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 NMT models did they compare with?
Context 1: Introduction
Ancient Chinese is the writing language in ancient China  It is a treasure of Chinese culture which brings together the wisdom and ideas of the Chinese nation and chronicles the ancient cultural heritage of China  Learning ancient Chinese not only helps people to understand and inherit the wisdom of the ancients, but also promotes people to absorb and develop Chinese culture 
However, it is difficult for modern people to read ancient Chinese  Firstly, compared with modern Chinese, ancient Chinese is more concise and shorter  The grammatical order of modern Chinese is also quite different from that of ancient Chinese  Secondly, most modern Chinese words are double syllables, while the most of the ancient Chinese words are monosyllabic  Thirdly, there is more than one polysemous phenomenon in ancient Chinese  In addition, manual translation has a high cost  Therefore, it is meaningful and useful to study the automatic translation from ancient Chinese to modern Chinese  Through ancient-modern Chinese translation, the wisdom, talent and accumulated experience of the predecessors can be passed on to more people 
Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 has achieved remarkable performance on many bilingual translation tasks  It is an end-to-end learning approach for machine translation, with the potential to show great advantages over the statistic machine translation (SMT) systems  However, NMT approach has not been widely applied to the ancient-modern Chinese translation task  One of the main reasons is the limited high-quality parallel data resource 
The most popular method of acquiring translation examples is bilingual text alignment BIBREF5   This kind of method can be classified into two types: lexical-based and statistical-based  The lexical-based approaches BIBREF6 , BIBREF7 focus on lexical information, which utilize the bilingual dictionary BIBREF8 , BIBREF9 or lexical features  Meanwhile, the statistical-based approaches BIBREF10 , BIBREF11 rely on statistical information, such as sentence length ratio in two languages and align mode probability 
However, these methods are designed for other bilingual language pairs that are written in different language characters (e g  English-French, Chinese-Japanese)  The ancient-modern Chinese has some characteristics that are quite different from other language pairs  For example, ancient and modern Chinese are both written in Chinese characters, but ancient Chinese is highly concise and its syntactical structure is different from modern Chinese  The traditional methods do not take these characteristics into account  In this paper, we propose an effective ancient-modern Chinese text alignment method at the level of clause based on the characteristics of these two languages  The proposed method combines both lexical-based information and statistical-based information, which achieves 94 2 F1-score on Test set  Recently, a simple longest common subsequence based approach for ancient-modern Chinese sentence alignment is proposed in BIBREF12   Our experiments showed that our proposed alignment approach performs much better than their method 
We apply the proposed method to create a large translation parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  Furthermore, we test SMT models and various NMT models on the created dataset and provide a strong baseline for this task 
Overview
There are four steps to build the ancient-modern Chinese translation dataset: (i) The parallel corpus crawling and cleaning  (ii) The paragraph alignment  (iii) The clause alignment based on aligned paragraphs  (iv) Augmenting data by merging aligned adjacent clauses  The most critical step is the third step 
Clause Alignment
In the clause alignment step, we combine both statistical-based and lexical-based information to measure the score for each possible clause alignment between ancient and modern Chinese strings  The dynamic programming is employed to further find overall optimal alignment paragraph by paragraph  According to the characteristics of the ancient and modern Chinese languages, we consider the following factors to measure the alignment score INLINEFORM0 between a bilingual clause pair:
Lexical Matching  The lexical matching score is used to calculate the matching coverage of the ancient clause INLINEFORM0   It contains two parts: exact matching and dictionary matching  An ancient Chinese character usually corresponds to one or more modern Chinese words  In the first part, we carry out Chinese Word segmentation to the modern Chinese clause INLINEFORM1   Then we match the ancient characters and modern words in the order from left to right  In further matching, the words that have been matched will be deleted from the original clauses 
