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. Which sports clubs are the targets?
Context 1: Introduction
Stance detection (also called stance identification or stance classification) is one of the considerably recent research topics in natural language processing (NLP)  It is usually defined as a classification problem where for a text and target pair, the stance of the author of the text for that target is expected as a classification output from the set: {Favor, Against, Neither} BIBREF0  
Stance detection is usually considered as a subtask of sentiment analysis (opinion mining) BIBREF1 topic in NLP  Both are mostly performed on social media texts, particularly on tweets, hence both are important components of social media analysis  Nevertheless, in sentiment analysis, the sentiment of the author of a piece of text usually as Positive, Negative, and Neutral is explored while in stance detection, the stance of the author of the text for a particular target (an entity, event, etc ) either explicitly or implicitly referred to in the text is considered  Like sentiment analysis, stance detection systems can be valuable components of information retrieval and other text analysis systems BIBREF0  
Previous work on stance detection include BIBREF2 where a stance classifier based on sentiment and arguing features is proposed in addition to an arguing lexicon automatically compiled  The ultimate approach performs better than distribution-based and uni-gram-based baseline systems BIBREF2   In BIBREF3 , the authors show that the use of dialogue structure improves stance detection in on-line debates  In BIBREF4 , Hasan and Ng carry out stance detection experiments using different machine learning algorithms, training data sets, features, and inter-post constraints in on-line debates, and draw insightful conclusions based on these experiments  For instance, they find that sequence models like HMMs perform better at stance detection when compared with non-sequence models like Naive Bayes (NB) BIBREF4   In another related study BIBREF5 , the authors conclude that topic-independent features can be exploited for disagreement detection in on-line dialogues  The employed features include agreement, cue words, denial, hedges, duration, polarity, and punctuation BIBREF5   Stance detection on a corpus of student essays is considered in BIBREF6   After using linguistically-motivated feature sets together with multivalued NB and SVM as the learning models, the authors conclude that they outperform two baseline approaches BIBREF6   In BIBREF7 , the author claims that Wikipedia can be used to determine stances about controversial topics based on their previous work regarding controversy extraction on the Web 
Among more recent related work, in BIBREF8 stance detection for unseen targets is studied and bidirectional conditional encoding is employed  The authors state that their approach achieves state-of-the art performance rates BIBREF8 on SemEval 2016 Twitter Stance Detection corpus BIBREF0   In BIBREF9 , a stance-community detection approach called SCIFNET is proposed  SCIFNET creates networks of people who are stance targets, automatically from the related document collections BIBREF9 using stance expansion and refinement techniques to arrive at stance-coherent networks  A tweet data set annotated with stance information regarding six predefined targets is proposed in BIBREF10 where this data set is annotated through crowdsourcing  The authors indicate that the data set is also annotated with sentiment information in addition to stance, so it can help reveal associations between stance and sentiment BIBREF10   Lastly, in BIBREF0 , SemEval 2016's aforementioned shared task on Twitter Stance Detection is described  Also provided are the results of the evaluations of 19 systems participating in two subtasks (one with training data set provided and the other without an annotated data set) of the shared task BIBREF0  
In this paper, we present a tweet data set in Turkish annotated with stance information, where the corresponding annotations are made publicly available  The domain of the tweets comprises two popular football clubs which constitute the targets of the tweets included  We also provide the evaluation results of SVM classifiers (for each target) on this data set using unigram, bigram, and hashtag features 
To the best of our knowledge, the current study is the first one to target at stance detection in Turkish tweets  Together with the provided annotated data set and the corresponding evaluations with the aforementioned SVM classifiers which can be used as baseline systems, our study will hopefully help increase social media analysis studies on Turkish content 
The rest of the paper is organized as follows: In Section SECREF2 , we describe our tweet data set annotated with the target and stance information  Section SECREF3 includes the details of our SVM-based stance classifiers and their evaluation results with discussions  Section SECREF4 includes future research topics based on the current study, and finally Section SECREF5 concludes the paper with a summary 
A Stance Detection Data Set
We have decided to consider tweets about popular sports clubs as our domain for stance detection  Considerable amounts of tweets are being published for sports-related events at every instant  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey  As is the case for the sentiment analysis tools, the outputs of the stance detection systems on a stream of tweets about these clubs can facilitate the use of the opinions of the football followers by these clubs 
In a previous study on the identification of public health-related tweets, two tweet data sets in Turkish (each set containing 1 million random tweets) have been compiled where these sets belong to two different periods of 20 consecutive days BIBREF11   We have decided to use one of these sets (corresponding to the period between August 18 and September 6, 2015) and firstly filtered the tweets using the possible names used to refer to the target clubs  Then, we have annotated the stance information in the tweets for these targets as Favor or Against  Within the course of this study, we have not considered those tweets in which the target is not explicitly mentioned, as our initial filtering process reveals 
For the purposes of the current study, we have not annotated any tweets with the Neither class  This stance class and even finer-grained classes can be considered in further annotation studies  We should also note that in a few tweets, the target of the stance was the management of the club while in some others a particular footballer of the club is praised or criticised  Still, we have considered the club as the target of the stance in all of the cases and carried out our annotations accordingly 
At the end of the annotation process, we have annotated 700 tweets, where 175 tweets are in favor of and 175 tweets are against Target-1, and similarly 175 tweets are in favor of and 175 are against Target-2  Hence, our data set is a balanced one although it is currently limited in size  The corresponding stance annotations are made publicly available at http://ceng metu edu tr/ INLINEFORM0 e120329/ Turkish_Stance_Detection_Tweet_Dataset csv in Comma Separated Values (CSV) format  The file contains three columns with the corresponding headers  The first column is the tweet id of the corresponding tweet, the second column contains the name of the stance target, and the last column includes the stance of the tweet for the target as Favor or Against 
To the best of our knowledge, this is the first publicly-available stance-annotated data set for Turkish  Hence, it is a significant resource as there is a scarcity of annotated data sets, linguistic resources, and NLP tools available for Turkish  Additionally, to the best of our knowledge, it is also significant for being the first stance-annotated data set including sports-related tweets, as previous stance detection data sets mostly include on-line texts on political/ethical issues 
Stance Detection Experiments Using SVM Classifiers
It is emphasized in the related literature that unigram-based methods are reliable for the stance detection task BIBREF2 and similarly unigram-based models have been used as baseline models in studies such as BIBREF0   In order to be used as a baseline and reference system for further studies on stance detection in Turkish tweets, we have trained two SVM classifiers (one for each target) using unigrams as features  Before the extraction of unigrams, we have employed automated preprocessing to filter out the stopwords in our annotated data set of 700 tweets  The stopword list used is the list presented in BIBREF12 which, in turn, is the slightly extended version of the stopword list provided in BIBREF13  
We have used the SVM implementation available in the Weka data mining application BIBREF14 where this particular implementation employs the SMO algorithm BIBREF15 to train a classifier with a linear kernel  The 10-fold cross-validation results of the two classifiers are provided in Table TABREF1 using the metrics of precision, recall, and F-Measure 
The evaluation results are quite favorable for both targets and particularly higher for Target-1, considering the fact that they are the initial experiments on the data set  The performance of the classifiers is better for the Favor class for both targets when compared with the performance results for the Against class  This outcome may be due to the common use of some terms when expressing positive stance towards sports clubs in Turkish tweets  The same percentage of common terms may not have been observed in tweets during the expression of negative stances towards the targets  Yet, completely the opposite pattern is observed in stance detection results of baseline systems given in BIBREF0 , i e , better F-Measure rates have been obtained for the Against class when compared with the Favor class BIBREF0   Some of the baseline systems reported in BIBREF0 are SVM-based systems using unigrams and ngrams as features similar to our study, but their data sets include all three stance classes of Favor, Against, and Neither, while our data set comprises only tweets classified as belonging to Favor or Against classes  Another difference is that the data sets in BIBREF0 have been divided into training and test sets, while in our study we provide 10-fold cross-validation results on the whole data set  On the other hand, we should also note that SVM-based sentiment analysis systems (such as those given in BIBREF16 ) have been reported to achieve better F-Measure rates for the Positive sentiment class when compared with the results obtained for the Negative class  Therefore, our evaluation results for each stance class seem to be in line with such sentiment analysis systems  Yet, further experiments on the extended versions of our data set should be conducted and the results should again be compared with the stance detection results given in the literature 
