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 do they define robustness of a model?
Context 1: Introduction
We posses a wealth of prior knowledge about many natural language processing tasks  For example, in text categorization, we know that words such as NBA, player, and basketball are strong indicators of the sports category BIBREF0 , and words like terrible, boring, and messing indicate a negative polarity while words like perfect, exciting, and moving suggest a positive polarity in sentiment classification 
A key problem arisen here, is how to leverage such knowledge to guide the learning process, an interesting problem for both NLP and machine learning communities  Previous studies addressing the problem fall into several lines  First, to leverage prior knowledge to label data BIBREF1 , BIBREF2   Second, to encode prior knowledge with a prior on parameters, which can be commonly seen in many Bayesian approaches BIBREF3 , BIBREF4   Third, to formalise prior knowledge with additional variables and dependencies BIBREF5   Last, to use prior knowledge to control the distributions over latent output variables BIBREF6 , BIBREF7 , BIBREF8 , which makes the output variables easily interpretable 
However, a crucial problem, which has rarely been addressed, is the bias in the prior knowledge that we supply to the learning model  Would the model be robust or sensitive to the prior knowledge? Or, which kind of knowledge is appropriate for the task? Let's see an example: we may be a baseball fan but unfamiliar with hockey so that we can provide a few number of feature words of baseball, but much less of hockey for a baseball-hockey classification task  Such prior knowledge may mislead the model with heavy bias to baseball  If the model cannot handle this situation appropriately, the performance may be undesirable 
In this paper, we investigate into the problem in the framework of Generalized Expectation Criteria BIBREF7   The study aims to reveal the factors of reducing the sensibility of the prior knowledge and therefore to make the model more robust and practical  To this end, we introduce auxiliary regularization terms in which our prior knowledge is formalized as distribution over output variables  Recall the example just mentioned, though we do not have enough knowledge to provide features for class hockey, it is easy for us to provide some neutral words, namely words that are not strong indicators of any class, like player here  As one of the factors revealed in this paper, supplying neutral feature words can boost the performance remarkably, making the model more robust 
More attractively, we do not need manual annotation to label these neutral feature words in our proposed approach 
More specifically, we explore three regularization terms to address the problem: (1) a regularization term associated with neutral features; (2) the maximum entropy of class distribution regularization term; and (3) the KL divergence between reference and predicted class distribution  For the first manner, we simply use the most common features as neutral features and assume the neutral features are distributed uniformly over class labels  For the second and third one, we assume we have some knowledge about the class distribution which will be detailed soon later 
To summarize, the main contributions of this work are as follows:
The rest of the paper is structured as follows: In Section 2, we briefly describe the generalized expectation criteria and present the proposed regularization terms  In Section 3, we conduct extensive experiments to justify the proposed methods  We survey related work in Section 4, and summarize our work in Section 5 
Method
We address the robustness problem on top of GE-FL BIBREF0 , a GE method which leverages labeled features as prior knowledge  A labeled feature is a strong indicator of a specific class and is manually provided to the classifier  For example, words like amazing, exciting can be labeled features for class positive in sentiment classification 
Generalized Expectation Criteria
Generalized expectation (GE) criteria BIBREF7 provides us a natural way to directly constrain the model in the preferred direction  For example, when we know the proportion of each class of the dataset in a classification task, we can guide the model to predict out a pre-specified class distribution 
Formally, in a parameter estimation objective function, a GE term expresses preferences on the value of some constraint functions about the model's expectation  Given a constraint function $G({\rm x}, y)$ , a conditional model distribution $p_\theta (y|\rm x)$ , an empirical distribution $\tilde{p}({\rm x})$ over input samples and a score function $S$ , a GE term can be expressed as follows:
$$S(E_{\tilde{p}({\rm x})}[E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]])$$   (Eq  4)
Learning from Labeled Features
Druck et al  ge-fl proposed GE-FL to learn from labeled features using generalized expectation criteria  When given a set of labeled features $K$ , the reference distribution over classes of these features is denoted by $\hat{p}(y| x_k), k \in K$   GE-FL introduces the divergence between this reference distribution and the model predicted distribution $p_\theta (y | x_k)$ , as a term of the objective function:
$$\mathcal {O} = \sum _{k \in K} KL(\hat{p}(y|x_k) || p_\theta (y | x_k)) + \sum _{y,i} \frac{\theta _{yi}^2}{2 \sigma ^2}$$   (Eq  6)
where $\theta _{yi}$ is the model parameter which indicates the importance of word $i$ to class $y$   The predicted distribution $p_\theta (y | x_k)$ can be expressed as follows: $ p_\theta (y | x_k) = \frac{1}{C_k} \sum _{\rm x} p_\theta (y|{\rm x})I(x_k) $
in which $I(x_k)$ is 1 if feature $k$ occurs in instance ${\rm x}$ and 0 otherwise, $C_k = \sum _{\rm x} I(x_k)$ is the number of instances with a non-zero value of feature $k$ , and $p_\theta (y|{\rm x})$ takes a softmax form as follows: $ p_\theta (y|{\rm x}) = \frac{1}{Z(\rm x)}\exp (\sum _i \theta _{yi}x_i)  $
To solve the optimization problem, L-BFGS can be used for parameter estimation 
In the framework of GE, this term can be obtained by setting the constraint function $G({\rm x}, y) = \frac{1}{C_k} \vec{I} (y)I(x_k)$ , where $\vec{I}(y)$ is an indicator vector with 1 at the index corresponding to label $y$ and 0 elsewhere 
Regularization Terms
GE-FL reduces the heavy load of instance annotation and performs well when we provide prior knowledge with no bias  In our experiments, we observe that comparable numbers of labeled features for each class have to be supplied  But as mentioned before, it is often the case that we are not able to provide enough knowledge for some of the classes  For the baseball-hockey classification task, as shown before, GE-FL will predict most of the instances as baseball  In this section, we will show three terms to make the model more robust 
Neutral features are features that are not informative indicator of any classes, for instance, word player to the baseball-hockey classification task  Such features are usually frequent words across all categories  When we set the preference distribution of the neutral features to be uniform distributed, these neutral features will prevent the model from biasing to the class that has a dominate number of labeled features 
Formally, given a set of neutral features $K^{^{\prime }}$ , the uniform distribution is $\hat{p}_u(y|x_k) = \frac{1}{|C|}, k \in K^{^{\prime }}$ , where $|C|$ is the number of classes  The objective function with the new term becomes
$$\mathcal {O}_{NE} = \mathcal {O} + \sum _{k \in K^{^{\prime }}} KL(\hat{p}_u(y|x_k) || p_\theta (y | x_k)) $$   (Eq  9)
Note that we do not need manual annotation to provide neutral features  One simple way is to take the most common features as neutral features  Experimental results show that this strategy works successfully 
Another way to prevent the model from drifting from the desired direction is to constrain the predicted class distribution on unlabeled data  When lacking knowledge about the class distribution of the data, one feasible way is to take maximum entropy principle, as below:
$$\mathcal {O}_{ME} = \mathcal {O} + \lambda \sum _{y} p(y) \log p(y)$$   (Eq  11)
where $p(y)$ is the predicted class distribution, given by $ p(y) = \frac{1}{|X|} \sum _{\rm x} p_\theta (y | \rm x)  $ To control the influence of this term on the overall objective function, we can tune $\lambda $ according to the difference in the number of labeled features of each class  In this paper, we simply set $\lambda $ to be proportional to the total number of labeled features, say $\lambda = \beta |K|$  
