

ground_truth: Validated transcripts were sent to professional translators., various sanity checks to the translations,  sanity check the overlaps of train, development and test sets

computed sentence-level BLEU, We manually inspected examples where the source transcript was identical to the translation, measured the perplexity of the translations, computed the ratio of English characters in the translations, calculate similarity scores between transcripts and translations

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What are the languages they use in their experiment?
Context 3:  Quite remarkably, MT-ES and MT-FI also outperform Orig by a substantial margin, and are only 0 8 points below their BT-ES and BT-FI counterparts  Recall that, for these two systems, training is done in machine translated Spanish or Finnish, while inference is done in machine translated English  This shows that the loss of performance when generalizing from original data to machine translated data is substantially larger than the loss of performance when generalizing from one language to another 
NLI experiments ::: Zero-Shot results
We next analyze the results for the Zero-Shot approach  In this case, inference is done in the test set in each target language which, in the case of XNLI, was human translated from English  As such, different from the Translate-Test approach, neither training on original data (Orig) nor training on machine translated data (BT-XX and MT-XX) makes use of the exact same type of text that the system is exposed to at test time  However, as shown in Table TABREF9, both BT-XX and MT-XX outperform Orig by approximately 2 points, which suggests that our (back-)translated versions of the training set are more similar to the human translated test sets than the original one  This also provides a new perspective on the Translate-Train approach, which was reported to outperform Orig in previous work BIBREF5: while the original motivation was to train the model on the same language that it is tested on, our results show that machine translating the training set is beneficial even when the target language is different 
NLI experiments ::: Original vs  translated test sets
So as to understand whether the improvements observed so far are limited to translated test sets or apply more generally, we conduct additional experiments comparing translated test sets to original ones  However, to the best of our knowledge, all existing non-English NLI benchmarks were created through translation  For that reason, we build a new test set that mimics XNLI, but is annotated in Spanish rather than English  We first collect the premises from a filtered version of CommonCrawl BIBREF42, taking a subset of 5 websites that represent a diverse set of genres: a newspaper, an economy forum, a celebrity magazine, a literature blog, and a consumer magazine  We then ask native Spanish annotators to generate an entailment, a neutral and a contradiction hypothesis for each premise  We collect a total of 2490 examples using this procedure, which is the same size as the XNLI development set  Finally, we create a human translated and a machine translated English version of the dataset using professional translators from Gengo and our machine translation system described in §SECREF8, respectively  We report results for the best epoch checkpoint on each set 
As shown in Table TABREF18, both BT-XX and MT-XX clearly outperform Orig in all test sets created through translation, which is consistent with our previous results  In contrast, the best results on the original English set are obtained by Orig, and neither BT-XX nor MT-XX obtain any clear improvement on the one in Spanish either  This confirms that the underlying phenomenon is limited to translated test sets  In addition, it is worth mentioning that the results for the machine translated test set in English are slightly better than those for the human translated one, which suggests that the difficulty of the task does not only depend on the translation quality  Finally, it is also interesting that MT-ES is only marginally better than MT-FI in both Spanish test sets, even if it corresponds to the Translate-Train approach, whereas MT-FI needs to Zero-Shot transfer from Finnish into Spanish  This reinforces the idea that it is training on translated data rather than training on the target language that is key in Translate-Train 
NLI experiments ::: Stress tests
In order to better understand how systems trained on original and translated data differ, we run additional experiments on the NLI Stress Tests BIBREF19, which were designed to test the robustness of NLI models to specific linguistic phenomena in English  The benchmark consists of a competence test, which evaluates the ability to understand antonymy relation and perform numerical reasoning, a distraction test, which evaluates the robustness to shallow patterns like lexical overlap and the presence of negation words, and a noise test, which evaluates robustness to spelling errors  Just as with previous experiments, we report results for the best epoch checkpoint in each test set 
As shown in Table TABREF23, Orig outperforms BT-FI and MT-FI on the competence test by a large margin, but the opposite is true on the distraction test  In particular, our results show that BT-FI and MT-FI are less reliant on lexical overlap and the presence of negative words  This feels intuitive, as translating the premise and hypothesis independently—as BT-FI and MT-FI do—is likely to reduce the lexical overlap between them  More generally, the translation process can alter similar superficial patterns in the data, which NLI models are sensitive to (§SECREF2)  This would explain why the resulting models have a different behavior on different stress tests 
NLI experiments ::: Output class distribution
With the aim to understand the effect of the previous phenomenon in cross-lingual settings, we look at the output class distribution of our different models in the XNLI development set  As shown in Table TABREF28, the predictions of all systems are close to the true class distribution in the case of English  Nevertheless, Orig is strongly biased for the rest of languages, and tends to underpredict entailment and overpredict neutral  This can again be attributed to the fact that the English test set is original, whereas the rest are human translated  In particular, it is well-known that NLI models tend to predict entailment when there is a high lexical overlap between the premise and the hypothesis (§SECREF2)  However, the degree of overlap will be smaller in the human translated test sets given that the premise and the hypothesis were translated independently, which explains why entailment is underpredicted  In contrast, BT-FI and MT-FI are exposed to the exact same phenomenon during training, which explains why they are not that heavily affected 
So as to measure the impact of this phenomenon, we explore a simple approach to correct this bias: having fine-tuned each model, we adjust the bias term added to the logit of each class so the model predictions match the true class distribution for each language  As shown in Table TABREF29, this brings large improvements for Orig, but is less effective for BT-FI and MT-FI  This shows that the performance of Orig was considerably hindered by this bias, which BT-FI and MT-FI effectively mitigate 
NLI experiments ::: Comparison with the state-of-the-art