We have also evaluated SVM classifiers which use only bigrams as features, as ngram-based classifiers have been reported to perform better for the stance detection problem BIBREF0   However, we have observed that using bigrams as the sole features of the SVM classifiers leads to quite poor results  This observation may be due to the relatively limited size of the tweet data set employed  Still, we can conclude that unigram-based features lead to superior results compared to the results obtained using bigrams as features, based on our experiments on our data set  Yet, ngram-based features may be employed on the extended versions of the data set to verify this conclusion within the course of future work 
With an intention to exploit the contribution of hashtag use to stance detection, we have also used the existence of hashtags in tweets as an additional feature to unigrams  The corresponding evaluation results of the SVM classifiers using unigrams together the existence of hashtags as features are provided in Table TABREF2  
When the results given in Table TABREF2 are compared with the results in Table TABREF1 , a slight decrease in F-Measure (0 5%) for Target-1 is observed, while the overall F-Measure value for Target-2 has increased by 1 8%  Although we could not derive sound conclusions mainly due to the relatively small size of our data set, the increase in the performance of the SVM classifier Target-2 is an encouraging evidence for the exploitation of hashtags in a stance detection system  We leave other ways of exploiting hashtags for stance detection as a future work 
To sum up, our evaluation results are significant as reference results to be used for comparison purposes and provides evidence for the utility of unigram-based and hashtag-related features in SVM classifiers for the stance detection problem in Turkish tweets 
Future Prospects
Future work based on the current study includes the following:
Conclusion
Stance detection is a considerably new research area in natural language processing and is considered within the scope of the well-studied topic of sentiment analysis  It is the detection of stance within text towards a target which may be explicitly specified in the text or not  In this study, we present a stance-annotated tweet data set in Turkish where the targets of the annotated stances are two popular sports clubs in Turkey  The corresponding annotations are made publicly-available for research purposes  To the best of our knowledge, this is the first stance detection data set for the Turkish language and also the first sports-related stance-annotated data set  Also presented in this study are SVM classifiers (one for each target) utilizing unigram and bigram features in addition to using the existence of hashtags as another feature  10-fold cross validation results of these classifiers are presented which can be used as reference results by prospective systems  Both the annotated data set and the classifiers with evaluations are significant since they are the initial contributions to stance detection problem in Turkish tweets 
Context 2: Introduction
Understanding the emotions expressed in a text or message is of high relevance nowadays  Companies are interested in this to get an understanding of the sentiment of their current customers regarding their products and the sentiment of their potential customers to attract new ones  Moreover, changes in a product or a company may also affect the sentiment of a customer  However, the intensity of an emotion is crucial in determining the urgency and importance of that sentiment  If someone is only slightly happy about a product, is a customer willing to buy it again? Conversely, if someone is very angry about customer service, his or her complaint might be given priority over somewhat milder complaints 
BIBREF0 present four tasks in which systems have to automatically determine the intensity of emotions (EI) or the intensity of the sentiment (Valence) of tweets in the languages English, Arabic, and Spanish  The goal is to either predict a continuous regression (reg) value or to do ordinal classification (oc) based on a number of predefined categories  The EI tasks have separate training sets for four different emotions: anger, fear, joy and sadness  Due to the large number of subtasks and the fact that this language does not have many resources readily available, we only focus on the Spanish subtasks  Our work makes the following contributions:
Our submissions ranked second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc), demonstrating that the proposed method is accurate in automatically determining the intensity of emotions and sentiment of Spanish tweets  This paper will first focus on the datasets, the data generation procedure, and the techniques and tools used  Then we present the results in detail, after which we perform a small error analysis on the largest mistakes our model made  We conclude with some possible ideas for future work 
Data
For each task, the training data that was made available by the organizers is used, which is a selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment BIBREF1   Links and usernames were replaced by the general tokens URL and @username, after which the tweets were tokenized by using TweetTokenizer  All text was lowercased  In a post-processing step, it was ensured that each emoji is tokenized as a single token 
Word Embeddings
To be able to train word embeddings, Spanish tweets were scraped between November 8, 2017 and January 12, 2018  We chose to create our own embeddings instead of using pre-trained embeddings, because this way the embeddings would resemble the provided data set: both are based on Twitter data  Added to this set was the Affect in Tweets Distant Supervision Corpus (DISC) made available by the organizers BIBREF0 and a set of 4 1 million tweets from 2015, obtained from BIBREF2   After removing duplicate tweets and tweets with fewer than ten tokens, this resulted in a set of 58 7 million tweets, containing 1 1 billion tokens  The tweets were preprocessed using the method described in Section SECREF6   The word embeddings were created using word2vec in the gensim library BIBREF3 , using CBOW, a window size of 40 and a minimum count of 5  The feature vectors for each tweet were then created by using the AffectiveTweets WEKA package BIBREF4  
Translating Lexicons
Most lexical resources for sentiment analysis are in English  To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5  
All lexicons from the AffectiveTweets package were translated, except for SentiStrength  Instead of translating this lexicon, the English version was replaced by the Spanish variant made available by BIBREF6  
For each subtask, the optimal combination of lexicons was determined  This was done by first calculating the benefits of adding each lexicon individually, after which only beneficial lexicons were added until the score did not increase anymore (e g  after adding the best four lexicons the fifth one did not help anymore, so only four were added)  The tests were performed using a default SVM model, with the set of word embeddings described in the previous section  Each subtask thus uses a different set of lexicons (see Table TABREF1 for an overview of the lexicons used in our final ensemble)  For each subtask, this resulted in a (modest) increase on the development set, between 0 01 and 0 05 
Translating Data
The training set provided by BIBREF0 is not very large, so it was interesting to find a way to augment the training set  A possible method is to simply translate the datasets into other languages, leaving the labels intact  Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish  This new set of “Spanish” data was then added to our original training set  Again, the machine translation platform Apertium BIBREF5 was used for the translation of the datasets 
Algorithms Used
Three types of models were used in our system, a feed-forward neural network, an LSTM network and an SVM regressor  The neural nets were inspired by the work of Prayas BIBREF7 in the previous shared task  Different regression algorithms (e g  AdaBoost, XGBoost) were also tried due to the success of SeerNet BIBREF8 , but our study was not able to reproduce their results for Spanish 
For both the LSTM network and the feed-forward network, a parameter search was done for the number of layers, the number of nodes and dropout used  This was done for each subtask, i e  different tasks can have a different number of layers  All models were implemented using Keras BIBREF9   After the best parameter settings were found, the results of 10 system runs to produce our predictions were averaged (note that this is different from averaging our different type of models in Section SECREF16 )  For the SVM (implemented in scikit-learn BIBREF10 ), the RBF kernel was used and a parameter search was conducted for epsilon  Detailed parameter settings for each subtask are shown in Table TABREF12   Each parameter search was performed using 10-fold cross validation, as to not overfit on the development set 
Semi-supervised Learning
One of the aims of this study was to see if using semi-supervised learning is beneficial for emotion intensity tasks  For this purpose, the DISC BIBREF0 corpus was used  This corpus was created by querying certain emotion-related words, which makes it very suitable as a semi-supervised corpus  However, the specific emotion the tweet belonged to was not made public  Therefore, a method was applied to automatically assign the tweets to an emotion by comparing our scraped tweets to this new data set 
First, in an attempt to obtain the query-terms, we selected the 100 words which occurred most frequently in the DISC corpus, in comparison with their frequencies in our own scraped tweets corpus  Words that were clearly not indicators of emotion were removed  The rest was annotated per emotion or removed if it was unclear to which emotion the word belonged  This allowed us to create silver datasets per emotion, assigning tweets to an emotion if an annotated emotion-word occurred in the tweet

ground_truth: Galatasaray, Fenerbahçe

Galatasaray , Fenerbahçe 

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 was the training data translated?