This maximum entropy term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$   Therefore, $E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]$ is just the model distribution $p_\theta (y|{\rm x})$ and its expectation with the empirical distribution $\tilde{p}(\rm x)$ is simply the average over input samples, namely $p(y)$   When $S$ takes the maximum entropy form, we can derive the objective function as above 
Sometimes, we have already had much knowledge about the corpus, and can estimate the class distribution roughly without labeling instances  Therefore, we introduce the KL divergence between the predicted and reference class distributions into the objective function 
Given the preference class distribution $\hat{p}(y)$ , we modify the objective function as follows:
$$\mathcal {O}_{KL} &= \mathcal {O} + \lambda KL(\hat{p}(y) || p(y))$$   (Eq  13)
Similarly, we set $\lambda = \beta |K|$  
This divergence term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$ and setting the score function to $S(\hat{p}, p) = \sum _i \hat{p}_i \log \frac{\hat{p}_i}{p_i}$ , where $p$ and $\hat{p}$ are distributions  Note that this regularization term involves the reference class distribution which will be discussed later 
Experiments
In this section, we first justify the approach when there exists unbalance in the number of labeled features or in class distribution  Then, to test the influence of $\lambda $ , we conduct some experiments with the method which incorporates the KL divergence of class distribution  Last, we evaluate our approaches in 9 commonly used text classification datasets  We set $\lambda = 5|K|$ by default in all experiments unless there is explicit declaration  The baseline we choose here is GE-FL BIBREF0 , a method based on generalization expectation criteria 
Data Preparation
We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare  We use bag-of-words feature and remove stopwords in the preprocess stage  Though we have labels of all documents, we do not use them during the learning process, instead, we use the label of features 
The movie dataset, in which the task is to classify the movie reviews as positive or negtive, is used for testing the proposed approaches with unbalanced labeled features, unbalanced datasets or different $\lambda $ parameters  All unbalanced datasets are constructed based on the movie dataset by randomly removing documents of the positive class  For each experiment, we conduct 10-fold cross validation 
As described in BIBREF0 , there are two ways to obtain labeled features  The first way is to use information gain  We first calculate the mutual information of all features according to the labels of the documents and select the top 20 as labeled features for each class as a feature pool  Note that using information gain requires the document label, but this is only to simulate how we human provide prior knowledge to the model  The second way is to use LDA BIBREF9 to select features  We use the same selection process as BIBREF0 , where they first train a LDA on the dataset, and then select the most probable features of each topic (sorted by $P(w_i|t_j)$ , the probability of word $w_i$ given topic $t_j$ ) 
Similar to BIBREF10 , BIBREF0 , we estimate the reference distribution of the labeled features using a heuristic strategy  If there are $|C|$ classes in total, and $n$ classes are associated with a feature $k$ , the probability that feature $k$ is related with any one of the $n$ classes is $\frac{0 9}{n}$ and with any other class is $\frac{0 1}{|C| - n}$  
Neutral features are the most frequent words after removing stop words, and their reference distributions are uniformly distributed  We use the top 10 frequent words as neutral features in all experiments 
With Unbalanced Labeled Features
In this section, we evaluate our approach when there is unbalanced knowledge on the categories to be classified  The labeled features are obtained through information gain  Two settings are chosen:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
As shown in Figure 1 , Maximum entropy principle shows improvement only on the balanced case  An obvious reason is that maximum entropy only favors uniform distribution 
Incorporating Neutral features performs similarly to maximum entropy since we assume that neutral words are uniformly distributed  Its accuracy decreases slowly when the number of labeled features becomes larger ( $t>4$ ) (Figure 1 (a)), suggesting that the model gradually biases to the class with more labeled features, just like GE-FL 
Incorporating the KL divergence of class distribution performs much better than GE-FL on both balanced and unbalanced datasets  This shows that it is effective to control the unbalance in labeled features and in the dataset 
With Balanced Labeled Features
We also compare with the baseline when the labeled features are balanced  Similar to the experiment above, the labeled features are obtained by information gain  Two settings are experimented with:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for each class, and conduct comparisons on the original balanced movie dataset (positive:negtive=1:1) 
(b) Similar to (a), but the class distribution is unbalanced, by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 2   When the dataset is balanced (Figure 2 (a)), there is little difference between GE-FL and our methods  The reason is that the proposed regularization terms provide no additional knowledge to the model and there is no bias in the labeled features  On the unbalanced dataset (Figure 2 (b)), incorporating KL divergence is much better than GE-FL since we provide additional knowledge(the true class distribution), but maximum entropy and neutral features are much worse because forcing the model to approach the uniform distribution misleads it 
With Unbalanced Class Distributions
Our methods are also evaluated on datasets with different unbalanced class distributions  We manually construct several movie datasets with class distributions of 1:2, 1:3, 1:4 by randomly removing 50%, 67%, 75% positive documents  The original balanced movie dataset is used as a control group  We test with both balanced and unbalanced labeled features  For the balanced case, we randomly select 10 features from the feature pool for each class, and for the unbalanced case, we select 10 features for one class, and 1 feature for the other  Results are shown in Figure 3  
Figure 3 (a) shows that when the dataset and the labeled features are both balanced, there is little difference between our methods and GE-FL(also see Figure 2 (a))  But when the class distribution becomes more unbalanced, the difference becomes more remarkable  Performance of neutral features and maximum entropy decrease significantly but incorporating KL divergence increases remarkably  This suggests if we have more accurate knowledge about class distribution, KL divergence can guide the model to the right direction 
Figure 3 (b) shows that when the labeled features are unbalanced, our methods significantly outperforms GE-FL  Incorporating KL divergence is robust enough to control unbalance both in the dataset and in labeled features while the other three methods are not so competitive 
The Influence of λ\lambda 
We present the influence of $\lambda $ on the method that incorporates KL divergence in this section  Since we simply set $\lambda = \beta |K|$ , we just tune $\beta $ here  Note that when $\beta = 0$ , the newly introduced regularization term is disappeared, and thus the model is actually GE-FL  Again, we test the method with different $\lambda $ in two settings:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other class  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 4   As expected, $\lambda $ reflects how strong the regularization is  The model tends to be closer to our preferences with the increasing of $\lambda $ on both cases 
Using LDA Selected Features
We compare our methods with GE-FL on all the 9 datasets in this section  Instead of using features obtained by information gain, we use LDA to select labeled features  Unlike information gain, LDA does not employ any instance labels to find labeled features  In this setting, we can build classification models without any instance annotation, but just with labeled features 
Table 1 shows that our three methods significantly outperform GE-FL  Incorporating neutral features performs better than GE-FL on 7 of the 9 datasets, maximum entropy is better on 8 datasets, and KL divergence better on 7 datasets 
LDA selects out the most predictive features as labeled features without considering the balance among classes  GE-FL does not exert any control on such an issue, so the performance is severely suffered  Our methods introduce auxiliary regularization terms to control such a bias problem and thus promote the model significantly 
Related Work
There have been much work that incorporate prior knowledge into learning, and two related lines are surveyed here  One is to use prior knowledge to label unlabeled instances and then apply a standard learning algorithm  The other is to constrain the model directly with prior knowledge 