So as to put our results into perspective, we compare our best variant to previous work on the XNLI test set  As shown in Table TABREF31, our method improves the state-of-the-art for both the Translate-Test and the Zero-Shot approaches by 4 3 and 2 8 points, respectively  It also obtains the best overall results published to date, with the additional advantage that the previous state-of-the-art required a machine translation system between English and each of the 14 target languages, whereas our method uses a single machine translation system between English and Finnish (which is not one of the target languages)  While the main goal of our work is not to design better cross-lingual models, but to analyze their behavior in connection to translation, this shows that the phenomenon under study is highly relevant, to the extent that it can be exploited to improve the state-of-the-art 
QA experiments
So as to understand whether our previous findings apply to other tasks besides NLI, we run additional experiments on QA  As shown in Table TABREF32, BT-FI and BT-ES do indeed outperform Orig for the Translate-Test approach on MLQA  The improvement is modest, but very consistent across different languages, models and runs  The results for MT-ES and MT-FI are less conclusive, presumably because mapping the answer spans across languages might introduce some noise  In contrast, we do not observe any clear improvement for the Zero-Shot approach on this dataset  Our XQuAD results in Table TABREF33 are more positive, but still inconclusive 
These results can partly be explained by the translation procedure used to create the different benchmarks: the premises and hypotheses of XNLI were translated independently, whereas the questions and context paragraphs of XQuAD were translated together  Similarly, MLQA made use of parallel contexts, and translators were shown the sentence containing each answer when translating the corresponding question  As a result, one can expect both QA benchmarks to have more consistent translations than XNLI, which would in turn diminish this phenomenon  In contrast, the questions and context paragraphs are independently translated when using machine translation, which explains why BT-ES and BT-FI outperform Orig for the Translate-Test approach  We conclude that the translation artifacts revealed by our analysis are not exclusive to NLI, as they also show up on QA for the Translate-Test approach, but their actual impact can be highly dependent on the translation procedure used and the nature of the task 
Discussion
Our analysis prompts to reconsider previous findings in cross-lingual transfer learning as follows:
Discussion ::: The cross-lingual transfer gap on XNLI was overestimated 
Given the parallel nature of XNLI, accuracy differences across languages are commonly interpreted as the loss of performance when generalizing from English to the rest of languages  However, our work shows that there is another factor that can have a much larger impact: the loss of performance when generalizing from original to translated data  Our results suggest that the real cross-lingual generalization ability of XLM-R is considerably better than what the accuracy numbers in XNLI reflect 
Discussion ::: Overcoming the cross-lingual gap is not what makes Translate-Train work 
The original motivation for Translate-Train was to train the model on the same language it is tested on  However, we show that it is training on translated data, rather than training on the target language, that is key for this approach to outperform Zero-Shot as reported by previous authors 
Discussion ::: Improvements previously attributed to data augmentation should be reconsidered 
The method by BIBREF4 combines machine translated premises and hypotheses in different languages (§SECREF2), resulting in an effect similar to BT-XX and MT-XX  As such, we believe that this method should be analyzed from the point of view of dataset artifacts rather than data augmentation, as the authors do  From this perspective, having the premise and the hypotheses in different languages can reduce the superficial patterns between them, which would explain why this approach is better than using examples in a single language 
Discussion ::: The potential of Translate-Test was underestimated 
The previous best results for Translate-Test on XNLI lagged behind the state-of-the-art by 4 6 points  Our work reduces this gap to only 0 8 points by addressing the underlying translation artifacts  The reason why Translate-Test is more severely affected by this phenomenon is twofold: (i) the effect is doubled by first using human translation to create the test set and then machine translation to translate it back to English, and (ii) Translate-Train was inadvertently mitigating this issue (see above), but equivalent techniques were never applied to Translate-Test 
Discussion ::: Future evaluation should better account for translation artifacts 
The evaluation issues raised by our analysis do not have a simple solution  In fact, while we use the term translation artifacts to highlight that they are an unintended effect of translation that impacts final evaluation, one could also argue that it is the original datasets that contain the artifacts, which translation simply alters or even mitigates  In any case, this is a more general issue that falls beyond the scope of cross-lingual transfer learning, so we argue that it should be carefully controlled when evaluating cross-lingual models  In the absence of more robust datasets, we recommend that future multilingual benchmarks should at least provide consistent test sets for English and the rest of languages  This can be achieved by (i) using original annotations in all languages, (ii) using original annotations in a non-English language and translating them into English and other languages, or (iii) if translating from English, doing so at the document level to minimize translation inconsistencies 
Conclusions
In this paper, we have shown that both human and machine translation can alter superficial patterns in data, which requires reconsidering previous findings in cross-lingual transfer learning  Based on the gained insights, we have improved the state-of-the-art in XNLI for the Translate-Test and Zero-Shot approaches by a substantial margin  Finally, we have shown that the phenomenon is not specific to NLI but also affects QA, although it is less pronounced there thanks to the translation procedure used in the corresponding benchmarks  So as to facilitate similar studies in the future, we release our NLI dataset, which, unlike previous benchmarks, was annotated in a non-English language and human translated into English 
Acknowledgments
We thank Nora Aranberri and Uxoa Iñurrieta for helpful discussion during the development of this work, as well as the rest of our colleagues from the IXA group that worked as annotators for our NLI dataset 
This research was partially funded by a Facebook Fellowship, the Basque Government excellence research group (IT1343-19), the Spanish MINECO (UnsupMT TIN2017‐91692‐EXP MCIU/AEI/FEDER, UE), Project BigKnowledge (Ayudas Fundación BBVA a equipos de investigación científica 2018), and the NVIDIA GPU grant program 

ground_truth: English
French
Spanish
German
Greek
Bulgarian
Russian
Turkish
Arabic
Vietnamese
Thai
Chinese
Hindi
Swahili
Urdu
Finnish

English, Spanish, Finnish