However, some ancient characters do not appear in its corresponding modern Chinese words  An ancient Chinese dictionary is employed to address this issue  We preprocess the ancient Chinese dictionary and remove the stop words  In this dictionary matching step, we retrieve the dictionary definition of each unmatched ancient character and use it to match the remaining modern Chinese words  To reduce the impact of universal word matching, we use Inverse Document Frequency (IDF) to weight the matching words  The lexical matching score is calculated as: DISPLAYFORM0
The above equation is used to calculate the matching coverage of the ancient clause INLINEFORM0   The first term of equation ( EQREF8 ) represents exact matching score  INLINEFORM1 denotes the length of INLINEFORM2 , INLINEFORM3 denotes each ancient character in INLINEFORM4 , and the indicator function INLINEFORM5 indicates whether the character INLINEFORM6 can match the words in the clause INLINEFORM7   The second term is dictionary matching score  Here INLINEFORM8 and INLINEFORM9 represent the remaining unmatched strings of INLINEFORM10 and INLINEFORM11 , respectively  INLINEFORM12 denotes the INLINEFORM13 -th character in the dictionary definition of the INLINEFORM14 and its IDF score is denoted as INLINEFORM15   The INLINEFORM16 is a predefined parameter which is used to normalize the IDF score  We tuned the value of this parameter on the Dev set 
Statistical Information  Similar to BIBREF11 and BIBREF6 , the statistical information contains alignment mode and length information  There are many alignment modes between ancient and modern Chinese languages  If one ancient Chinese clause aligns two adjacent modern Chinese clauses, we call this alignment as 1-2 alignment mode  We show some examples of different alignment modes in Figure FIGREF9   In this paper, we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes which account for INLINEFORM0 of the Dev set  We estimate the probability Pr INLINEFORM1 n-m INLINEFORM2 of each alignment mode n-m on the Dev set  To utilize length information, we make an investigation on length correlation between these two languages  Based on the assumption of BIBREF11 that each character in one language gives rise to a random number of characters in the other language and those random variables INLINEFORM3 are independent and identically distributed with a normal distribution, we estimate the mean INLINEFORM4 and standard deviation INLINEFORM5 from the paragraph aligned parallel corpus  Given a clause pair INLINEFORM6 , the statistical information score can be calculated by: DISPLAYFORM0
where INLINEFORM0 denotes the normal distribution probability density function 
Edit Distance  Because ancient and modern Chinese are both written in Chinese characters, we also consider using the edit distance  It is a way of quantifying the dissimilarity between two strings by counting the minimum number of operations (insertion, deletion, and substitution) required to transform one string into the other  Here we define the edit distance score as: DISPLAYFORM0
Dynamic Programming  The overall alignment score for each possible clause alignment is as follows: DISPLAYFORM0
Here INLINEFORM0 and INLINEFORM1 are pre-defined interpolation factors  We use dynamic programming to find the overall optimal alignment paragraph by paragraph  Let INLINEFORM2 be total alignment scores of aligning the first to INLINEFORM3 -th ancient Chinese clauses with the first to to INLINEFORM4 -th modern Chinese clauses, and the recurrence then can be described as follows: DISPLAYFORM0
Where INLINEFORM0 denotes concatenate clause INLINEFORM1 to clause INLINEFORM2   As we discussed above, here we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes 
Ancient-Modern Chinese Dataset
Data Collection  To build the large ancient-modern Chinese dataset, we collected 1 7K bilingual ancient-modern Chinese articles from the internet  More specifically, a large part of the ancient Chinese data we used come from ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era  They used plain and accurate words to express what happened at that time, and thus ensure the generality of the translated materials 
Paragraph Alignment  To further ensure the quality of the new dataset, the work of paragraph alignment is manually completed  After data cleaning and manual paragraph alignment, we obtained 35K aligned bilingual paragraphs 
Clause Alignment  We applied our clause alignment algorithm on the 35K aligned bilingual paragraphs and obtained 517K aligned bilingual clauses  The reason we use clause alignment algorithm instead of sentence alignment is because we can construct more aligned sentences more flexibly and conveniently  To be specific, we can get multiple additional sentence level bilingual pairs by “data augmentation” 