Liu et al text manually labeled features which are highly predictive to unsupervised clustering assignments and use them to label unlabeled data  Chang et al guiding proposed constraint driven learning  They first used constraints and the learned model to annotate unlabeled instances, and then updated the model with the newly labeled data  Daumé daume2008cross proposed a self training method in which several models are trained on the same dataset, and only unlabeled instances that satisfy the cross task knowledge constraints are used in the self training process 
MaCallum et al gec proposed generalized expectation(GE) criteria which formalised the knowledge as constraint terms about the expectation of the model into the objective function Graça et al pr proposed posterior regularization(PR) framework which projects the model's posterior onto a set of distributions that satisfy the auxiliary constraints  Druck et al ge-fl explored constraints of labeled features in the framework of GE by forcing the model's predicted feature distribution to approach the reference distribution  Andrzejewski et al andrzejewski2011framework proposed a framework in which general domain knowledge can be easily incorporated into LDA  Altendorf et al altendorf2012learning explored monotonicity constraints to improve the accuracy while learning from sparse data  Chen et al chen2013leveraging tried to learn comprehensible topic models by leveraging multi-domain knowledge 
Mann and McCallum simple,generalized incorporated not only labeled features but also other knowledge like class distribution into the objective function of GE-FL  But they discussed only from the semi-supervised perspective and did not investigate into the robustness problem, unlike what we addressed in this paper 
There are also some active learning methods trying to use prior knowledge  Raghavan et al feedback proposed to use feedback on instances and features interlacedly, and demonstrated that feedback on features boosts the model much  Druck et al active proposed an active learning method which solicits labels on features rather than on instances and then used GE-FL to train the model 
Conclusion and Discussions
This paper investigates into the problem of how to leverage prior knowledge robustly in learning models  We propose three regularization terms on top of generalized expectation criteria  As demonstrated by the experimental results, the performance can be considerably improved when taking into account these factors  Comparative results show that our proposed methods is more effective and works more robustly against baselines  To the best of our knowledge, this is the first work to address the robustness problem of leveraging knowledge, and may inspire other research 
We then present more detailed discussions about the three regularization methods  Incorporating neutral features is the simplest way of regularization, which doesn't require any modification of GE-FL but just finding out some common features  But as Figure 1 (a) shows, only using neutral features are not strong enough to handle extremely unbalanced labeled features 
The maximum entropy regularization term shows the strong ability of controlling unbalance 
This method doesn't need any extra knowledge, and is thus suitable when we know nothing about the corpus  But this method assumes that the categories are uniformly distributed, which may not be the case in practice, and it will have a degraded performance if the assumption is violated (see Figure 1 (b), Figure 2 (b), Figure 3 (a)) 
The KL divergence performs much better on unbalanced corpora than other methods  The reason is that KL divergence utilizes the reference class distribution and doesn't make any assumptions  The fact suggests that additional knowledge does benefit the model 
However, the KL divergence term requires providing the true class distribution  Sometimes, we may have the exact knowledge about the true distribution, but sometimes we may not  Fortunately, the model is insensitive to the true distribution and therefore a rough estimation of the true distribution is sufficient  In our experiments, when the true class distribution is 1:2, where the reference class distribution is set to 1:1 5/1:2/1:2 5, the accuracy is 0 755/0 756/0 760 respectively  This provides us the possibility to perform simple computing on the corpus to obtain the distribution in reality  Or, we can set the distribution roughly with domain expertise 
Context 3: Introduction
Automatic classification of sentiment has mainly focused on categorizing tweets in either two (binary sentiment analysis) or three (ternary sentiment analysis) categories BIBREF0   In this work we study the problem of fine-grained sentiment classification where tweets are classified according to a five-point scale ranging from VeryNegative to VeryPositive  To illustrate this, Table TABREF3 presents examples of tweets associated with each of these categories  Five-point scales are widely adopted in review sites like Amazon and TripAdvisor, where a user's sentiment is ordered with respect to its intensity  From a sentiment analysis perspective, this defines a classification problem with five categories  In particular, Sebastiani et al  BIBREF1 defined such classification problems whose categories are explicitly ordered to be ordinal classification problems  To account for the ordering of the categories, learners are penalized according to how far from the true class their predictions are 
Although considering different scales, the various settings of sentiment classification are related  First, one may use the same feature extraction and engineering approaches to represent the text spans such as word membership in lexicons, morpho-syntactic statistics like punctuation or elongated word counts BIBREF2 , BIBREF3   Second, one would expect that knowledge from one task can be transfered to the others and this would benefit the performance  Knowing that a tweet is “Positive” in the ternary setting narrows the classification decision between the VeryPositive and Positive categories in the fine-grained setting  From a research perspective this raises the question of whether and how one may benefit when tackling such related tasks and how one can transfer knowledge from one task to another during the training phase 
Our focus in this work is to exploit the relation between the sentiment classification settings and demonstrate the benefits stemming from combining them  To this end, we propose to formulate the different classification problems as a multitask learning problem and jointly learn them  Multitask learning BIBREF4 has shown great potential in various domains and its benefits have been empirically validated BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 using different types of data and learning approaches  An important benefit of multitask learning is that it provides an elegant way to access resources developed for similar tasks  By jointly learning correlated tasks, the amount of usable data increases  For instance, while for ternary classification one can label data using distant supervision with emoticons BIBREF9 , there is no straightforward way to do so for the fine-grained problem  However, the latter can benefit indirectly, if the ternary and fine-grained tasks are learned jointly 
The research question that the paper attempts to answer is the following: Can twitter sentiment classification problems, and fine-grained sentiment classification in particular, benefit from multitask learning? To answer the question, the paper brings the following two main contributions: (i) we show how jointly learning the ternary and fine-grained sentiment classification problems in a multitask setting improves the state-of-the-art performance, and (ii) we demonstrate that recurrent neural networks outperform models previously proposed without access to huge corpora while being flexible to incorporate different sources of data 
Multitask Learning for Twitter Sentiment Classification
In his work, Caruana BIBREF4 proposed a multitask approach in which a learner takes advantage of the multiplicity of interdependent tasks while jointly learning them  The intuition is that if the tasks are correlated, the learner can learn a model jointly for them while taking into account the shared information which is expected to improve its generalization ability  People express their opinions online on various subjects (events, products  ), on several languages and in several styles (tweets, paragraph-sized reviews  ), and it is exactly this variety that motivates the multitask approaches  Specifically for Twitter for instance, the different settings of classification like binary, ternary and fine-grained are correlated since their difference lies in the sentiment granularity of the classes which increases while moving from binary to fine-grained problems 