Context 2: 
Our semi-supervised approach is quite straightforward: first a model is trained on the training set and then this model is used to predict the labels of the silver data  This silver data is then simply added to our training set, after which the model is retrained  However, an extra step is applied to ensure that the silver data is of reasonable quality  Instead of training a single model initially, ten different models were trained which predict the labels of the silver instances  If the highest and lowest prediction do not differ more than a certain threshold the silver instance is maintained, otherwise it is discarded 
This results in two parameters that could be optimized: the threshold and the number of silver instances that would be added  This method can be applied to both the LSTM and feed-forward networks that were used  An overview of the characteristics of our data set with the final parameter settings is shown in Table TABREF14   Usually, only a small subset of data was added to our training set, meaning that most of the silver data is not used in the experiments  Note that since only the emotions were annotated, this method is only applicable to the EI tasks 
Ensembling
To boost performance, the SVM, LSTM, and feed-forward models were combined into an ensemble  For both the LSTM and feed-forward approach, three different models were trained  The first model was trained on the training data (regular), the second model was trained on both the training and translated training data (translated) and the third one was trained on both the training data and the semi-supervised data (silver)  Due to the nature of the SVM algorithm, semi-supervised learning does not help, so only the regular and translated model were trained in this case  This results in 8 different models per subtask  Note that for the valence tasks no silver training data was obtained, meaning that for those tasks the semi-supervised models could not be used 
Per task, the LSTM and feed-forward model's predictions were averaged over 10 prediction runs  Subsequently, the predictions of all individual models were combined into an average  Finally, models were removed from the ensemble in a stepwise manner if the removal increased the average score  This was done based on their original scores, i e  starting out by trying to remove the worst individual model and working our way up to the best model  We only consider it an increase in score if the difference is larger than 0 002 (i e  the difference between 0 716 and 0 718)  If at some point the score does not increase and we are therefore unable to remove a model, the process is stopped and our best ensemble of models has been found  This process uses the scores on the development set of different combinations of models  Note that this means that the ensembles for different subtasks can contain different sets of models  The final model selections can be found in Table TABREF17  
Results and Discussion
Table TABREF18 shows the results on the development set of all individuals models, distinguishing the three types of training: regular (r), translated (t) and semi-supervised (s)  In Tables TABREF17 and TABREF18 , the letter behind each model (e g  SVM-r, LSTM-r) corresponds to the type of training used  Comparing the regular and translated columns for the three algorithms, it shows that in 22 out of 30 cases, using translated instances as extra training data resulted in an improvement  For the semi-supervised learning approach, an improvement is found in 15 out of 16 cases  Moreover, our best individual model for each subtask (bolded scores in Table TABREF18 ) is always either a translated or semi-supervised model  Table TABREF18 also shows that, in general, our feed-forward network obtained the best results, having the highest F-score for 8 out of 10 subtasks 
However, Table TABREF19 shows that these scores can still be improved by averaging or ensembling the individual models  On the dev set, averaging our 8 individual models results in a better score for 8 out of 10 subtasks, while creating an ensemble beats all of the individual models as well as the average for each subtask  On the test set, however, only a small increase in score (if any) is found for stepwise ensembling, compared to averaging  Even though the results do not get worse, we cannot conclude that stepwise ensembling is a better method than simply averaging 
Our official scores (column Ens Test in Table TABREF19 ) have placed us second (EI-Reg, EI-Oc), fourth (V-Reg) and fifth (V-Oc) on the SemEval AIT-2018 leaderboard  However, it is evident that the results obtained on the test set are not always in line with those achieved on the development set  Especially on the anger subtask for both EI-Reg and EI-Oc, the scores are considerably lower on the test set in comparison with the results on the development set  Therefore, a small error analysis was performed on the instances where our final model made the largest errors 
Error Analysis
Due to some large differences between our results on the dev and test set of this task, we performed a small error analysis in order to see what caused these differences  For EI-Reg-anger, the gold labels were compared to our own predictions, and we manually checked 50 instances for which our system made the largest errors 
Some examples that were indicative of the shortcomings of our system are shown in Table TABREF20   First of all, our system did not take into account capitalization  The implications of this are shown in the first sentence, where capitalization intensifies the emotion used in the sentence  In the second sentence, the name Imperator Furiosa is not understood  Since our texts were lowercased, our system was unable to capture the named entity and thought the sentence was about an angry emperor instead  In the third sentence, our system fails to capture that when you are so angry that it makes you laugh, it results in a reduced intensity of the angriness  Finally, in the fourth sentence, it is the figurative language me infla la vena (it inflates my vein) that the system is not able to understand 
The first two error-categories might be solved by including smart features regarding capitalization and named entity recognition  However, the last two categories are problems of natural language understanding and will be very difficult to fix 
Conclusion
To conclude, the present study described our submission for the Semeval 2018 Shared Task on Affect in Tweets  We participated in four Spanish subtasks and our submissions ranked second, second, fourth and fifth place  Our study aimed to investigate whether the automatic generation of additional training data through translation and semi-supervised learning, as well as the creation of stepwise ensembles, increase the performance of our Spanish-language models  Strong support was found for the translation and semi-supervised learning approaches; our best models for all subtasks use either one of these approaches  These results suggest that both of these additional data resources are beneficial when determining emotion intensity (for Spanish)  However, the creation of a stepwise ensemble from the best models did not result in better performance compared to simply averaging the models  In addition, some signs of overfitting on the dev set were found  In future work, we would like to apply the methods (translation and semi-supervised learning) used on Spanish on other low-resource languages and potentially also on other tasks 
Context 3: Introduction
Data imbalance is a common issue in a variety of NLP tasks such as tagging and machine reading comprehension  Table TABREF3 gives concrete examples: for the Named Entity Recognition (NER) task BIBREF2, BIBREF3, most tokens are backgrounds with tagging class $O$  Specifically, the number of tokens tagging class $O$ is 5 times as many as those with entity labels for the CoNLL03 dataset and 8 times for the OntoNotes5 0 dataset; Data-imbalanced issue is more severe for MRC tasks BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8 with the value of negative-positive ratio being 50-200 