Data Augmentation  We augmented the data in the following way: Given an aligned clause pair, we merged its adjacent clause pairs as a new sample pair  For example, suppose we have three adjacent clause level bilingual pairs: ( INLINEFORM0 , INLINEFORM1 ), ( INLINEFORM2 , INLINEFORM3 ), and ( INLINEFORM4 , INLINEFORM5 )  We can get some additional sentence level bilingual pairs, such as: ( INLINEFORM6 , INLINEFORM7 ) and ( INLINEFORM8 , INLINEFORM9 )  Here INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 are adjacent clauses in the original paragraph, and INLINEFORM13 denotes concatenate clause INLINEFORM14 to clause INLINEFORM15   The advantage of using this data augmentation method is that compared with only using ( INLINEFORM16 , INLINEFORM17 ) as the training data, we can also use ( INLINEFORM18 , INLINEFORM19 ) and ( INLINEFORM20 , INLINEFORM21 ) as the training data, which can provide richer supervision information for the model and make the model learn the align information between the source language and the target language better  After the data augmentation, we filtered the sentences which are longer than 50 or contain more than four clause pairs 
Dataset Creation  Finally, we split the dataset into three sets: training (Train), development (Dev) and testing (Test)  Note that the unaugmented dataset contains 517K aligned bilingual clause pairs from 35K aligned bilingual paragraphs  To keep all the sentences in different sets come from different articles, we split the 35K aligned bilingual paragraphs into Train, Dev and Test sets following these ratios respectively: 80%, 10%, 10%  Before data augmentation, the unaugmented Train set contains INLINEFORM0 aligned bilingual clause pairs from 28K aligned bilingual paragraphs  Then we augmented the Train, Dev and Test sets respectively  Note that the augmented Train, Dev and Test sets also contain the unaugmented data  The statistical information of the three data sets is shown in Table TABREF17   We show some examples of data in Figure FIGREF14  
RNN-based NMT model
We first briefly introduce the RNN based Neural Machine Translation (RNN-based NMT) model  The RNN-based NMT with attention mechanism BIBREF0 has achieved remarkable performance on many translation tasks  It consists of encoder and decoder part 
We firstly introduce the encoder part  The input word sequence of source language are individually mapped into a INLINEFORM0 -dimensional vector space INLINEFORM1   Then a bi-directional RNN BIBREF15 with GRU BIBREF16 or LSTM BIBREF17 cell converts these vectors into a sequences of hidden states INLINEFORM2  
For the decoder part, another RNN is used to generate target sequence INLINEFORM0   The attention mechanism BIBREF0 , BIBREF18 is employed to allow the decoder to refer back to the hidden state sequence and focus on a particular segment  The INLINEFORM1 -th hidden state INLINEFORM2 of decoder part is calculated as: DISPLAYFORM0
Here g INLINEFORM0 is a linear combination of attended context vector c INLINEFORM1 and INLINEFORM2 is the word embedding of (i-1)-th target word: DISPLAYFORM0
The attended context vector c INLINEFORM0 is computed as a weighted sum of the hidden states of the encoder: DISPLAYFORM0
The probability distribution vector of the next word INLINEFORM0 is generated according to the following: DISPLAYFORM0
We take this model as the basic RNN-based NMT model in the following experiments 
Transformer-NMT
Recently, the Transformer model BIBREF4 has made remarkable progress in machine translation  This model contains a multi-head self-attention encoder and a multi-head self-attention decoder 
As proposed by BIBREF4 , an attention function maps a query and a set of key-value pairs to an output, where the queries INLINEFORM0 , keys INLINEFORM1 , and values INLINEFORM2 are all vectors  The input consists of queries and keys of dimension INLINEFORM3 , and values of dimension INLINEFORM4   The attention function is given by: DISPLAYFORM0
Multi-head attention mechanism projects queries, keys and values to INLINEFORM0 different representation subspaces and calculates corresponding attention  The attention function outputs are concatenated and projected again before giving the final output  Multi-head attention allows the model to attend to multiple features at different positions 
The encoder is composed of a stack of INLINEFORM0 identical layers  Each layer has two sub-layers: multi-head self-attention mechanism and position-wise fully connected feed-forward network  Similarly, the decoder is also composed of a stack of INLINEFORM1 identical layers  In addition to the two sub-layers in each encoder layer, the decoder contains a third sub-layer which performs multi-head attention over the output of the encoder stack (see more details in BIBREF4 ) 
Experiments
Our experiments revolve around the following questions: Q1: As we consider three factors for clause alignment, do all these factors help? How does our method compare with previous methods? Q2: How does the NMT and SMT models perform on this new dataset we build?
