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 is a semicharacter architecture?
Context 1: Introduction
Despite the rapid progress of deep learning techniques on diverse supervised learning tasks, these models remain brittle to subtle shifts in the data distribution  Even when the permissible changes are confined to barely-perceptible perturbations, training robust models remains an open challenge  Following the discovery that imperceptible attacks could cause image recognition models to misclassify examples BIBREF0 , a veritable sub-field has emerged in which authors iteratively propose attacks and countermeasures 
For all the interest in adversarial computer vision, these attacks are rarely encountered outside of academic research  However, adversarial misspellings constitute a longstanding real-world problem  Spammers continually bombard email servers, subtly misspelling words in efforts to evade spam detection while preserving the emails' intended meaning BIBREF1 , BIBREF2   As another example, programmatic censorship on the Internet has spurred communities to adopt similar methods to communicate surreptitiously BIBREF3  
In this paper, we focus on adversarially-chosen spelling mistakes in the context of text classification, addressing the following attack types: dropping, adding, and swapping internal characters within words  These perturbations are inspired by psycholinguistic studies BIBREF4 , BIBREF5 which demonstrated that humans can comprehend text altered by jumbling internal characters, provided that the first and last characters of each word remain unperturbed 
First, in experiments addressing both BiLSTM and fine-tuned BERT models, comprising four different input formats: word-only, char-only, word+char, and word-piece BIBREF6 , we demonstrate that an adversary can degrade a classifier's performance to that achieved by random guessing  This requires altering just two characters per sentence  Such modifications might flip words either to a different word in the vocabulary or, more often, to the out-of-vocabulary token UNK  Consequently, adversarial edits can degrade a word-level model by transforming the informative words to UNK  Intuitively, one might suspect that word-piece and character-level models would be less susceptible to spelling attacks as they can make use of the residual word context  However, our experiments demonstrate that character and word-piece models are in fact more vulnerable  We show that this is due to the adversary's effective capacity for finer grained manipulations on these models  While against a word-level model, the adversary is mostly limited to UNK-ing words, against a word-piece or character-level model, each character-level add, drop, or swap produces a distinct input, providing the adversary with a greater set of options 
Second, we evaluate first-line techniques including data augmentation and adversarial training, demonstrating that they offer only marginal benefits here, e g , a BERT model achieving $90 3$ accuracy on a sentiment classification task, is degraded to $64 1$ by an adversarially-chosen 1-character swap in the sentence, which can only be restored to $69 2$ by adversarial training 
Third (our primary contribution), we propose a task-agnostic defense, attaching a word recognition model that predicts each word in a sentence given a full sequence of (possibly misspelled) inputs  The word recognition model's outputs form the input to a downstream classification model  Our word recognition models build upon the RNN-based semi-character word recognition model due to BIBREF7   While our word recognizers are trained on domain-specific text from the task at hand, they often predict UNK at test time, owing to the small domain-specific vocabulary  To handle unobserved and rare words, we propose several backoff strategies including falling back on a generic word recognizer trained on a larger corpus  Incorporating our defenses, BERT models subject to 1-character attacks are restored to $88 3$ , $81 1$ , $78 0$ accuracy for swap, drop, add attacks respectively, as compared to $69 2$ , $63 6$ , and $50 0$ for adversarial training
Fourth, we offer a detailed qualitative analysis, demonstrating that a low word error rate alone is insufficient for a word recognizer to confer robustness on the downstream task  Additionally, we find that it is important that the recognition model supply few degrees of freedom to an attacker  We provide a metric to quantify this notion of sensitivity in word recognition models and study its relation to robustness empirically  Models with low sensitivity and word error rate are most robust 
Related Work
Several papers address adversarial attacks on NLP systems  Changes to text, whether word- or character-level, are all perceptible, raising some questions about what should rightly be considered an adversarial example BIBREF8 , BIBREF9   BIBREF10 address the reading comprehension task, showing that by appending distractor sentences to the end of stories from the SQuAD dataset BIBREF11 , they could cause models to output incorrect answers  Inspired by this work, BIBREF12 demonstrate an attack that breaks entailment systems by replacing a single word with either a synonym or its hypernym  Recently, BIBREF13 investigated the problem of producing natural-seeming adversarial examples, noting that adversarial examples in NLP are often ungrammatical BIBREF14  
In related work on character-level attacks, BIBREF8 , BIBREF15 explored gradient-based methods to generate string edits to fool classification and translation systems, respectively  While their focus is on efficient methods for generating adversaries, ours is on improving the worst case adversarial performance  Similarly, BIBREF9 studied how synthetic and natural noise affects character-level machine translation  They considered structure invariant representations and adversarial training as defenses against such noise  Here, we show that an auxiliary word recognition model, which can be trained on unlabeled data, provides a strong defense 