Data imbalance results in the following two issues: (1) the training-test discrepancy: Without balancing the labels, the learning process tends to converge to a point that strongly biases towards class with the majority label  This actually creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function while at test time, F1 score concerns more about positive examples; (2) the overwhelming effect of easy-negative examples  As pointed out by meng2019dsreg, significantly large number of negative examples also means that the number of easy-negative example is large  The huge number of easy examples tends to overwhelm the training, making the model not sufficiently learned to distinguish between positive examples and hard-negative examples  The cross-entropy objective (CE for short) or maximum likelihood (MLE) objective, which is widely adopted as the training objective for data-imbalanced NLP tasks BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, handles neither of the issues 
To handle the first issue, we propose to replace CE or MLE with losses based on the Sørensen–Dice coefficient BIBREF0 or Tversky index BIBREF1  The Sørensen–Dice coefficient, dice loss for short, is the harmonic mean of precision and recall  It attaches equal importance to false positives (FPs) and false negatives (FNs) and is thus more immune to data-imbalanced datasets  Tversky index extends dice loss by using a weight that trades precision and recall, which can be thought as the approximation of the $F_{\beta }$ score, and thus comes with more flexibility  Therefore, We use dice loss or Tversky index to replace CE loss to address the first issue 
Only using dice loss or Tversky index is not enough since they are unable to address the dominating influence of easy-negative examples  This is intrinsically because dice loss is actually a hard version of the F1 score  Taking the binary classification task as an example, at test time, an example will be classified as negative as long as its probability is smaller than 0 5, but training will push the value to 0 as much as possible  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones  Inspired by the idea of focal loss BIBREF16 in computer vision, we propose a dynamic weight adjusting strategy, which associates each training example with a weight in proportion to $(1-p)$, and this weight dynamically changes as training proceeds  This strategy helps to deemphasize confident examples during training as their $p$ approaches the value of 1, makes the model attentive to hard-negative examples, and thus alleviates the dominating effect of easy-negative examples 
Combing both strategies, we observe significant performance boosts on a wide range of data imbalanced NLP tasks  Notably, we are able to achieve SOTA results on CTB5 (97 92, +1 86), CTB6 (96 57, +1 80) and UD1 4 (96 98, +2 19) for the POS task; SOTA results on CoNLL03 (93 33, +0 29), OntoNotes5 0 (92 07, +0 96)), MSRA 96 72(+0 97) and OntoNotes4 0 (84 47,+2 36) for the NER task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification 
The rest of this paper is organized as follows: related work is presented in Section 2  We describe different training objectives in Section 3  Experimental results are presented in Section 4  We perform ablation studies in Section 5, followed by a brief conclusion in Section 6 
Related Work ::: Data Resample
The idea of weighting training examples has a long history  Importance sampling BIBREF17 assigns weights to different samples and changes the data distribution  Boosting algorithms such as AdaBoost BIBREF18 select harder examples to train subsequent classifiers  Similarly, hard example mining BIBREF19 downsamples the majority class and exploits the most difficult examples  Oversampling BIBREF20, BIBREF21 is used to balance the data distribution  Another line of data resampling is to dynamically control the weights of examples as training proceeds  For example, focal loss BIBREF16 used a soft weighting scheme that emphasizes harder examples during training  In self-paced learning BIBREF22, example weights are obtained through optimizing the weighted training loss which encourages learning easier examples first  At each training step, self-paced learning algorithm optimizes model parameters and example weights jointly  Other works BIBREF23, BIBREF24 adjusted the weights of different training examples based on training loss  Besides, recent work BIBREF25, BIBREF26 proposed to learn a separate network to predict sample weights 
Related Work ::: Data Imbalance Issue in Object Detection
The background-object label imbalance issue is severe and thus well studied in the field of object detection BIBREF27, BIBREF28, BIBREF29, BIBREF30, BIBREF31  The idea of hard negative mining (HNM) BIBREF30 has gained much attention recently  shrivastava2016ohem proposed the online hard example mining (OHEM) algorithm in an iterative manner that makes training progressively more difficult, and pushes the model to learn better  ssd2016liu sorted all of the negative samples based on the confidence loss and picking the training examples with the negative-positive ratio at 3:1  pang2019rcnn proposed a novel method called IoU-balanced sampling and aploss2019chen designed a ranking model to replace the conventional classification task with a average-precision loss to alleviate the class imbalance issue  The efforts made on object detection have greatly inspired us to solve the data imbalance issue in NLP 
Losses ::: Notation
For illustration purposes, we use the binary classification task to demonstrate how different losses work  The mechanism can be easily extended to multi-class classification 
Let $\lbrace x_i\rbrace $ denote a set of instances  Each $x_i$ is associated with a golden label vector $y_i = [y_{i0},y_{i1} ]$, where $y_{i1}\in \lbrace 0,1\rbrace $ and $y_{i0}\in \lbrace 0,1\rbrace $ respectively denote the positive and negative classes, and thus $y_i$ can be either $[0,1]$ or $[0,1]$  Let $p_i = [p_{i0},p_{i1} ]$ denote the probability vector, and $p_{i1}$ and $p_{i0}$ respectively denote the probability that a model assigns the positive and negative label to $x_i$ 
Losses ::: Cross Entropy Loss
The vanilla cross entropy (CE) loss is given by:
As can be seen from Eq DISPLAY_FORM8, each $x_i$ contributes equally to the final objective  Two strategies are normally used to address the the case where we wish that not all $x_i$ are treated equal: associating different classes with different weighting factor $\alpha $ or resampling the datasets  For the former, Eq DISPLAY_FORM8 is adjusted as follows:
where $\alpha _i\in [0,1]$ may be set by the inverse class frequency or treated as a hyperparameter to set by cross validation  In this work, we use $\lg (\frac{n-n_t}{n_t}+K)$ to calculate the coefficient $\alpha $, where $n_t$ is the number of samples with class $t$ and $n$ is the total number of samples in the training set  $K$ is a hyperparameter to tune  The data resampling strategy constructs a new dataset by sampling training examples from the original dataset based on human-designed criteria, e g , extract equal training samples from each class  Both strategies are equivalent to changing the data distribution and thus are of the same nature  Empirically, these two methods are not widely used due to the trickiness of selecting $\alpha $ especially for multi-class classification tasks and that inappropriate selection can easily bias towards rare classes BIBREF32 
Losses ::: Dice coefficient and Tversky index
Sørensen–Dice coefficient BIBREF0, BIBREF33, dice coefficient (DSC) for short, is a F1-oriented statistic used to gauge the similarity of two sets  Given two sets $A$ and $B$, the dice coefficient between them is given as follows:
In our case, $A$ is the set that contains of all positive examples predicted by a specific model, and $B$ is the set of all golden positive examples in the dataset  When applied to boolean data with the definition of true positive (TP), false positive (FP), and false negative (FN), it can be then written as follows:
For an individual example $x_i$, its corresponding DSC loss is given as follows:
As can be seen, for a negative example with $y_{i1}=0$, it does not contribute to the objective  For smoothing purposes, it is common to add a $\gamma $ factor to both the nominator and the denominator, making the form to be as follows:
As can be seen, negative examples, with $y_{i1}$ being 0 and DSC being $\frac{\gamma }{ p_{i1}+\gamma }$, also contribute to the training  Additionally, milletari2016v proposed to change the denominator to the square form for faster convergence, which leads to the following dice loss (DL):
Another version of DL is to directly compute set-level dice coefficient instead of the sum of individual dice coefficient  We choose the latter due to ease of optimization 
Tversky index (TI), which can be thought as the approximation of the $F_{\beta }$ score, extends dice coefficient to a more general case  Given two sets $A$ and $B$, tversky index is computed as follows:
Tversky index offers the flexibility in controlling the tradeoff between false-negatives and false-positives  It degenerates to DSC if $\alpha =\beta =0 5$  The Tversky loss (TL) for the training set $\lbrace x_i,y_i\rbrace $ is thus as follows:
Losses ::: Self-adusting Dice Loss
Consider a simple case where the dataset consists of only one example $x_i$, which is classified as positive as long as $p_{i1}$ is larger than 0 5  The computation of $F1$ score is actually as follows:
Comparing Eq DISPLAY_FORM14 with Eq DISPLAY_FORM22, we can see that Eq DISPLAY_FORM14 is actually a soft form of $F1$, using a continuous $p$ rather than the binary $\mathbb {I}( p_{i1}>0 5)$  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones, which has a huge negative effect on the final F1 performance 
To address this issue, we propose to multiply the soft probability $p$ with a decaying factor $(1-p)$, changing Eq DISPLAY_FORM22 to the following form:
One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds  The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples  For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them  Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified 
A close look at Eq DISPLAY_FORM14 reveals that it actually mimics the idea of focal loss (FL for short) BIBREF16 for object detection in vision  Focal loss was proposed for one-stage object detector to handle foreground-background tradeoff encountered during training  It down-weights the loss assigned to well-classified examples by adding a $(1-p)^{\beta }$ factor, leading the final loss to be $(1-p)^{\beta }\log p$ 
In Table TABREF18, we show the losses used in our experiments, which is described in the next section 
Experiments
We evaluate the proposed method on four NLP tasks: part-of-speech tagging, named entity recognition, machine reading comprehension and paraphrase identification  Baselines in our experiments are optimized by using the standard cross-entropy training objective 
Experiments ::: Part-of-Speech Tagging
Part-of-speech tagging (POS) is the task of assigning a label (e g , noun, verb, adjective) to each word in a given text  In this paper, we choose BERT as the backbone and conduct experiments on three Chinese POS datasets  We report the span-level micro-averaged precision, recall and F1 for evaluation  Hyperparameters are tuned on the corresponding development set of each dataset 
Experiments ::: Part-of-Speech Tagging ::: Datasets
We conduct experiments on the widely used Chinese Treebank 5 0, 6 0 as well as UD1 4 
CTB5 is a Chinese dataset for tagging and parsing, which contains 507,222 words, 824,983 characters and 18,782 sentences extracted from newswire sources 
CTB6 is an extension of CTB5, containing 781,351 words, 1,285,149 characters and 28,295 sentences 
UD is the abbreviation of Universal Dependencies, which is a framework for consistent annotation of grammar (parts of speech, morphological features, and syntactic dependencies) across different human languages  In this work, we use UD1 4 for Chinese POS tagging 
Experiments ::: Part-of-Speech Tagging ::: Baselines
We use the following baselines:
Joint-POS: shao2017character jointly learns Chinese word segmentation and POS 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice 
Bert-Tagger: devlin2018bert treats part-of-speech as a tagging task 
Experiments ::: Part-of-Speech Tagging ::: Results
Table presents the experimental results on the POS task  As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i e , outperforming BERT-tagger by +1 86 in terms of F1 score on CTB5, +1 80 on CTB6 and +2 19 on UD1 4  As far as we are concerned, we are achieving SOTA performances on the three datasets  Weighted cross entropy and focal loss only gain a little performance improvement on CTB5 and CTB6, and the dice loss obtains huge gain on CTB5 but not on CTB6, which indicates the three losses are not consistently robust in resolving the data imbalance issue  The proposed DSC loss performs robustly on all the three datasets 
Experiments ::: Named Entity Recognition
Named entity recognition (NER) refers to the task of detecting the span and semantic category of entities from a chunk of text  Our implementation uses the current state-of-the-art BERT-MRC model proposed by xiaoya2019ner as a backbone  For English datasets, we use BERT$_\text{Large}$ English checkpoints, while for Chinese we use the official Chinese checkpoints  We report span-level micro-averaged precision, recall and F1-score  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Named Entity Recognition ::: Datasets
For the NER task, we consider both Chinese datasets, i e , OntoNotes4 0 BIBREF34 and MSRA BIBREF35, and English datasets, i e , CoNLL2003 BIBREF36 and OntoNotes5 0 BIBREF37 
CoNLL2003 is an English dataset with 4 entity types: Location, Organization, Person and Miscellaneous  We followed data processing protocols in BIBREF14 
English OntoNotes5 0 consists of texts from a wide variety of sources and contains 18 entity types  We use the standard train/dev/test split of CoNLL2012 shared task 
Chinese MSRA performs as a Chinese benchmark dataset containing 3 entity types  Data in MSRA is collected from news domain  Since the development set is not provided in the original MSRA dataset, we randomly split the training set into training and development splits by 9:1  We use the official test set for evaluation 
Chinese OntoNotes4 0 is a Chinese dataset and consists of texts from news domain, which has 18 entity types  In this paper, we take the same data split as wu2019glyce did 
Experiments ::: Named Entity Recognition ::: Baselines
We use the following baselines:
ELMo: a tagging model from peters2018deep 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice, only used in Chinese datasets 
CVT: from kevin2018cross, which uses Cross-View Training(CVT) to improve the representations of a Bi-LSTM encoder 
Bert-Tagger: devlin2018bert treats NER as a tagging task 
Glyce-BERT: wu2019glyce combines glyph information with BERT pretraining 
BERT-MRC: The current SOTA model for both Chinese and English NER datasets proposed by xiaoya2019ner, which formulate NER as machine reading comprehension task 
Experiments ::: Named Entity Recognition ::: Results
Table shows experimental results on NER datasets  For English datasets including CoNLL2003 and OntoNotes5 0, our proposed method outperforms BERT-MRCBIBREF38 by +0 29 and +0 96 respectively  We observe huge performance boosts on Chinese datasets, achieving F1 improvements by +0 97 and +2 36 on MSRA and OntoNotes4 0, respectively  As far as we are concerned, we are setting new SOTA performances on all of the four NER datasets 
Experiments ::: Machine Reading Comprehension
Machine reading comprehension (MRC) BIBREF39, BIBREF40, BIBREF41, BIBREF40, BIBREF42, BIBREF15 has become a central task in natural language understanding  MRC in the SQuAD-style is to predict the answer span in the passage given a question and the passage  In this paper, we choose the SQuAD-style MRC task and report Extract Match (EM) in addition to F1 score on validation set  All hyperparameters are tuned on the development set of each dataset 
Experiments ::: Machine Reading Comprehension ::: Datasets
The following five datasets are used for MRC task: SQuAD v1 1, SQuAD v2 0 BIBREF4, BIBREF6 and Quoref BIBREF8 
SQuAD v1 1 and SQuAD v2 0 are the most widely used QA benchmarks  SQuAD1 1 is a collection of 100K crowdsourced question-answer pairs, and SQuAD2 0 extends SQuAD1 1 allowing no short answer exists in the provided passage 
Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems, containing 24K questions over 4 7K paragraphs from Wikipedia 
Experiments ::: Machine Reading Comprehension ::: Baselines
We use the following baselines:
QANet: qanet2018 builds a model based on convolutions and self-attention  Convolution to model local interactions and self-attention to model global interactions 
BERT: devlin2018bert treats NER as a tagging task 
XLNet: xlnet2019 proposes a generalized autoregressive pretraining method that enables learning bidirectional contexts 
Experiments ::: Machine Reading Comprehension ::: Results
Table shows the experimental results for MRC tasks  With either BERT or XLNet, our proposed DSC loss obtains significant performance boost on both EM and F1  For SQuADv1 1, our proposed method outperforms XLNet by +1 25 in terms of F1 score and +0 84 in terms of EM and achieves 87 65 on EM and 89 51 on F1 for SQuAD v2 0  Moreover, on QuoRef, the proposed method surpasses XLNet results by +1 46 on EM and +1 41 on F1  Another observation is that, XLNet outperforms BERT by a huge margin, and the proposed DSC loss can obtain further performance improvement by an average score above 1 0 in terms of both EM and F1, which indicates the DSC loss is complementary to the model structures 