Clause Alignment Results (Q1)
In order to evaluate our clause alignment algorithm, we manually aligned bilingual clauses from 37 bilingual ancient-modern Chinese articles, and finally got 4K aligned bilingual clauses as the Test set and 2K clauses as the Dev set 
Metrics  We used F1-score and precision score as the evaluation metrics  Suppose that we get INLINEFORM0 bilingual clause pairs after running the algorithm on the Test set, and there are INLINEFORM1 bilingual clause pairs of these INLINEFORM2 pairs are in the ground truth of the Test set, the precision score is defined as INLINEFORM3 (the algorithm gives INLINEFORM4 outputs, INLINEFORM5 of which are correct)  And suppose that the ground truth of the Test set contains INLINEFORM6 bilingual clause pairs, the recall score is INLINEFORM7 (there are INLINEFORM8 ground truth samples, INLINEFORM9 of which are output by the algorithm), then the F1-score is INLINEFORM10  
Baselines  Since the related work BIBREF10 , BIBREF11 can be seen as the ablation cases of our method (only statistical score INLINEFORM0 with dynamic programming), we compared the full proposed method with its variants on the Test set for ablation study  In addition, we also compared our method with the longest common subsequence (LCS) based approach proposed by BIBREF12   To the best of our knowledge, BIBREF12 is the latest related work which are designed for Ancient-Modern Chinese alignment 
Hyper-parameters  For the proposed method, we estimated INLINEFORM0 and INLINEFORM1 on all aligned paragraphs  The probability Pr INLINEFORM2 n-m INLINEFORM3 of each alignment mode n-m was estimated on the Dev set  For the hyper-parameters INLINEFORM4 , INLINEFORM5 and INLINEFORM6 , the grid search was applied to tune them on the Dev set  In order to show the effect of hyper-parameters INLINEFORM7 , INLINEFORM8 , and INLINEFORM9 , we reported the results of various hyper-parameters on the Dev set in Table TABREF26   Based on the results of grid search on the Dev set, we set INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 in the following experiment  The Jieba Chinese text segmentation is employed for modern Chinese word segmentation 
Results  The results on the Test set are shown in Table TABREF28 , the abbreviation w/o means removing a particular part from the setting  From the results, we can see that the lexical matching score is the most important among these three factors, and statistical information score is more important than edit distance score  Moreover, the dictionary term in lexical matching score significantly improves the performance  From these results, we obtain the best setting that involves all these three factors  We used this setting for dataset creation  Furthermore, the proposed method performs much better than LCS BIBREF12  
Translation Results (Q2)
In this experiment, we analyzed and compared the performance of the SMT and various NMT models on our built dataset  To verify the effectiveness of our data augmented method  We trained the NMT and SMT models on both unaugmented dataset (including 0 46M training pairs) and augmented dataset, and test all the models on the same Test set which is augmented  The models to be tested and their configurations are as follows:
SMT  The state-of-art Moses toolkit BIBREF19 was used to train SMT model  We used KenLM BIBREF20 to train a 5-gram language model, and the GIZA++ toolkit to align the data 
RNN-based NMT  The basic RNN-based NMT model is based on BIBREF0 which is introduced above  Both the encoder and decoder used 2-layer RNN with 1024 LSTM cells, and the encoder is a bi-directional RNN  The batch size, threshold of element-wise gradient clipping and initial learning rate of Adam optimizer BIBREF21 were set to 128, 5 0 and 0 001  When trained the model on augmented dataset, we used 4-layer RNN  Several techniques were investigated to train the model, including layer-normalization BIBREF22 , RNN-dropout BIBREF23 , and learning rate decay BIBREF1   The hyper-parameters were chosen empirically and adjusted in the Dev set  Furthermore, we tested the basic NMT model with several techniques, such as target language reversal BIBREF24 (reversing the order of the words in all target sentences, but not source sentences), residual connection BIBREF25 and pre-trained word2vec BIBREF26   For word embedding pre-training, we collected an external ancient corpus which contains INLINEFORM0 134M tokens 
Transformer-NMT  We also trained the Transformer model BIBREF4 which is a strong baseline of NMT on both augmented and unaugmented parallel corpus  The training configuration of the Transformer model is shown in Table TABREF32   The hyper-parameters are set based on the settings in the paper BIBREF4 and the sizes of our training sets 