Spelling correction BIBREF16 is often viewed as a sub-task of grammatical error correction BIBREF17 , BIBREF18   Classic methods rely on a source language model and a noisy channel model to find the most likely correction for a given word BIBREF19 , BIBREF20   Recently, neural techniques have been applied to the task BIBREF7 , BIBREF21 , which model the context and orthography of the input together  Our work extends the ScRNN model of BIBREF7  
Robust Word Recognition
To tackle character-level adversarial attacks, we introduce a simple two-stage solution, placing a word recognition model ( $W$ ) before the downstream classifier ( $C$ )  Under this scheme, all inputs are classified by the composed model $C \circ W$   This modular approach, with $W$ and $C$ trained separately, offers several benefits: (i) we can deploy the same word recognition model for multiple downstream classification tasks/models; and (ii) we can train the word recognition model with larger unlabeled corpora 
Against adversarial mistakes, two important factors govern the robustness of this combined model: $W$ 's accuracy in recognizing misspelled words and $W$ 's sensitivity to adversarial perturbations on the same input  We discuss these aspects in detail below 
ScRNN with Backoff
We now describe semi-character RNNs for word recognition, explain their limitations, and suggest techniques to improve them 
Inspired by the psycholinguistic studies BIBREF5 , BIBREF4 , BIBREF7 proposed a semi-character based RNN (ScRNN) that processes a sentence of words with misspelled characters, predicting the correct words at each step  Let $s = \lbrace w_1, w_2, \dots , w_n\rbrace $ denote the input sentence, a sequence of constituent words $w_i$   Each input word ( $w_i$ ) is represented by concatenating (i) a one hot vector of the first character ( $\mathbf {w_{i1}}$ ); (ii) a one hot representation of the last character ( $\mathbf {w_{il}}$ , where $l$ is the length of word $w_i$ ); and (iii) a bag of characters representation of the internal characters ( $\sum _{j=2}^{l-1}\mathbf {w_{ij}})$   ScRNN treats the first and the last characters individually, and is agnostic to the ordering of the internal characters  Each word, represented accordingly, is then fed into a BiLSTM cell  At each sequence step, the training target is the correct corresponding word (output dimension equal to vocabulary size), and the model is optimized with cross-entropy loss 
While BIBREF7 demonstrate strong word recognition performance, a drawback of their evaluation setup is that they only attack and evaluate on the subset of words that are a part of their training vocabulary  In such a setting, the word recognition performance is unreasonably dependent on the chosen vocabulary size  In principle, one can design models to predict (correctly) only a few chosen words, and ignore the remaining majority and still reach 100% accuracy  For the adversarial setting, rare and unseen words in the wild are particularly critical, as they provide opportunities for the attackers  A reliable word-recognizer should handle these cases gracefully  Below, we explore different ways to back off when the ScRNN predicts UNK (a frequent outcome for rare and unseen words):
Pass-through: word-recognizer passes on the (possibly misspelled) word as is 
Backoff to neutral word: Alternatively, noting that passing $\colorbox {gray!20}{\texttt {UNK}}$ -predicted words through unchanged exposes the downstream model to potentially corrupted text, we consider backing off to a neutral word like `a', which has a similar distribution across classes 
Backoff to background model: We also consider falling back upon a more generic word recognition model trained upon a larger, less-specialized corpus whenever the foreground word recognition model predicts UNK  Figure 1 depicts this scenario pictorially 
Empirically, we find that the background model (by itself) is less accurate, because of the large number of words it is trained to predict  Thus, it is best to train a precise foreground model on an in-domain corpus and focus on frequent words, and then to resort to a general-purpose background model for rare and unobserved words  Next, we delineate our second consideration for building robust word-recognizers 
Model Sensitivity
In computer vision, an important factor determining the success of an adversary is the norm constraint on the perturbations allowed to an image ( $|| \bf x - \bf x^{\prime }||_{\infty } < \epsilon $ )  Higher values of $\epsilon $ lead to a higher chance of mis-classification for at least one $\bf x^{\prime }$   Defense methods such as quantization BIBREF22 and thermometer encoding BIBREF23 try to reduce the space of perturbations available to the adversary by making the model invariant to small changes in the input 
In NLP, we often get such invariance for free, e g , for a word-level model, most of the perturbations produced by our character-level adversary lead to an UNK at its input  If the model is robust to the presence of these UNK tokens, there is little room for an adversary to manipulate it  Character-level models, on the other hand, despite their superior performance in many tasks, do not enjoy such invariance  This characteristic invariance could be exploited by an attacker  Thus, to limit the number of different inputs to the classifier, we wish to reduce the number of distinct word recognition outputs that an attacker can induce, not just the number of words on which the model is “fooled”  We denote this property of a model as its sensitivity 
We can quantify this notion for a word recognition system $W$ as the expected number of unique outputs it assigns to a set of adversarial perturbations  Given a sentence $s$ from the set of sentences $\mathcal {S}$ , let $A(s) = {s_1}^{\prime } , {s_2}^{\prime }, \dots , {s_n}^{\prime }$ denote the set of $n$ perturbations to it under attack type $A$ , and let $V$ be the function that maps strings to an input representation for the downstream classifier  For a word level model, $V$ would transform sentences to a sequence of word ids, mapping OOV words to the same UNK ID  Whereas, for a char (or word+char, word-piece) model, $V$ would map inputs to a sequence of character IDs  Formally, sensitivity is defined as