Experiments ::: Paraphrase Identification
Paraphrases are textual expressions that have the same semantic meaning using different surface words  Paraphrase identification (PI) is the task of identifying whether two sentences have the same meaning or not  We use BERT BIBREF11 and XLNet BIBREF43 as backbones and report F1 score for comparison  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Paraphrase Identification ::: Datasets
We conduct experiments on two widely used datasets for PI task: MRPC BIBREF44 and QQP 
MRPC is a corpus of sentence pairs automatically extracted from online news sources, with human annotations of whether the sentence pairs are semantically equivalent  The MRPC dataset has imbalanced classes (68% positive, 32% for negative) 
QQP is a collection of question pairs from the community question-answering website Quora  The class distribution in QQP is also unbalanced (37% positive, 63% negative) 
Experiments ::: Paraphrase Identification ::: Results
Table shows the results for PI task  We find that replacing the training objective with DSC introduces performance boost for both BERT and XLNet  Using DSC loss improves the F1 score by +0 58 for MRPC and +0 73 for QQP 
Ablation Studies ::: The Effect of Dice Loss on Accuracy-oriented Tasks
We argue that the most commonly used cross-entropy objective is actually accuracy-oriented, whereas the proposed dice loss (DL) performs as a hard version of F1-score  To explore the effect of the dice loss on accuracy-oriented tasks such as text classification, we conduct experiments on the Stanford Sentiment Treebank sentiment classification datasets including SST-2 and SST-5  We fine-tune BERT$_\text{Large}$ with different training objectives  Experiment results for SST are shown in   For SST-5, BERT with CE achieves 55 57 in terms of accuracy, with DL and DSC losses slightly degrade the accuracy performance and achieve 54 63 and 55 19, respectively  For SST-2, BERT with CE achieves 94 9 in terms of accuracy  The same as SST-5, we observe a slight performance drop with DL and DSC, which means that the dice loss actually works well for F1 but not for accuracy 
Ablation Studies ::: The Effect of Hyperparameters in Tversky index
As mentioned in Section SECREF10, Tversky index (TI) offers the flexibility in controlling the tradeoff between false-negatives and false-positives  In this subsection, we explore the effect of hyperparameters (i e , $\alpha $ and $\beta $) in TI to test how they manipulate the tradeoff  We conduct experiments on the Chinese OntoNotes4 0 NER dataset and English QuoRef MRC dataset to examine the influence of tradeoff between precision and recall  Experiment results are shown in Table   The highest F1 for Chinese OntoNotes4 0 is 84 67 when $\alpha $ is set to 0 6 while for QuoRef, the highest F1 is 68 44 when $\alpha $ is set to 0 4  In addition, we can observe that the performance varies a lot as $\alpha $ changes in distinct datasets, which shows that the hyperparameters $\alpha ,\beta $ play an important role in the proposed method 
Conclusion
In this paper, we alleviate the severe data imbalance issue in NLP tasks  We propose to use dice loss in replacement of the standard cross-entropy loss, which performs as a soft version of F1 score  Using dice loss can help narrow the gap between training objectives and evaluation metrics  Empirically, we show that the proposed training objective leads to significant performance boost for part-of-speech, named entity recognition, machine reading comprehension and paraphrase identification tasks 

ground_truth: using the machine translation platform Apertium 

machine translation platform Apertium BIBREF5

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 are weights dynamically adjusted?
Context 4:  If present, the word is considered as the ‘LAT’; else, the Noun in close proximity to the question word and following it is returned as the ‘LAT’ 
For questions with question words: ‘When’, ‘Who’, ‘Why’, the ’LAT’ is a question word itself  For the word ‘How', e g  in How many selenoproteins are encoded in the human genome?, we look at the adjective and if we find one, we take it to be the LAT, otherwise the word 'How' is considered as the ‘LAT’ 
Perhaps because of using only very simple rules, the accuracy for ‘LAT’ derivation is 75%; that is, in the remaining 25% of the cases the LAT word is identified incorrectly  Worth noting is that the overall performance the system that used LATs was slightly inferior to the system without LATs, but the types of errors changed  The training used BioBERT with the LAT feature as part of the input string  The errors it introduces usually involve finding the wrong element of the correct type e g  wrong enzyme when two similar enzymes are described in the text, or 'neuron' when asked about a type of cell with a certain function, when the answer calls for a different cell category, adipocytes, and both are mentioned in the text  We feel with more data and additional tuning or perhaps using an ensemble model, we might be able to keep the correct answers, and improve the results on the confusing examples like the one mentioned above  Therefore if we improve our ‘LAT’ derivation logic, or have larger datasets, then perhaps the neural network techniques they will yield better results 
Our Systems and Their Performance on Factoid Questions ::: Impact of Training using BioAsq data (slightly negative)
Training on BioAsq data in our entry in Batch 1 and Batch 2 under the name QA1 showed it might lead to overfitting  This happened both with (Batch 2) and without (Batch 1) hyperparameters tuning: abysmal 18% MRR in Batch 1, and slighly better one, 40% in Batch 2 (although in Batch 2 it was overall the second best result in MRR but 16% lower than the highest score) 
In Batch 3 (only), our UNCC_QA3 system was fine tuned on BioAsq and SQuAD 2 0 BIBREF7, and for data preprocessing Context paragraph is generated from relevant snippets provided in the test data  This system underperformed, by about 2% in MRR, our other entry UNCC_QA1, which was also an overall category winner for this batch  The latter was also trained on SQuAD, but not on BioAsq  We suspect that the reason could be the simplistic nature of the find() function described in Section 3 1  So, this could be an area where a better algorithm for finding the best occurrence of an entity could improve performance 
Our Systems and Their Performance on Factoid Questions ::: Impact of Using Context from URLs (negative)
In some experiments, for context in testing, we used documents for which URL pointers are provided in BioAsq  However, our system UNCC_QA3 underperformed our other system tested only on the provided snippets 
In Batch 5 the underperformance was about 6% of MRR, compared to our best system UNCC_QA1, and by 9% to the top performer 
Performance on Yes/No and List questions
Our work focused on Factoid questions  But we also have done experiments on List-type and Yes/No questions 
Performance on Yes/No and List questions ::: Entailment improves Yes/No accuracy
We started by answering always YES (in batch 2 and 3) to get the baseline performance  For batch 4 we used entailment  Our algorithm was very simple: Given a question we iterate through the candidate sentences and try to find any candidate sentence is contradicting the question (with confidence over 50%), if so 'No' is returned as answer, else 'Yes' is returned  In batch 4 this strategy produced better than the BioAsq baseline performance, and compared to our other systems, the use of entailment increased the performance by about 13% (macro F1 score)  We used 'AllenNlp' BIBREF13 entailment library to find entailment of the candidate sentences with question 
Performance on Yes/No and List questions ::: For List-type the URLs have negative impact
Overall, we followed the similar strategy that's been followed for Factoid Question Answering task  We started our experiment with batch 2, where we submitted 20 best answers (with context from snippets)  Starting with batch 3, we performed post processing: once models generate answer predictions (n-best predictions), we do post-processing on the predicted answers  In test batch 4, our system (called FACTOIDS) achieved highest recall score of ‘0 7033’ but low precision of 0 1119, leaving open the question of how could we have better balanced the two measures 