There are two main decisions to be made in our approach: the learning algorithm, which learns a decision function, and the data representation  With respect to the former, neural networks are particularly suitable as one can design architectures with different properties and arbitrary complexity  Also, as training neural network usually relies on back-propagation of errors, one can have shared parts of the network trained by estimating errors on the joint tasks and others specialized for particular tasks  Concerning the data representation, it strongly depends on the data type available  For the task of sentiment classification of tweets with neural networks, distributed embeddings of words have shown great potential  Embeddings are defined as low-dimensional, dense representations of words that can be obtained in an unsupervised fashion by training on large quantities of text BIBREF10  
Concerning the neural network architecture, we focus on Recurrent Neural Networks (RNNs) that are capable of modeling short-range and long-range dependencies like those exhibited in sequence data of arbitrary length like text  While in the traditional information retrieval paradigm such dependencies are captured using INLINEFORM0 -grams and skip-grams, RNNs learn to capture them automatically BIBREF11   To circumvent the problems with capturing long-range dependencies and preventing gradients from vanishing, the long short-term memory network (LSTM) was proposed BIBREF12   In this work, we use an extended version of LSTM called bidirectional LSTM (biLSTM)  While standard LSTMs access information only from the past (previous words), biLSTMs capture both past and future information effectively BIBREF13 , BIBREF11   They consist of two LSTM networks, for propagating text forward and backwards with the goal being to capture the dependencies better  Indeed, previous work on multitask learning showed the effectiveness of biLSTMs in a variety of problems: BIBREF14 tackled sequence prediction, while BIBREF6 and BIBREF15 used biLSTMs for Named Entity Recognition and dependency parsing respectively 
Figure FIGREF2 presents the architecture we use for multitask learning  In the top-left of the figure a biLSTM network (enclosed by the dashed line) is fed with embeddings INLINEFORM0 that correspond to the INLINEFORM1 words of a tokenized tweet

ground_truth: ability to accurately classify texts even when the amount of prior knowledge for different classes is unbalanced, and when the class distribution of the dataset is unbalanced

Low sensitivity to bias in prior knowledge

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 clinical datasets used in the paper?
Context 2: Introduction
During the first two decades of the 21st century, the sharing and processing of vast amounts of data has become pervasive  This expansion of data sharing and processing capabilities is both a blessing and a curse  Data helps build better information systems for the digital era and enables further research for advanced data management that benefits the society in general  But the use of this very data containing sensitive information conflicts with private data protection, both from an ethical and a legal perspective 
There are several application domains on which this situation is particularly acute  This is the case of the medical domain BIBREF0  There are plenty of potential applications for advanced medical data management that can only be researched and developed using real data; yet, the use of medical data is severely limited –when not entirely prohibited– due to data privacy protection policies 
One way of circumventing this problem is to anonymise the data by removing, replacing or obfuscating the personal information mentioned, as exemplified in Table TABREF1  This task can be done by hand, having people read and anonymise the documents one by one  Despite being a reliable and simple solution, this approach is tedious, expensive, time consuming and difficult to scale to the potentially thousands or millions of documents that need to be anonymised 
For this reason, numerous of systems and approaches have been developed during the last decades to attempt to automate the anonymisation of sensitive content, starting with the automatic detection and classification of sensitive information  Some of these systems rely on rules, patterns and dictionaries, while others use more advanced techniques related to machine learning and, more recently, deep learning 
Given that this paper is concerned with text documents (e g  medical records), the involved techniques are related to Natural Language Processing (NLP)  When using NLP approaches, it is common to pose the problem of document anonymisation as a sequence labelling problem, i e  classifying each token within a sequence as being sensitive information or not  Further, depending on the objective of the anonymisation task, it is also important to determine the type of sensitive information (names of individuals, addresses, age, sex, etc ) 
The anonymisation systems based on NLP techniques perform reasonably well, but are far from perfect  Depending on the difficulty posed by each dataset or the amount of available data for training machine learning models, the performance achieved by these methods is not enough to fully rely on them in certain situations BIBREF0  However, in the last two years, the NLP community has reached an important milestone thanks to the appearance of the so-called Transformers neural network architectures BIBREF1  In this paper, we conduct several experiments in sensitive information detection and classification on Spanish clinical text using BERT (from `Bidirectional Encoder Representations from Transformers') BIBREF2 as the base for a sequence labelling approach  The experiments are carried out on two datasets: the MEDDOCAN: Medical Document Anonymization shared task dataset BIBREF3, and NUBes BIBREF4, a corpus of real medical reports in Spanish  In these experiments, we compare the performance of BERT with other machine-learning-based systems, some of which use language-specific features  Our aim is to evaluate how good a BERT-based model performs without language nor domain specialisation apart from the training data labelled for the task at hand 
The rest of the paper is structured as follows: the next section describes related work about data anonymisation in general and clinical data anonymisation in particular; it also provides a more detailed explanation and background about the Transformers architecture and BERT  Section SECREF3 describes the data involved in the experiments and the systems evaluated in this paper, including the BERT-based system; finally, it details the experimental design  Section SECREF4 introduces the results for each set of experiments  Finally, Section SECREF5 contains the conclusions and future lines of work 
Related Work
The state of the art in the field of Natural Language Processing (NLP) has reached an important milestone in the last couple of years thanks to deep-learning architectures, increasing in several points the performance of new models for almost any text processing task 
The major change started with the Transformers model proposed by vaswani2017attention  It substituted the widely used recurrent and convolutional neural network architectures by another approach based solely on self-attention, obtaining an impressive performance gain  The original proposal was focused on an encoder-decoder architecture for machine translation, but soon the use of Transformers was made more general BIBREF1  There are several other popular models that use Transformers, such as Open AI's GPT and GPT2 BIBREF5, RoBERTa BIBREF6 and the most recent XLNet BIBREF7; still, BERT BIBREF2 is one of the most widespread Transformer-based models 
BERT trains its unsupervised language model using a Masked Language Model and Next Sentence Prediction  A common problem in NLP is the lack of enough training data  BERT can be pre-trained to learn general or specific language models using very large amounts of unlabelled text (e g  web content, Wikipedia, etc ), and this knowledge can be transferred to a different downstream task in a process that receives the name fine-tuning 
devlin2018bert have used fine-tuning to achieve state-of-the-art results on a wide variety of challenging natural language tasks, such as text classification, Question Answering (QA) and Named Entity Recognition and Classification (NERC)  BERT has also been used successfully by other community practitioners for a wide range of NLP-related tasks BIBREF8, BIBREF9 