$$S_{W,V}^A=\mathbb {E}_{s}\left[\frac{\#_{u}(V \circ W({s_1}^{\prime }), \dots , V \circ W({s_n}^{\prime }))}{n}\right] ,$$   (Eq  12)
where $V \circ W (s_i)$ returns the input representation (of the downstream classifier) for the output string produced by the word-recognizer $W$ using $s_i$ and $\#_{u}(\cdot )$ counts the number of unique arguments 
Intuitively, we expect a high value of $S_{W, V}^A$ to lead to a lower robustness of the downstream classifier, since the adversary has more degrees of freedom to attack the classifier  Thus, when using word recognition as a defense, it is prudent to design a low sensitivity system with a low error rate  However, as we will demonstrate, there is often a trade-off between sensitivity and error rate 
Synthesizing Adversarial Attacks
Suppose we are given a classifier $C: \mathcal {S} \rightarrow \mathcal {Y}$ which maps natural language sentences $s \in \mathcal {S}$ to a label from a predefined set $y \in \mathcal {Y}$   An adversary for this classifier is a function $A$ which maps a sentence $s$ to its perturbed versions $\lbrace s^{\prime }_1, s^{\prime }_2, \ldots , s^{\prime }_{n}\rbrace $ such that each $s^{\prime }_i$ is close to $s$ under some notion of distance between sentences  We define the robustness of classifier $C$ to the adversary $A$ as:
$$R_{C,A} = \mathbb {E}_s \left[\min _{s^{\prime } \in A(s)} \mathbb {1}[C(s^{\prime }) = y]\right],$$   (Eq  14)
where $y$ represents the ground truth label for $s$   In practice, a real-world adversary may only be able to query the classifier a few times, hence $R_{C,A}$ represents the worst-case adversarial performance of $C$   Methods for generating adversarial examples, such as HotFlip BIBREF8 , focus on efficient algorithms for searching the $\min $ above  Improving $R_{C,A}$ would imply better robustness against all these methods 
We explore adversaries which perturb sentences with four types of character-level edits:
(1) Swap: swapping two adjacent internal characters of a word  (2) Drop: removing an internal character of a word  (3) Keyboard: substituting an internal character with adjacent characters of QWERTY keyboard (4) Add: inserting a new character internally in a word  In line with the psycholinguistic studies BIBREF5 , BIBREF4 , to ensure that the perturbations do not affect human ability to comprehend the sentence, we only allow the adversary to edit the internal characters of a word, and not edit stopwords or words shorter than 4 characters 
For 1-character attacks, we try all possible perturbations listed above until we find an adversary that flips the model prediction  For 2-character attacks, we greedily fix the edit which had the least confidence among 1-character attacks, and then try all the allowed perturbations on the remaining words  Higher order attacks can be performed in a similar manner  The greedy strategy reduces the computation required to obtain higher order attacks, but also means that the robustness score is an upper bound on the true robustness of the classifier 
Experiments and Results
In this section, we first discuss our experiments on the word recognition systems 
Word Error Correction
Data: We evaluate the spell correctors from § "Robust Word Recognition" on movie reviews from the Stanford Sentiment Treebank (SST) BIBREF24   The SST dataset consists of 8544 movie reviews, with a vocabulary of over 16K words  As a background corpus, we use the IMDB movie reviews BIBREF25 , which contain 54K movie reviews, and a vocabulary of over 78K words  The two datasets do not share any reviews in common  The spell-correction models are evaluated on their ability to correct misspellings  The test setting consists of reviews where each word (with length $\ge 4$ , barring stopwords) is attacked by one of the attack types (from swap, add, drop and keyboard attacks)  In the all attack setting, we mix all attacks by randomly choosing one for each word  This most closely resembles a real world attack setting 
In addition to our word recognition models, we also compare to After The Deadline (ATD), an open-source spell corrector  We found ATD to be the best freely-available corrector  We refer the reader to BIBREF7 for comparisons of ScRNN to other anonymized commercial spell checkers 
For the ScRNN model, we use a single-layer Bi-LSTM with a hidden dimension size of 50  The input representation consists of 198 dimensions, which is thrice the number of unique characters (66) in the vocabulary  We cap the vocabulary size to 10K words, whereas we use the entire vocabulary of 78470 words when we backoff to the background model  For training these networks, we corrupt the movie reviews according to all attack types, i e , applying one of the 4 attack types to each word, and trying to reconstruct the original words via cross entropy loss 
We calculate the word error rates (WER) of each of the models for different attacks and present our findings in Table 2   Note that ATD incorrectly predicts $11 2$ words for every 100 words (in the `all' setting), whereas, all of the backoff variations of the ScRNN reconstruct better  The most accurate variant involves backing off to the background model, resulting in a low error rate of $6 9\%$ , leading to the best performance on word recognition  This is a $32\%$ relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy  We can attribute the improved performance to the fact that there are $5 25\%$ words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus  Notably, only training on the larger background corpus does worse, at $8 7\%$ , since the distribution of word frequencies is different in the background corpus compared to the foreground corpus 
Robustness to adversarial attacks
We use sentiment analysis and paraphrase detection as downstream tasks, as for these two tasks, 1-2 character edits do not change the output labels 