In the post-processing phase, we take the top ‘20’ (batch 3) and top 5 (batch 4 and 5), predicted answers, tokenize them using common separators: 'comma' , 'and', 'also', 'as well as'  Tokens with characters count more than ‘100’ are eliminated and rest of the tokens are added to the list of possible answers  BioASQ evaluation mechanism does not consider snippets with more than ‘100’ characters as a valid answer  Considering lengthy snippets in to the list of answers would reduce the mean precision score  As a final step, duplicate snippets in the answer pool are removed  For example, consider these top 3 answers predicted by the system (before post-processing):
{
"text": "dendritic cells",
"probability": 0 7554540733426441,
"start_logit": 8 466046333312988,
"end_logit": 9 536355018615723
},
{
"text": "neutrophils, macrophages and
distinct subtypes of dendritic cells",
"probability": 0 13806867348304214,
"start_logit": 6 766478538513184,
"end_logit": 9 536355018615723
},
{
"text": "macrophages and distinct subtypes of dendritic",
"probability": 0 013973475271178242,
"start_logit": 6 766478538513184,
"end_logit": 7 24576473236084
},
After execution of post-processing heuristics, the list of answers returned is as follows:
["dendritic cells"],
["neutrophils"],
["macrophages"],
["distinct subtypes of dendritic cells"]
Summary of our results
The tables below summarize all our results  They show that the performance of our systems was mixed  The simple architectures and algorithm we used worked very well only in Batch 3  However, we feel we can built a better system based on this experience  In particular we observed both the value of contextual embeddings and of feature engineering (LAT), however we failed to combine them properly 
Summary of our results ::: Factoid questions ::: Systems used in Batch 5 experiments
System description for ‘UNCC_QA1’: The system was finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph was generated from relevant snippets provided in the test data 
System description for ‘QA1’ : ‘LAT’ feature was added and finetuned with SQuAD 2 0  For data preprocessing Context / paragraph was generated from relevant snippets provided in the test data 
System description for ‘UNCC_QA3’ : Fine tuning process is same as it is done for the system ‘UNCC_QA1’ in test batch-5  Difference is during data preprocessing, Context/paragraph is generated from the relevant documents for which URLS are included in the test data 
Summary of our results ::: List Questions
For List-type questions, although post processing helped in the later batches, we never managed to obtain competitive precision, although our recall was good 
Summary of our results ::: Yes/No questions
The only thing worth remembering from our performance is that using entailment can have a measurable impact (at least with respect to a weak baseline)  The results (weak) are in Table 3 
Discussion, Future Experiments, and Conclusions ::: Summary:
In contrast to 2018, when we submitted BIBREF2 to BioASQ a system based on extractive summarization (and scored very high in the ideal answer category), this year we mainly targeted factoid question answering task and focused on experimenting with BioBERT  After these experiments we see the promise of BioBERT in QA tasks, but we also see its limitations  The latter we tried to address with mixed results using feature engineering  Overall these experiments allowed us to secure a best and a second best score in different test batches  Along with Factoid-type question, we also tried ‘Yes/No’ and ‘List’-type questions, and did reasonably well with our very simple approach 
For Yes/No the moral worth remembering is that reasoning has a potential to influence results, as evidenced by our adding the AllenNLP entailment BIBREF13 system increased its performance 
All our data and software is available at Github, in the previously referenced URL (end of Section 2) 
Discussion, Future Experiments, and Conclusions ::: Future experiments
In the current model, we have a shallow neural network with a softmax layer for predicting answer span  Shallow networks however are not good at generalizations  In our future experiments we would like to create dense question answering neural network with a softmax layer for predicting answer span  The main idea is to get contextual word embedding for the words present in the question and paragraph (Context) and feed the contextual word embeddings retrieved from the last layer of BioBERT to the dense question answering network  The mentioned dense layered question answering neural network need to be tuned for finding right hyper parameters  An example of such architecture is shown in Fig FIGREF30 
In one more experiment, we would like to add a better version of ‘LAT’ contextual word embedding as a feature, along with the actual contextual word embeddings for question text, and Context and feed them as input to the dense question answering neural network  By this experiment, we would like to find if ‘LAT’ feature is improving overall answer prediction accuracy  Adding ‘LAT’ feature this way instead of feeding this word piece embedding directly to the BioBERT (as we did in our above experiments) would not downgrade the quality of contextual word embeddings generated form ‘BioBERT'  Quality contextual word embeddings would lead to efficient transfer learning and chances are that it would improve the model's answer prediction accuracy 
We also see potential for incorporating domain specific inference into the task e g  using the MedNLI dataset BIBREF14  For all types of experiments it might be worth exploring clinical BERT embeddings BIBREF15, explicitly incorporating domain knowledge (e g  BIBREF16) and possibly deeper discourse representations (e g  BIBREF17) 
APPENDIX
In this appendix we provide additional details about the implementations 
APPENDIX ::: Systems and their descriptions:
We used several variants of our systems when experimenting with the BioASQ problems  In retrospect, it would be much easier to understand the changes if we adopted some mnemonic conventions in naming the systems  So, we apologize for the names that do not reflect the modifications, and necessitate this list 
APPENDIX ::: Systems and their descriptions: ::: Factoid Type Question Answering:
We preprocessed the test data to convert test data to BioBERT format, We generated Context/paragraph by either aggregating relevant snippets provided or by aggregating documents for which URLS are provided in the BioASQ test data 
APPENDIX ::: Systems and their descriptions: ::: System description for QA1:
We generated Context/paragraph by aggregating relevant snippets available in the test data and mapped it against the question text and question id  We ignored the content present in the documents (document URLS were provided in the original test data)  The model is finetuned with BioASQ data 
data preprocessing is done in the same way as it is done for test batch-1  Model fine tuned on BioASQ data 
‘LAT’/ Focus word feature added and fine tuned with SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA_1:
System is finetuned on the SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
‘LAT’/ Focus word feature added and fine tuned with SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
The System is finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA3:
System is finetuned on the SQuAD 2 0 [reference] and BioASQ dataset[] For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
Fine tuning process is same as it is done for the system ‘UNCC_QA_1’ in test batch-5  Difference is during data preprocessing, Context/paragraph is generated form from the relevant documents for which URLS are included in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA2:
Fine tuning process is same as for ‘UNCC_QA_1 ’  Difference is Context/paragraph is generated form from the relevant documents for which URLS are included in the test data  System ‘UNCC_QA_1’ got the highest ‘MRR’ score in the 3rd test batch set 
APPENDIX ::: Systems and their descriptions: ::: System description for FACTOIDS:
The System is finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: List Type Questions:
We attempted List type questions starting from test batch ‘2’  Used similar approach that's been followed for Factoid Question answering task  For all the test batch sets, in the data pre processing phase Context/ paragraph is generated either by aggregating relevant snippets or by aggregating documents(URLS) provided in the BioASQ test data 