Regarding the task of data anonymisation in particular, anonymisation systems may follow different approaches and pursue different objectives (Cormode and Srivastava, 2009)  The first objective of these systems is to detect and classify the sensitive information contained in the documents to be anonymised  In order to achieve that, they use rule-based approaches, Machine Learning (ML) approaches, or a combination of both 
Although most of these efforts are for English texts –see, among others, the i2b2 de-identification challenges BIBREF10, BIBREF11, dernon2016deep, or khin2018deep–, other languages are also attracting growing interest  Some examples are mamede2016automated for Portuguese and tveit2004anonymization for Norwegian  With respect to the anonymisation of text written in Spanish, recent studies include medina2018building, hassan2018anonimizacion and garcia2018automating  Most notably, in 2019 the first community challenge about anonymisation of medical documents in Spanish, MEDDOCAN BIBREF3, was held as part of the IberLEF initiative  The winners of the challenge –the Neither-Language-nor-Domain-Experts (NLNDE) BIBREF12– achieved F1-scores as high as 0 975 in the task of sensitive information detection and categorisation by using recurrent neural networks with Conditional Random Field (CRF) output layers 
At the same challenge, mao2019hadoken occupied the 8th position among 18 participants using BERT  According to the description of the system, the authors used BERT-Base Multilingual Cased and an output CRF layer  However, their system is $\sim $3 F1-score points below our implementation without the CRF layer 
Materials and Methods
The aim of this paper is to evaluate BERT's multilingual model and compare it to other established machine-learning algorithms in a specific task: sensitive data detection and classification in Spanish clinical free text  This section describes the data involved in the experiments and the systems evaluated  Finally, we introduce the experimental setup 
Materials and Methods ::: Data
Two datasets are exploited in this article  Both datasets consist of plain text containing clinical narrative written in Spanish, and their respective manual annotations of sensitive information in BRAT BIBREF13 standoff format  In order to feed the data to the different algorithms presented in Section SECREF7, these datasets were transformed to comply with the commonly used BIO sequence representation scheme BIBREF14 
Materials and Methods ::: Data ::: NUBes-PHI
NUBes BIBREF4 is a corpus of around 7,000 real medical reports written in Spanish and annotated with negation and uncertainty information  Before being published, sensitive information had to be manually annotated and replaced for the corpus to be safely shared  In this article, we work with the NUBes version prior to its anonymisation, that is, with the manual annotations of sensitive information  It follows that the version we work with is not publicly available and, due to contractual restrictions, we cannot reveal the provenance of the data  In order to avoid confusion between the two corpus versions, we henceforth refer to the version relevant in this paper as NUBes-PHI (from `NUBes with Personal Health Information') 
NUBes-PHI consists of 32,055 sentences annotated for 11 different sensitive information categories  Overall, it contains 7,818 annotations  The corpus has been randomly split into train (72%), development (8%) and test (20%) sets to conduct the experiments described in this paper  The size of each split and the distribution of the annotations can be consulted in Tables and , respectively 
The majority of sensitive information in NUBes-PHI are temporal expressions (`Date' and `Time'), followed by healthcare facility mentions (`Hospital'), and the age of the patient  Mentions of people are not that frequent, with physician names (`Doctor') occurring much more often than patient names (`Patient')  The least frequent sensitive information types, which account for $\sim $10% of the remaining annotations, consist of the patient's sex, job, and kinship, and locations other than healthcare facilities (`Location')  Finally, the tag `Other' includes, for instance, mentions to institutions unrelated to healthcare and whether the patient is right- or left-handed  It occurs just 36 times 
Materials and Methods ::: Data ::: The MEDDOCAN corpus
The organisers of the MEDDOCAN shared task BIBREF3 curated a synthetic corpus of clinical cases enriched with sensitive information by health documentalists  In this regard, the MEDDOCAN evaluation scenario could be said to be somewhat far from the real use case the technology developed for the shared task is supposed to be applied in  However, at the moment it also provides the only public means for a rigorous comparison between systems for sensitive health information detection in Spanish texts 
The size of the MEDDOCAN corpus is shown in Table   Compared to NUBes-PHI (Table ), this corpus contains more sensitive information annotations, both in absolute and relative terms 
The sensitive annotation categories considered in MEDDOCAN differ in part from those in NUBes-PHI  Most notably, it contains finer-grained labels for location-related mentions –namely, `Address', `Territory', and `Country'–, and other sensitive information categories that we did not encounter in NUBes-PHI (e g , identifiers, phone numbers, e-mail addresses, etc )  In total, the MEDDOCAN corpus has 21 sensitive information categories  We refer the reader to the organisers' article BIBREF3 for more detailed information about this corpus 
Materials and Methods ::: Systems
Apart from experimenting with a pre-trained BERT model, we have run experiments with other systems and baselines, to compare them and obtain a better perspective about BERT's performance in these datasets 
Materials and Methods ::: Systems ::: Baseline
As the simplest baseline, a sensitive data recogniser and classifier has been developed that consists of regular-expressions and dictionary look-ups  For each category to detect a specific method has been implemented  For instance, the Date, Age, Time and Doctor detectors are based on regular-expressions; Hospital, Sex, Kinship, Location, Patient and Job are looked up in dictionaries  The dictionaries are hand-crafted from the training data available, except for the Patient's case, for which the possible candidates considered are the 100 most common female and male names in Spain according to the Instituto Nacional de Estadística (INE; Spanish Statistical Office) 
Materials and Methods ::: Systems ::: CRF
Conditional Random Fields (CRF) BIBREF15 have been extensively used for tasks of sequential nature  In this paper, we propose as one of the competitive baselines a CRF classifier trained with sklearn-crfsuite for Python 3 5 and the following configuration: algorithm = lbfgs; maximum iterations = 100; c1 = c2 = 0 1; all transitions = true; optimise = false  The features extracted from each token are as follows:
[noitemsep]
prefixes and suffixes of 2 and 3 characters;
the length of the token in characters and the length of the sentence in tokens;
whether the token is all-letters, a number, or a sequence of punctuation marks;
whether the token contains the character `@';
whether the token is the start or end of the sentence;
the token's casing and the ratio of uppercase characters, digits, and punctuation marks to its length;
and, the lemma, part-of-speech tag, and named-entity tag given by ixa-pipes BIBREF16 upon analysing the sentence the token belongs to 
Noticeably, none of the features used to train the CRF classifier is domain-dependent  However, the latter group of features is language dependent 
Materials and Methods ::: Systems ::: spaCy
spaCy is a widely used NLP library that implements state-of-the-art text processing pipelines, including a sequence-labelling pipeline similar to the one described by strubell2017fast  spaCy offers several pre-trained models in Spanish, which perform basic NLP tasks such as Named Entity Recognition (NER)  In this paper, we have trained a new NER model to detect NUBes-PHI labels  For this purpose, the new model uses all the labels of the training corpus coded with its context at sentence level  The network optimisation parameters and dropout values are the ones recommended in the documentation for small datasets  Finally, the model is trained using batches of size 64  No more features are included, so the classifier is language-dependent but not domain-dependent 
Materials and Methods ::: Systems ::: BERT
As introduced earlier, BERT has shown an outstanding performance in NERC-like tasks, improving the start-of-the-art results for almost every dataset and language  We take the same approach here, by using the model BERT-Base Multilingual Cased with a Fully Connected (FC) layer on top to perform a fine-tuning of the whole model for an anonymisation task in Spanish clinical data  Our implementation is built on PyTorch and the PyTorch-Transformers library BIBREF1  The training phase consists in the following steps (roughly depicted in Figure ):