For sentiment classification, we systematically study the effect of character-level adversarial attacks on two architectures and four different input formats  The first architecture encodes the input sentence into a sequence of embeddings, which are then sequentially processed by a BiLSTM  The first and last states of the BiLSTM are then used by the softmax layer to predict the sentiment of the input  We consider three input formats for this architecture: (1) Word-only: where the input words are encoded using a lookup table; (2) Char-only: where the input words are encoded using a separate single-layered BiLSTM over their characters; and (3) Word $+$ Char: where the input words are encoded using a concatenation of (1) and (2)  
The second architecture uses the fine-tuned BERT model BIBREF26 , with an input format of word-piece tokenization  This model has recently set a new state-of-the-art on several NLP benchmarks, including the sentiment analysis task we consider here  All models are trained and evaluated on the binary version of the sentence-level Stanford Sentiment Treebank BIBREF24 dataset with only positive and negative reviews 
We also consider the task of paraphrase detection  Here too, we make use of the fine-tuned BERT BIBREF26 , which is trained and evaluated on the Microsoft Research Paraphrase Corpus (MRPC) BIBREF27  
Two common methods for dealing with adversarial examples include: (1) data augmentation (DA) BIBREF28 ; and (2) adversarial training (Adv) BIBREF29   In DA, the trained model is fine-tuned after augmenting the training set with an equal number of examples randomly attacked with a 1-character edit  In Adv, the trained model is fine-tuned with additional adversarial examples (selected at random) that produce incorrect predictions from the current-state classifier  The process is repeated iteratively, generating and adding newer adversarial examples from the updated classifier model, until the adversarial accuracy on dev set stops improving 
In Table 3 , we examine the robustness of the sentiment models under each attack and defense method  In the absence of any attack or defense, BERT (a word-piece model) performs the best ( $90 3\%$ ) followed by word+char models ( $80 5\%$ ), word-only models ( $79 2\%$ ) and then char-only models ( $70 3\%$ )  However, even single-character attacks (chosen adversarially) can be catastrophic, resulting in a significantly degraded performance of $46\%$ , $57\%$ , $59\%$ and $33\%$ , respectively under the `all' setting 
Intuitively, one might suppose that word-piece and character-level models would be more robust to such attacks given they can make use of the remaining context  However, we find that they are the more susceptible  To see why, note that the word `beautiful' can only be altered in a few ways for word-only models, either leading to an UNK or an existing vocabulary word, whereas, word-piece and character-only models treat each unique character combination differently  This provides more variations that an attacker can exploit  Following similar reasoning, add and key attacks pose a greater threat than swap and drop attacks  The robustness of different models can be ordered as word-only $>$ word+char $>$ char-only $\sim $ word-piece, and the efficacy of different attacks as add $>$ key $>$ drop $>$ swap 
Next, we scrutinize the effectiveness of defense methods when faced against adversarially chosen attacks  Clearly from table 3 , DA and Adv are not effective in this case  We observed that despite a low training error, these models were not able to generalize to attacks on newer words at test time  ATD spell corrector is the most effective on keyboard attacks, but performs poorly on other attack types, particularly the add attack strategy 
The ScRNN model with pass-through backoff offers better protection, bringing back the adversarial accuracy within $5\%$ range for the swap attack  It is also effective under other attack classes, and can mitigate the adversarial effect in word-piece models by $21\%$ , character-only models by $19\%$ , and in word, and word+char models by over $4 5\%$   This suggests that the direct training signal of word error correction is more effective than the indirect signal of sentiment classification available to DA and Adv for model robustness 
We observe additional gains by using background models as a backoff alternative, because of its lower word error rate (WER), especially, under the swap and drop attacks  However, these gains do not consistently translate in all other settings, as lower WER is necessary but not sufficient  Besides lower error rate, we find that a solid defense should furnish the attacker the fewest options to attack, i e  it should have a low sensitivity 
As we shall see in section § "Understanding Model Sensitivity" , the backoff neutral variation has the lowest sensitivity due to mapping UNK predictions to a fixed neutral word  Thus, it results in the highest robustness on most of the attack types for all four model classes 
Table 4 shows the accuracy of BERT on 200 examples from the dev set of the MRPC paraphrase detection task under various attack and defense settings  We re-trained the ScRNN model variants on the MRPC training set for these experiments  Again, we find that simple 1-2 character attacks can bring down the accuracy of BERT significantly ( $89\%$ to $31\%$ )  Word recognition models can provide an effective defense, with both our pass-through and neutral variants recovering most of the accuracy  While the neutral backoff model is effective on 2-char attacks, it hurts performance in the no attack setting, since it incorrectly modifies certain correctly spelled entity names  Since the two variants are already effective, we did not train a background model for this task 
Understanding Model Sensitivity
To study model sensitivity, for each sentence, we perturb one randomly-chosen word and replace it with all possible perturbations under a given attack type  The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated)  As described in equation 12 , we count the number of unique predictions from the output sentences  Two corrections are considered unique if they are mapped differently by the downstream classifier 