For test batch-2, model (System: QA1) is finetuned on BioASQ data and submitted top ‘20’ answers predicted by the model as the list of answers  system ‘QA1’ achieved low F-Measure score:‘0 0786’ in the second test batch  In the further test batches for List type questions, we finetuned the model on Squad data set [reference], implemented post processing techniques (refer section 5 2) and achieved a better F-measure score: ‘0 2862’ in the final test batch set 
In test batch-3 (Systems : ‘QA1’/’’UNCC_QA_1’/’UNCC_QA3’/’UNCC_QA2’) top 20 answers returned by the model is sent for post processing and in test batch 4 and 5 only top 5 answers are sent for post processing  System UNCC_QA2(in batch 3) for List type question answering, Context is generated from documents for which URLS are provided in the BioASQ test data  for the rest of the systems (in test batch-3) for List Type question answering task snippets present in the BioaSQ test data are used to generate context 
In test batch-4 (System : ‘FACTOIDS’/’UNCC_QA_1’/’UNCC_QA3’’) top 5 answers returned by the model is sent for post processing  In case of system ‘FACTOIDS’ snippets in the test data were used to generate context  for systems ’UNCC_QA_1’ and ’UNCC_QA3’ context is generated from the documents for which URLS are provided in the BioASQ test data 
In test batch-5 ( Systems: ‘QA1’/’UNCC_QA_1’/’UNCC_QA3’/’UNCC_QA2’ ) our approach is the same as that of test batch-4 where top 5 answers returned by the model is sent for post processing  for all the systems (in test batch-5) context is generated from the snippets provided in the BioASQ test data 
APPENDIX ::: Systems and their descriptions: ::: Yes/No Type Questions:
For the first 3 test batches, We have submitted answer ‘Yes’ to all the questions  Later, we employed ‘Sentence Entailment’ techniques(refer section 6 0) for the fourth and fifth test batch sets  Our Systems with ‘Sentence Entailment’ approach (for ‘Yes’/ ‘No’ question answering): ‘UNCC_QA_1’(test batch-4), UNCC_QA3(test batch-5) 
APPENDIX ::: Additional details for Yes/No Type Questions
We used Textual Entailment in Batch 4 and 5 for ‘Yes’/‘No’ question type  The algorithm was very simple: Given a question we iterate through the candidate sentences, and look for any candidate sentences contradicting the question  If we find one 'No' is returned as answer, else 'Yes' is returned  (The confidence for contradiction was set at 50%) We used AllenNLP BIBREF13 entailment library to find entailment of the candidate sentences with question 
Flow Chart for Yes/No Question answer processing is shown in Fig FIGREF51
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions
There are different question types, and we distinguished them based on the question words: ‘Which’, ‘What’, ‘When’, ‘How’ etc  Each type of question is being handled differently and there are commonalities among the rules written for different question types  How are question words identified? question words have parts of speech(POS): 'WDT', 'WRB', 'WP' 
Assumptions:
1) Lexical answer type (‘LAT’) or focus word is of type Noun and follows the question word 
2) The LAT word is a Subject  (This clearly not always true, but we used a very simple method)  Note: ‘StanfordNLP’ dependency parsing tag for identifying subject is 'nsubj' or 'nsubjpass' 
3) When a question has multiple words that are of type Subject (and Noun), a word that is in proximity to the question word is considered as ‘LAT’ 
4) For questions with question words: ‘When’, ‘Who’, ‘Why’, the ’LAT’ is a question word itself that is, ‘When’, ‘Who’, ‘Why’ respectively 
Rules and logic flow to traverse a question: The three cases below describe the logic flow of finding LATs  The figures show the grammatical structures used for this purpose 
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-1:
Question with question word ‘How’ 
For questions with question word 'How', the adjective that follows the question word is considered as ‘LAT’ (need not follow immediately)  If an adjective is absent, word 'How' is considered as ‘LAT’  When there are multiple words that are adjectives, a word in close proximity to the question word and follows it is returned as ‘LAT’  Note: The part of speech tag to identify adjectives is 'JJ'  For Other possible question words like ‘whose’  ‘LAT’/Focus word is question words itself 
Example Question: How many selenoproteins are encoded in the human genome?
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-2:
Questions with question words ‘Which’ , ‘What’ and all other possible question words; a 'Noun' immediately following the question word 
Example Question: Which enzyme is targeted by Evolocumab?
Here, Focus word/LAT is ‘enzyme’ which is both Noun and Subject and immediately follows the question word 
When the word immediately following the question word is a noun, the window size is set to ‘3’  This size ‘3’ means that we iterate through the next ‘3’ words (if present) to check if any of the word is both 'Noun' and 'Subject', If so, the word is considered as ‘LAT’/Focus Word  Else the word that is present very next to the question word is considered as ‘LAT’ 
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-3:
Questions with question words ‘Which’ , ‘What’ and all other possible question words; word immediately following the question word is not a 'Noun' 
Example Question: What is the function of the protein Magt1?
Here, Focus word/LAT is ‘function ’ which is both Noun and Subject and does not immediately follow the question word 
When the very next word following the question word is not a Noun, window size is set to ‘5’  Window size ‘5’ corresponds that we iterate through the next ‘5’ words (if present) and search for the word that is both Noun and Subject  If present, the word is considered as ‘LAT’  Else, the 'Noun' close proximity to the question word and follows it is returned as ‘LAT’ 
Ad we mentioned earlier, the accuracy for ‘LAT’ derivation is 75 percent  But clearly the simple logic described above can be improved, as shown in BIBREF9, BIBREF10  Whether this in turn produces improvements in this particular task is an open question 
APPENDIX ::: Proposing Future Experiments
In the current model, we have a shallow neural network with a softmax layer for predicting answer span  Shallow networks however are not good at generalizations  In our future experiments we would like to create dense question answering neural network with a softmax layer for predicting answer span  The main idea is to get contextual word embedding for the words present in the question and paragraph (Context) and feed the contextual word embeddings retrieved from the last layer of BioBERT to the dense question answering network  The mentioned dense layered question answering Neural network need to be tuned for finding right hyper parameters  An example of such architecture is shown in Fig FIGREF30 
In another experiment we would like to only feed contextual word embeddings for Focus word/ ‘LAT’, paragraph/ Context as input to the question answering neural network  In this experiment we would neglect all embeddings for the question text except that of Focus word/ ‘LAT’  Our assumption and idea for considering focus word and neglecting remaining words in the question is that during training phase it would make more precise for the model to identify the focus of the question and map answers against the question’s focus  To validate our assumption, we would like to take sample question answering data and find the cosine distance between contextual embedding of Focus word and that of the actual answer and verify if the cosine distance is comparatively low in most of the cases 
In one more experiment, we would like to add a better version of ‘LAT’ contextual word embedding as a feature, along with the actual contextual word embeddings for question text, and Context and feed them as input to the dense question answering neural network  By this experiment, we would like to find if ‘LAT’ feature is improving overall answer prediction accuracy  Adding ‘LAT’ feature this way instead of feeding Focus word’s word piece embedding directly (as we did in our above experiments) to the BioBERT would not downgrade the quality of contextual word embeddings generated form ‘BioBERT'  Quality contextual word embeddings would lead to efficient transfer learning and chances are that it would improve the model's answer prediction accuracy 

ground_truth: One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds. The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples. For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them. Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified.

associates each training example with a weight in proportion to $(1-p)$, and this weight dynamically changes as training proceeds