Pre-processing: since we are relying on a pre-trained BERT model, we must match the same configuration by using a specific tokenisation and vocabulary  BERT also needs that the inputs contains special tokens to signal the beginning and the end of each sequence 
Fine-tuning: the pre-processed sequence is fed into the model  BERT outputs the contextual embeddings that encode each of the inputted tokens  This embedding representation for each token is fed into the FC linear layer after a dropout layer (with a 0 1 dropout probability), which in turn outputs the logits for each possible class  The cross-entropy loss function is calculated comparing the logits and the gold labels, and the error is back-propagated to adjust the model parameters 
We have trained the model using an AdamW optimiser BIBREF17 with the learning rate set to 3e-5, as recommended by devlin2018bert, and with a gradient clipping of 1 0  We also applied a learning-rate scheduler that warms up the learning rate from zero to its maximum value as the training progresses, which is also a common practice  For each experiment set proposed below, the training was run with an early-stopping patience of 15 epochs  Then, the model that performed best against the development set was used to produce the reported results 
The experiments were run on a 64-core server with operating system Ubuntu 16 04, 250GB of RAM memory, and 4 GeForce RTX 2080 GPUs with 11GB of memory  The maximum sequence length was set at 500 and the batch size at 12  In this setting, each epoch –a full pass through all the training data– required about 10 minutes to complete 
Materials and Methods ::: Experimental design
We have conducted experiments with BERT in the two datasets of Spanish clinical narrative presented in Section SECREF3 The first experiment set uses NUBes-PHI, a corpus of real medical reports manually annotated with sensitive information  Because this corpus is not publicly available, and in order to compare the BERT-based model to other related published systems, the second set of experiments uses the MEDDOCAN 2019 shared task competition dataset  The following sections provide greater detail about the two experimental setups 
Materials and Methods ::: Experimental design ::: Experiment A: NUBes-PHI
In this experiment set, we evaluate all the systems presented in Section SECREF7, namely, the rule-based baseline, the CRF classifier, the spaCy entity tagger, and BERT  The evaluation comprises three scenarios of increasing difficulty:
[noitemsep]
- Evaluates the performance of the systems at predicting whether each token is sensitive or non-sensitive; that is, the measurements only take into account whether a sensitive token has been recognised or not, regardless of the BIO label and the category assigned  This scenario shows how good a system would be at obfuscating sensitive data (e g , by replacing sensitive tokens with asterisks) 
- We measure the performance of the systems at predicting the sensitive information type of each token –i e , the 11 categories presented in Section SECREF5 or `out'  Detecting entity types correctly is important if a system is going to be used to replace sensitive data by fake data of the same type (e g , random people names) 
- This is the strictest evaluation, as it takes into account both the BIO label and the category assigned to each individual token  Being able to discern between two contiguous sensitive entities of the same type is relevant not only because it is helpful when producing fake replacements, but because it also yields more accurate statistics of the sensitive information present in a given document collection 
The systems are evaluated in terms of micro-average precision, recall and F1-score in all the scenarios 
In addition to the scenarios proposed, a subject worth being studied is the need of labelled data  Manually labelled data is an scarce and expensive resource, which for some application domains or languages is difficult to come by  In order to obtain an estimation of the dependency of each system on the available amount of training data, we have retrained all the compared models using decreasing amounts of data –from 100% of the available training instances to just 1%  The same data subsets have been used to train all the systems  Due to the knowledge transferred from the pre-trained BERT model, the BERT-based model is expected to be more robust to data scarcity than those that start their training from scratch 
Materials and Methods ::: Experimental design ::: Experiment B: MEDDOCAN
In this experiment set, our BERT implementation is compared to several systems that participated in the MEDDOCAN challenge: a CRF classifier BIBREF18, a spaCy entity recogniser BIBREF18, and NLNDE BIBREF12, the winner of the shared task and current state of the art for sensitive information detection and classification in Spanish clinical text  Specifically, we include the results of a domain-independent NLNDE model (S2), and the results of a model enriched with domain-specific embeddings (S3)  Finally, we include the results obtained by mao2019hadoken with a CRF output layer on top of BERT embeddings  MEDDOCAN consists of two scenarios:
[noitemsep]
- This evaluation measures how good a system is at detecting sensitive text spans, regardless of the category assigned to them 
- In this scenario, systems are required to match exactly not only the boundaries of each sensitive span, but also the category assigned 
The systems are evaluated in terms of micro-averaged precision, recall and F-1 score  Note that, in contrast to the evaluation in Experiment A, MEDDOCAN measurements are entity-based instead of tokenwise  An exhaustive explanation of the MEDDOCAN evaluation procedure is available online, as well as the official evaluation script, which we used to obtain the reported results 
Results
This section describes the results obtained in the two sets of experiments: NUBes-PHI and MEDDOCAN 
Results ::: Experiment A: NUBes-PHI
Table shows the results of the conducted experiments in NUBes-PHI for all the compared systems  The included baseline serves to give a quick insight about how challenging the data is  With simple regular expressions and gazetteers a precision of 0 853 is obtained  On the other hand, the recall, which directly depends on the coverage provided by the rules and resources, drops to 0 469  Hence, this task is unlikely to be solved without the generalisation capabilities provided by machine-learning and deep-learning models 
Regarding the detection scenario –that is, the scenario concerned with a binary classification to determine whether each individual token conveys sensitive information or not–, it can be observed that BERT outperforms its competitors  A fact worth highlighting is that, according to these results, BERT achieves a precision lower than the rest of the systems (i e , it makes more false positive predictions); in exchange, it obtains a remarkably higher recall  Noticeably, it reaches a recall of 0 979, improving by more than 4 points the second-best system, spaCy 
The table also shows the results for the relaxed metric that only takes into account the entity type detected, regardless of the BIO label (i e , ignoring whether the token is at the beginning or in the middle of a sensitive sequence of tokens)  The conclusions are very similar to those extracted previously, with BERT gaining 2 1 points of F1-score over the CRF based approach  The confusion matrices of the predictions made by CRF, spaCy, and BERT in this scenario are shown in Table   As can bee seen, BERT has less difficulty in predicting correctly less frequent categories, such as `Location', `Job', and `Patient'  One of the most common mistakes according to the confusion matrices is classifying hospital names as `Location' instead of the more accurate `Hospital'; this is hardly a harmful error, given that a hospital is actually a location  Last, the category `Other' is completely leaked by all the compared systems, most likely due to its almost total lack of support in both training and evaluation datasets 
To finish with this experiment set, Table also shows the strict classification precision, recall and F1-score for the compared systems  Despite the fact that, in general, the systems obtain high values, BERT outperforms them again  BERT's F1-score is 1 9 points higher than the next most competitive result in the comparison  More remarkably, the recall obtained by BERT is about 5 points above 