The neutral backoff variant has the lowest sensitivity (Table 5 )  This is expected, as it returns a fixed neutral word whenever the ScRNN predicts an UNK, therefore reducing the number of unique outputs it predicts  Open vocabulary (i e  char-only, word+char, word-piece) downstream classifiers consider every unique combination of characters differently, whereas word-only classifiers internally treat all out of vocabulary (OOV) words alike  Hence, for char-only, word+char, and word-piece models, the pass-through version is more sensitive than the background variant, as it passes words as is (and each combination is considered uniquely)  However, for word-only models, pass-through is less sensitive as all the OOV character combinations are rendered identical 
Ideally, a preferred defense is one with low sensitivity and word error rate  In practice, however, we see that a low error rate often comes at the cost of sensitivity  We see this trade-off in Figure 2 , where we plot WER and sensitivity on the two axes, and depict the robustness when using different backoff variants  Generally, sensitivity is the more dominant factor out of the two, as the error rates of the considered variants are reasonably low 
We verify if the sentiment (of the reviews) is preserved with char-level attacks  In a human study with 50 attacked (and subsequently misclassified), and 50 unchanged reviews, it was noted that 48 and 49, respectively, preserved the sentiment 
Conclusion
As character and word-piece inputs become commonplace in modern NLP pipelines, it is worth highlighting the vulnerability they add  We show that minimally-doctored attacks can bring down accuracy of classifiers to random guessing  We recommend word recognition as a safeguard against this and build upon RNN-based semi-character word recognizers  We discover that when used as a defense mechanism, the most accurate word recognition models are not always the most robust against adversarial attacks  Additionally, we highlight the need to control the sensitivity of these models to achieve high robustness 
Acknowledgements
The authors are grateful to Graham Neubig, Eduard Hovy, Paul Michel, Mansi Gupta, and Antonios Anastasopoulos for suggestions and feedback 
Context 2: Introduction
In recent years, there has been a movement to leverage social medial data to detect, estimate, and track the change in prevalence of disease  For example, eating disorders in Spanish language Twitter tweets BIBREF0 and influenza surveillance BIBREF1   More recently, social media has been leveraged to monitor social risks such as prescription drug and smoking behaviors BIBREF2 , BIBREF3 , BIBREF4 as well as a variety of mental health disorders including suicidal ideation BIBREF5 , attention deficient hyperactivity disorder BIBREF6 and major depressive disorder BIBREF7   In the case of major depressive disorder, recent efforts range from characterizing linguistic phenomena associated with depression BIBREF8 and its subtypes e g , postpartum depression BIBREF5 , to identifying specific depressive symptoms BIBREF9 , BIBREF10 e g , depressed mood  However, more research is needed to better understand the predictive power of supervised machine learning classifiers and the influence of feature groups and feature sets for efficiently classifying depression-related tweets to support mental health monitoring at the population-level BIBREF11  
This paper builds upon related works toward classifying Twitter tweets representing symptoms of major depressive disorder by assessing the contribution of lexical features (e g , unigrams) and emotion (e g , strongly negative) to classification performance, and by applying methods to eliminate low-value features 
METHODS
Specifically, we conducted a feature ablation study to assess the informativeness of each feature group and a feature elimination study to determine the optimal feature sets for classifying Twitter tweets  We leveraged an existing, annotated Twitter dataset that was constructed based on a hierarchical model of depression-related symptoms BIBREF12 , BIBREF13   The dataset contains 9,473 annotations for 9,300 tweets  Each tweet is annotated as no evidence of depression (e g , “Citizens fear an economic depression") or evidence of depression (e g , “depressed over disappointment")  If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e g , “feeling down in the dumps"), disturbed sleep (e g , “another restless night"), or fatigue or loss of energy (e g , “the fatigue is unbearable") BIBREF10   For each class, every annotation (9,473 tweets) is binarized as the positive class e g , depressed mood=1 or negative class e g , not depressed mood=0 
Features
Furthermore, this dataset was encoded with 7 feature groups with associated feature values binarized (i e , present=1 or absent=0) to represent potentially informative features for classifying depression-related classes  We describe the feature groups by type, subtype, and provide one or more examples of words representing the feature subtype from a tweet:
lexical features, unigrams, e g , “depressed”;
syntactic features, parts of speech, e g , “cried” encoded as V for verb;
emotion features, emoticons, e g , :( encoded as SAD;
demographic features, age and gender e g , “this semester” encoded as an indicator of 19-22 years of age and “my girlfriend” encoded as an indicator of male gender, respectively;
sentiment features, polarity and subjectivity terms with strengths, e g , “terrible” encoded as strongly negative and strongly subjective;
personality traits, neuroticism e g , “pissed off” implies neuroticism;
LIWC Features, indicators of an individual's thoughts, feelings, personality, and motivations, e g , “feeling” suggestions perception, feeling, insight, and cognitive mechanisms experienced by the Twitter user

ground_truth: A semi-character based RNN (ScRNN) treats the first and last characters individually, and is agnostic to the ordering of the internal characters

processes a sentence of words with misspelled characters, predicting the correct words at each step

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 dataset annotated?
Context 2: 
A more detailed description of leveraged features and their values, including LIWC categories, can be found in BIBREF10  
Based on our prior initial experiments using these feature groups BIBREF10 , we learned that support vector machines perform with the highest F1-score compared to other supervised approaches  For this study, we aim to build upon this work by conducting two experiments: 1) to assess the contribution of each feature group and 2) to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy 
Feature Contribution
Feature ablation studies are conducted to assess the informativeness of a feature group by quantifying the change in predictive power when comparing the performance of a classifier trained with the all feature groups versus the performance without a particular feature group  We conducted a feature ablation study by holding out (sans) each feature group and training and testing the support vector model using a linear kernel and 5-fold, stratified cross-validation  We report the average F1-score from our baseline approach (all feature groups) and report the point difference (+ or -) in F1-score performance observed by ablating each feature set 
By ablating each feature group from the full dataset, we observed the following count of features - sans lexical: 185, sans syntactic: 16,935, sans emotion: 16,954, sans demographics: 16,946, sans sentiment: 16,950, sans personality: 16,946, and sans LIWC: 16,832  In Figure 1, compared to the baseline performance, significant drops in F1-scores resulted from sans lexical for depressed mood (-35 points), disturbed sleep (-43 points), and depressive symptoms (-45 points)  Less extensive drops also occurred for evidence of depression (-14 points) and fatigue or loss of energy (-3 points)  In contrast, a 3 point gain in F1-score was observed for no evidence of depression  We also observed notable drops in F1-scores for disturbed sleep by ablating demographics (-7 points), emotion (-5 points), and sentiment (-5 points) features  These F1-score drops were accompanied by drops in both recall and precision  We found equal or higher F1-scores by removing non-lexical feature groups for no evidence of depression (0-1 points), evidence of depression (0-1 points), and depressive symptoms (2 points) 
Unsurprisingly, lexical features (unigrams) were the largest contributor to feature counts in the dataset  We observed that lexical features are also critical for identifying depressive symptoms, specifically for depressed mood and for disturbed sleep  For the classes higher in the hierarchy - no evidence of depression, evidence of depression, and depressive symptoms - the classifier produced consistent F1-scores, even slightly above the baseline for depressive symptoms and minor fluctuations of change in recall and precision when removing other feature groups suggesting that the contribution of non-lexical features to classification performance was limited  However, notable changes in F1-score were observed for the classes lower in the hierarchy including disturbed sleep and fatigue or loss of energy  For instance, changes in F1-scores driven by both recall and precision were observed for disturbed sleep by ablating demographics, emotion, and sentiment features, suggesting that age or gender (“mid-semester exams have me restless”), polarity and subjective terms (“lack of sleep is killing me”), and emoticons (“wide awake :(”) could be important for both identifying and correctly classifying a subset of these tweets 
Feature Elimination
Feature elimination strategies are often taken 1) to remove irrelevant or noisy features, 2) to improve classifier performance, and 3) to reduce training and run times  We conducted an experiment to determine whether we could maintain or improve classifier performances by applying the following three-tiered feature elimination approach:
Reduction We reduced the dataset encoded for each class by eliminating features that occur less than twice in the full dataset 
Selection We iteratively applied Chi-Square feature selection on the reduced dataset, selecting the top percentile of highest ranked features in increments of 5 percent to train and test the support vector model using a linear kernel and 5-fold, stratified cross-validation 
Rank We cumulatively plotted the average F1-score performances of each incrementally added percentile of top ranked features  We report the percentile and count of features resulting in the first occurrence of the highest average F1-score for each class 
All experiments were programmed using scikit-learn 0 18 
The initial matrices of almost 17,000 features were reduced by eliminating features that only occurred once in the full dataset, resulting in 5,761 features  We applied Chi-Square feature selection and plotted the top-ranked subset of features for each percentile (at 5 percent intervals cumulatively added) and evaluated their predictive contribution using the support vector machine with linear kernel and stratified, 5-fold cross validation 
In Figure 2, we observed optimal F1-score performance using the following top feature counts: no evidence of depression: F1: 87 (15th percentile, 864 features), evidence of depression: F1: 59 (30th percentile, 1,728 features), depressive symptoms: F1: 55 (15th percentile, 864 features), depressed mood: F1: 39 (55th percentile, 3,168 features), disturbed sleep: F1: 46 (10th percentile, 576 features), and fatigue or loss of energy: F1: 72 (5th percentile, 288 features) (Figure 1)  We note F1-score improvements for depressed mood from F1: 13 at the 1st percentile to F1: 33 at the 20th percentile 