Upon manual inspection of the errors committed by the BERT-based model, we discovered that it has a slight tendency towards producing ill-formed BIO sequences (e g, starting a sensitive span with `Inside' instead of `Begin'; see Table )  We could expect that complementing the BERT-based model with a CRF layer on top would help enforce the emission of valid sequences, alleviating this kind of errors and further improving its results 
Finally, Figure shows the impact of decreasing the amount of training data in the detection scenario  It shows the difference in precision, recall, and F1-score with respect to that obtained using 100% of the training data  A general downward trend can be observed, as one would expect: less training data leads to less accurate predictions  However, the BERT-based model is the most robust to training-data reduction, showing an steadily low performance loss  With 1% of the dataset (230 training instances), the BERT-based model only suffers a striking 7-point F1-score loss, in contrast to the 32 and 39 points lost by the CRF and spaCy models, respectively  This steep performance drop stems to a larger extent from recall decline, which is not that marked in the case of BERT  Overall, these results indicate that the transfer-learning achieved through the BERT multilingual pre-trained model not only helps obtain better results, but also lowers the need of manually labelled data for this application domain 
Results ::: Experiment B: MEDDOCAN
The results of the two MEDDOCAN scenarios –detection and classification– are shown in Table   These results follow the same pattern as in the previous experiments, with the CRF classifier being the most precise of all, and BERT outperforming both the CRF and spaCy classifiers thanks to its greater recall  We also show the results of mao2019hadoken who, despite of having used a BERT-based system, achieve lower scores than our models  The reason why it should be so remain unclear 
With regard to the winner of the MEDDOCAN shared task, the BERT-based model has not improved the scores obtained by neither the domain-dependent (S3) nor the domain-independent (S2) NLNDE model  However, attending to the obtained results, BERT remains only 0 3 F1-score points behind, and would have achieved the second position among all the MEDDOCAN shared task competitors  Taking into account that only 3% of the gold labels remain incorrectly annotated, the task can be considered almost solved, and it is not clear if the differences among the systems are actually significant, or whether they stem from minor variations in initialisation or a long-tail of minor labelling inconsistencies 
Conclusions and Future Work
In this work we have briefly introduced the problems related to data privacy protection in clinical domain  We have also described some of the groundbreaking advances on the Natural Language Processing field due to the appearance of Transformers-based deep-learning architectures and transfer learning from very large general-domain multilingual corpora, focusing our attention in one of its most representative examples, Google's BERT model 
In order to assess the performance of BERT for Spanish clinical data anonymisation, we have conducted several experiments with a BERT-based sequence labelling approach using the pre-trained multilingual BERT model shared by Google as the starting point for the model training  We have compared this BERT-based sequence labelling against other methods and systems  One of the experiments uses the MEDDOCAN 2019 shared task dataset, while the other uses a novel Spanish clinical reports dataset called NUBes-PHI 
The results of the experiments show that, in NUBes-PHI, the BERT-based model outperforms the other systems without requiring any adaptation or domain-specific feature engineering, just by being trained on the provided labelled data  Interestingly, the BERT-based model obtains a remarkably higher recall than the other systems  High recall is a desirable outcome because, when anonymising sensible documents, the accidental leak of sensible data is likely to be more dangerous than the unintended over-obfuscation of non-sensitive text 
Further, we have conducted an additional experiment on this dataset by progressively reducing the training data for all the compared systems  The BERT-based model shows the highest robustness to training-data scarcity, loosing only 7 points of F1-score when trained on 230 instances instead of 21,371  These observation are in line with the results obtained by the NLP community using BERT for other tasks 
The experiments with the MEDDOCAN 2019 shared task dataset follow the same pattern  In this case, the BERT-based model falls 0 3 F1-score points behind the shared task winning system, but it would have achieved the second position in the competition with no further refinement 
Since we have used a pre-trained multilingual BERT model, the same approach is likely to work for other languages just by providing some labelled training data  Further, this is the simplest fine-tuning that can be performed based on BERT  More sophisticated fine-tuning layers could help improve the results  For example, it could be expected that a CRF layer helped enforce better BIO tagging sequence predictions  Precisely, mao2019hadoken participated in the MEDDOCAN competition using a BERT+CRF architecture, but their reported scores are about 3 points lower than our implementation  From the description of their work, it is unclear what the source of this score difference could be 
Further, at the time of writing this paper, new multilingual pre-trained models and Transformer architectures have become available  It would not come as a surprise that these new resources and systems –e g , XLM-RoBERTa BIBREF19 or BETO BIBREF20, a BERT model fully pre-trained on Spanish texts– further advanced the state of the art in this task 
Acknowledgements
This work has been supported by Vicomtech and partially funded by the project DeepReading (RTI2018-096846-B-C21, MCIU/AEI/FEDER,UE) 
Context 3:  Notice, as discussed above, the biLSTM consists of two LSTMs that are fed with the word sequence forward and backwards  On top of the biLSTM network one (or more) hidden layers INLINEFORM2 transform its output  The output of INLINEFORM3 is led to the softmax layers for the prediction step  There are INLINEFORM4 softmax layers and each is used for one of the INLINEFORM5 tasks of the multitask setting  In tasks such as sentiment classification, additional features like membership of words in sentiment lexicons or counts of elongated/capitalized words can be used to enrich the representation of tweets before the classification step BIBREF3   The lower part of the network illustrates how such sources of information can be incorporated to the process  A vector “Additional Features” for each tweet is transformed from the hidden layer(s) INLINEFORM6 and then is combined by concatenation with the transformed biLSTM output in the INLINEFORM7 layer 
Experimental setup
Our goal is to demonstrate how multitask learning can be successfully applied on the task of sentiment classification of tweets  The particularities of tweets are to be short and informal text spans  The common use of abbreviations, creative language etc , makes the sentiment classification problem challenging  To validate our hypothesis, that learning the tasks jointly can benefit the performance, we propose an experimental setting where there are data from two different twitter sentiment classification problems: a fine-grained and a ternary  We consider the fine-grained task to be our primary task as it is more challenging and obtaining bigger datasets, e g  by distant supervision, is not straightforward and, hence we report the performance achieved for this task 
Ternary and fine-grained sentiment classification were part of the SemEval-2016 “Sentiment Analysis in Twitter” task BIBREF16   We use the high-quality datasets the challenge organizers released  The dataset for fine-grained classification is split in training, development, development_test and test parts  In the rest, we refer to these splits as train, development and test, where train is composed by the training and the development instances  Table TABREF7 presents an overview of the data  As discussed in BIBREF16 and illustrated in the Table, the fine-grained dataset is highly unbalanced and skewed towards the positive sentiment: only INLINEFORM0 of the training examples are labeled with one of the negative classes 