We observed peak F1-score performances at low percentiles for fatigue or loss of energy (5th percentile), disturbed sleep (10th percentile) as well as depressive symptoms and no evidence of depression (both 15th percentile) suggesting fewer features are needed to reach optimal performance  In contrast, peak F1-score performances occurred at moderate percentiles for evidence of depression (30th percentile) and depressed mood (55th percentile) suggesting that more features are needed to reach optimal performance  However, one notable difference between these two classes is the dramatic F1-score improvements for depressed mood i e , 20 point increase from the 1st percentile to the 20th percentile compared to the more gradual F1-score improvements for evidence of depression i e , 11 point increase from the 1st percentile to the 20th percentile  This finding suggests that for identifying depressed mood a variety of features are needed before incremental gains are observed 
RESULTS
From our annotated dataset of Twitter tweets (n=9,300 tweets), we conducted two feature studies to better understand the predictive power of several feature groups for classifying whether or not a tweet contains no evidence of depression (n=6,829 tweets) or evidence of depression (n=2,644 tweets)  If there was evidence of depression, we determined whether the tweet contained one or more depressive symptoms (n=1,656 tweets) and further classified the symptom subtype of depressed mood (n=1,010 tweets), disturbed sleep (n=98 tweets), or fatigue or loss of energy (n=427 tweets) using support vector machines  From our prior work BIBREF10 and in Figure 1, we report the performance for prediction models built by training a support vector machine using 5-fold, stratified cross-validation with all feature groups as a baseline for each class  We observed high performance for no evidence of depression and fatigue or loss of energy and moderate performance for all remaining classes 
Discussion
We conducted two feature study experiments: 1) a feature ablation study to assess the contribution of feature groups and 2) a feature elimination study to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy 
Future Work
Our next step is to address the classification of rarer depressive symptoms suggestive of major depressive disorder from our dataset and hierarchy including inappropriate guilt, difficulty concentrating, psychomotor agitation or retardation, weight loss or gain, and anhedonia BIBREF15 , BIBREF16   We are developing a population-level monitoring framework designed to estimate the prevalence of depression (and depression-related symptoms and psycho-social stressors) over millions of United States-geocoded tweets  Identifying the most discriminating feature sets and natural language processing classifiers for each depression symptom is vital for this goal 
Conclusions
In summary, we conducted two feature study experiments to assess the contribution of feature groups and to determine the optimal percentile of top ranked features for classifying Twitter tweets in the depression schema hierarchy  From these experiments, we conclude that simple lexical features and reduced feature sets can produce comparable results to the much larger feature dataset 
Acknowledgments
Research reported in this publication was supported by the National Library of Medicine of the [United States] National Institutes of Health under award numbers K99LM011393 and R00LM011393  This study was granted an exemption from review by the University of Utah Institutional Review Board (IRB 00076188)  Note that in order to protect tweeter anonymity, we have not reproduced tweets verbatim  Example tweets shown were generated by the researchers as exemplars only  Finally, we would like to thank the anonymous reviewers of this paper for their valuable comments 
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  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: no evidence of depression, depressed mood, disturbed sleep, fatigue or loss of energy

The annotations are based on evidence of depression and further annotated by the depressive symptom if there is evidence of depression

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 4:  The two word representations INLINEFORM3 and INLINEFORM4 are projected to a space where their element wise difference can be used to model their dissimilarity and softmax applied on them can produce high probability to more dissimilar word pairs 
It is good to note that conflict suffers from the same limitation that attention suffers from  This is when a pair of sentences are highly matching especially with multiple associations  But when the two methods work together, each compensates for the other's shortcomings 
Combination of attention and conflict
We used two weighted representations of INLINEFORM0 using weights of attention and conflict as computed in Eqn  (4) and (8) respectively  Our final representation of a word representation INLINEFORM1 conditioned on INLINEFORM2 can be expressed as: DISPLAYFORM0
where A and C denote that they are from attention and conflict models respectively 
Relation to Multi-Head attention
Multi-head attention, as introduced in BIBREF3 , computes multiple identical attention mechanism parallelly on multiple linear projections of same inputs  The parameters of each attention and projections are different in each head  Finally, they concatenate all the attentions which is similar to how we concatenate conflict and attention  However, they use dot-product to compute each of the attention 
Our combined model that contains both attention and conflict can be thought of as a 2-head attention model but both heads are different  Our conflict head explicitly captures difference between the inputs 
Visualizing attention and conflict
We observe how our conflict model learns the dissimilarities between word representations  We achieve that by visualizing the heatmap of the weight matrix INLINEFORM0 for both attention and conflict from eqns  (3) and (8)  While attention successfully learns the alignments, conflict matrix also shows that our approach models the contradicting associations like "animal" and "lake" or "australia" and "world"  These two associations are the unique pairs which are instrumental in determining that the two queries are not similar 
The model
We create two models both of which constitutes of three main parts: encoder, interaction and classifier and take two sequences as input  Except interaction, all the other parts are exactly identical between the two models  The encoder is shared among the sequences simply uses two stacked GRU layers  The interaction part consists of only attention for one model while for the another one it consists of attention and conflict combined as shown in (eqn 11)   The classifier part is simply stacked fully-connected layers  Figure 3 shows a block diagram of how our model looks like 
Task 1: Quora Duplicate Question Pair Detection
The dataset includes pairs of questions labelled as 1 or 0 depending on whether a pair is duplicate or not respectively  This is a popular pair-level classification task on which extensive work has already been done before like BIBREF7 , BIBREF8   For this task, we make the output layer of our model to predict two probabilities for non-duplicate and duplicate  We sample the data from the original dataset so that it contains equal positive and negative classes  Original dataset has some class imbalance but for sake simplicity we don't consider it  The final data that we use has roughly 400,000 question pairs and we split this data into train and test using 8:2 ratio 
We train all our models for roughly 2 epochs with a batch size of 64  We use a hidden dimension of 150 throughout the model  The embedding layer uses ELMO BIBREF9 which has proven to be very useful in various downstream language understanding tasks  Our FC layers consists of four dense layers with INLINEFORM0 activation after each layer  The dropout rate is kept as 0 2 for every recurrent and FC linear layers  We use Adam optimizer in our experiment with epsilon=1e-8, beta=0 9 and learning rate=1e-3 
Task 2: Ranking questions in Bing's People Also Ask
People Also Ask is a feature in Bing search result page where related questions are recommended to the user  User may click on a question to view the answer  Clicking is a positive feedback that shows user's interest in the question  We use this click logs to build a question classifier using the same model in Figure 3  The problem statement is very similar to BIBREF10 where they use logistic regression to predict whether an user would click on ad  Our goal is to classify if a question is potential high-click question or not for a given query  For this, we first create a labelled data set using the click logs where any question having CTR lower than 0 3 is labelled as 0 and a question having CTR more than 0 7 as 1 
Our final data resembles that of a pair-level classifier, as in Task 1, where user query and candidate questions are input  With these data set, we train a binary classifier to detect high-click and low-click questions 
Quantitative Analysis
For both tasks, we compute classification accuracy using three model variants and report the results in Table 1 and Table 2  We observe that model with both attention and conflict combined gives the best results 
We also show the training loss curve for both the models having attention and attention combined with conflict respectively  Figure 4 and 5 shows these curves for Task 1 and Task 2 respectively  The curves are smoothed using moving average having an window size of 8  We notice that the conflict model has much steeper slope and converges to a much better minima in both the tasks  It can also be noticed that in the training procedure for the model which has both attention and conflict, the updates are much smoother 
Qualitative Comparison
We also show qualitative results where we can observe that our model with attention and conflict combined does better on cases where pairs are non-duplicate and has very small difference  We have observed that the conflict model is very sensitive to even minor differences and compensates in such cases where attention poses high bias towards similarities already there in the sequences 
Sequence 1: What are the best ways to learn French ?
Sequence 2: How do I learn french genders ?
Attention only: 1
Attention+Conflict: 0
Ground Truth: 0
Sequence 1: How do I prevent breast cancer ?
Sequence 2: Is breast cancer preventable ?
Attention only: 1
Attention+Conflict: 0
Ground Truth: 0
We provide two examples with predictions from the models with only attention and combination of attention and conflict  Each example is accompanied by the ground truth in our data 
Analyzing the gains
We analyzed the gains in Task 1 which we get from the attention-conflict model in order to ensure that they are not due to randomness in weight initialization or simply additional parameters  We particularly focused on the examples which were incorrectly marked in attention model but correctly in attention-conflict model  We saw that 70% of those cases are the ones where the pair was incorrectly marked as duplicate in the previous model but our combined model correctly marked them as non-duplicate 
Conclusion
In this work, we highlighted the limits of attention especially in cases where two sequences have a contradicting relationship based on the task it performs  To alleviate this problem and further improve the performance, we propose a conflict mechanism that tries to capture how two sequences repel each other  This acts like the inverse of attention and, empirically, we show that how conflict and attention together can improve the performance 
Future research work should be based on alternative design of conflict mechanism using other difference operators other than element wise difference which we use 

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

English, Spanish, Finnish