Feature representation We report results using two different feature sets  The first one, dubbed nbow, is a neural bag-of-words that uses text embeddings to generate low-dimensional, dense representations of the tweets  To construct the nbow representation, given the word embeddings dictionary where each word is associated with a vector, we apply the average compositional function that averages the embeddings of the words that compose a tweet  Simple compositional functions like average were shown to be robust and efficient in previous work BIBREF17   Instead of training embeddings from scratch, we use the pre-trained on tweets GloVe embeddings of BIBREF10   In terms of resources required, using only nbow is efficient as it does not require any domain knowledge  However, previous research on sentiment analysis showed that using extra resources, like sentiment lexicons, can benefit significantly the performance BIBREF3 , BIBREF2   To validate this and examine at which extent neural networks and multitask learning benefit from such features we evaluate the models using an augmented version of nbow, dubbed nbow+  The feature space of the latter, is augmented using 1,368 extra features consisting mostly of counts of punctuation symbols ('!?#@'), emoticons, elongated words and word membership features in several sentiment lexicons  Due to space limitations, for a complete presentation of these features, we refer the interested reader to BIBREF2 , whose open implementation we used to extract them 
Evaluation measure To reproduce the setting of the SemEval challenges BIBREF16 , we optimize our systems using as primary measure the macro-averaged Mean Absolute Error ( INLINEFORM0 ) given by: INLINEFORM1
where INLINEFORM0 is the number of categories, INLINEFORM1 is the set of instances whose true class is INLINEFORM2 , INLINEFORM3 is the true label of the instance INLINEFORM4 and INLINEFORM5 the predicted label  The measure penalizes decisions far from the true ones and is macro-averaged to account for the fact that the data are unbalanced  Complementary to INLINEFORM6 , we report the performance achieved on the micro-averaged INLINEFORM7 measure, which is a commonly used measure for classification 
The models To evaluate the multitask learning approach, we compared it with several other models  Support Vector Machines (SVMs) are maximum margin classification algorithms that have been shown to achieve competitive performance in several text classification problems BIBREF16   SVM INLINEFORM0 stands for an SVM with linear kernel and an one-vs-rest approach for the multi-class problem  Also, SVM INLINEFORM1 is an SVM with linear kernel that employs the crammer-singer strategy BIBREF18 for the multi-class problem  Logistic regression (LR) is another type of linear classification method, with probabilistic motivation  Again, we use two types of Logistic Regression depending on the multi-class strategy: LR INLINEFORM2 that uses an one-vs-rest approach and multinomial Logistic Regression also known as the MaxEnt classifier that uses a multinomial criterion 
Both SVMs and LRs as discussed above treat the problem as a multi-class one, without considering the ordering of the classes  For these four models, we tuned the hyper-parameter INLINEFORM0 that controls the importance of the L INLINEFORM1 regularization part in the optimization problem with grid-search over INLINEFORM2 using 10-fold cross-validation in the union of the training and development data and then retrained the models with the selected values  Also, to account for the unbalanced classification problem we used class weights to penalize more the errors made on the rare classes  These weights were inversely proportional to the frequency of each class

ground_truth: MEDDOCAN, NUBes-PHI

MEDDOCAN, NUBes 

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. By how much did they improve?
Context 3:  For the four models we used the implementations of Scikit-learn BIBREF19  
For multitask learning we use the architecture shown in Figure FIGREF2 , which we implemented with Keras BIBREF20   The embeddings are initialized with the 50-dimensional GloVe embeddings while the output of the biLSTM network is set to dimension 50  The activation function of the hidden layers is the hyperbolic tangent  The weights of the layers were initialized from a uniform distribution, scaled as described in BIBREF21   We used the Root Mean Square Propagation optimization method  We used dropout for regularizing the network  We trained the network using batches of 128 examples as follows: before selecting the batch, we perform a Bernoulli trial with probability INLINEFORM0 to select the task to train for  With probability INLINEFORM1 we pick a batch for the fine-grained sentiment classification problem, while with probability INLINEFORM2 we pick a batch for the ternary problem  As shown in Figure FIGREF2 , the error is backpropagated until the embeddings, that we fine-tune during the learning process  Notice also that the weights of the network until the layer INLINEFORM3 are shared and therefore affected by both tasks 
To tune the neural network hyper-parameters we used 5-fold cross validation  We tuned the probability INLINEFORM0 of dropout after the hidden layers INLINEFORM1 and for the biLSTM for INLINEFORM2 , the size of the hidden layer INLINEFORM3 and the probability INLINEFORM4 of the Bernoulli trials from INLINEFORM5   During training, we monitor the network's performance on the development set and apply early stopping if the performance on the validation set does not improve for 5 consecutive epochs 
Experimental results Table TABREF9 illustrates the performance of the models for the different data representations  The upper part of the Table summarizes the performance of the baselines  The entry “Balikas et al ” stands for the winning system of the 2016 edition of the challenge BIBREF2 , which to the best of our knowledge holds the state-of-the-art  Due to the stochasticity of training the biLSTM models, we repeat the experiment 10 times and report the average and the standard deviation of the performance achieved 
Several observations can be made from the table  First notice that, overall, the best performance is achieved by the neural network architecture that uses multitask learning  This entails that the system makes use of the available resources efficiently and improves the state-of-the-art performance  In conjunction with the fact that we found the optimal probability INLINEFORM0 , this highlights the benefits of multitask learning over single task learning  Furthermore, as described above, the neural network-based models have only access to the training data as the development are hold for early stopping  On the other hand, the baseline systems were retrained on the union of the train and development sets  Hence, even with fewer resources available for training on the fine-grained problem, the neural networks outperform the baselines  We also highlight the positive effect of the additional features that previous research proposed  Adding the features both in the baselines and in the biLSTM-based architectures improves the INLINEFORM1 scores by several points 
Lastly, we compare the performance of the baseline systems with the performance of the state-of-the-art system of BIBREF2   While BIBREF2 uses n-grams (and character-grams) with INLINEFORM0 , the baseline systems (SVMs, LRs) used in this work use the nbow+ representation, that relies on unigrams  Although they perform on par, the competitive performance of nbow highlights the potential of distributed representations for short-text classification  Further, incorporating structure and distributed representations leads to the gains of the biLSTM network, in the multitask and single task setting 
Similar observations can be drawn from Figure FIGREF10 that presents the INLINEFORM0 scores  Again, the biLSTM network with multitask learning achieves the best performance  It is also to be noted that although the two evaluation measures are correlated in the sense that the ranking of the models is the same, small differences in the INLINEFORM1 have large effect on the scores of the INLINEFORM2 measure 
Conclusion
In this paper, we showed that by jointly learning the tasks of ternary and fine-grained classification with a multitask learning model, one can greatly improve the performance on the second  This opens several avenues for future research  Since sentiment is expressed in different textual types like tweets and paragraph-sized reviews, in different languages (English, German,   ) and in different granularity levels (binary, ternary,  ) one can imagine multitask approaches that could benefit from combining such resources  Also, while we opted for biLSTM networks here, one could use convolutional neural networks or even try to combine different types of networks and tasks to investigate the performance effect of multitask learning  Lastly, while our approach mainly relied on the foundations of BIBREF4 , the internal mechanisms and the theoretical guarantees of multitask learning remain to be better understood 
Acknowledgements
This work is partially supported by the CIFRE N 28/2015 

ground_truth: They decrease MAE in 0.34