For the evaluation, we used the average of 1 to 4 gram BLEUs multiplied by a brevity penalty BIBREF27 which computed by multi-bleu perl in Moses as metrics  The results are reported in Table TABREF34   For RNN-based NMT, we can see that target language reversal, residual connection, and word2vec can further improve the performance of the basic RNN-based NMT model  However, we find that word2vec and reversal tricks seem no obvious improvement when trained the RNN-based NMT and Transformer models on augmented parallel corpus  For SMT, it performs better than NMT models when they were trained on the unaugmented dataset  Nevertheless, when trained on the augmented dataset, both the RNN-based NMT model and Transformer based NMT model outperform the SMT model  In addition, as with other translation tasks BIBREF4 , the Transformer also performs better than RNN-based NMT 
Because the Test set contains both augmented and unaugmented data, it is not surprising that the RNN-based NMT model and Transformer based NMT model trained on unaugmented data would perform poorly  In order to further verify the effect of data augmentation, we report the test results of the models on only unaugmented test data (including 48K test pairs) in Table TABREF35   From the results, it can be seen that the data augmentation can still improve the models 
Analysis
The generated samples of various models are shown in Figure FIGREF36   Besides BLEU scores, we analyze these examples from a human perspective and draw some conclusions  At the same time, we design different metrics and evaluate on the whole Test set to support our conclusions as follows:
On the one hand, we further compare the translation results from the perspective of people  We find that although the original meaning can be basically translated by SMT, its translation results are less smooth when compared with the other two NMT models (RNN-based NMT and Transformer)  For example, the translations of SMT are usually lack of auxiliary words, conjunctions and function words, which is not consistent with human translation habits  To further confirm this conclusion, the average length of the translation results of the three models are measured (RNN-based NMT:17 12, SMT:15 50, Transformer:16 78, Reference:16 47)  We can see that the average length of the SMT outputs is shortest, and the length gaps between the SMT outputs and the references are largest  Meanwhile, the average length of the sentences translated by Transformer is closest to the average length of references  These results indirectly verify our point of view, and show that the NMT models perform better than SMT in this task 
On the other hand, there still exists some problems to be solved  We observe that translating proper nouns and personal pronouns (such as names, place names and ancient-specific appellations) is very difficult for all of these models  For instance, the ancient Chinese appellation `Zhen' should be translated into `Wo' in modern Chinese  Unfortunately, we calculate the accurate rate of some special words (such as `Zhen',`Chen' and `Gua'), and find that this rate is very low (the accurate rate of translating `Zhen' are: RNN-based NMT:0 14, SMT:0 16, Transformer:0 05)  We will focus on this issue in the future 
Conclusion and Future Work
We propose an effective ancient-modern Chinese clause alignment method which achieves 94 2 F1-score on Test set  Based on it, we build a large scale parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  In addition, we test the performance of the SMT and various NMT models on our built dataset and provide a strong NMT baseline for this task which achieves 27 16 BLEU score (4-gram)  We further analyze the performance of the SMT and various NMT models and summarize some specific problems that machine translation models will encounter when translating ancient Chinese 
For the future work, firstly, we are going to expand the dataset using the proposed method continually  Secondly, we will focus on solving the problem of proper noun translation and improve the translation system according to the features of ancient Chinese translation  Finally, we plan to introduce some techniques of statistical translation into neural machine translation to improve the performance 
This work is supported by National Natural Science Fund for Distinguished Young Scholar (Grant No  61625204) and partially supported by the State Key Program of National Science Foundation of China (Grant Nos  61836006 and 61432014) 
Context 2: 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

ground_truth: RNN-based NMT model, Transformer-NMT

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 method's improvements of F1 for NER task for English and Chinese datasets?
Context 2: 
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 
Context 3: Introduction
Rendering natural language descriptions from structured data is required in a wide variety of commercial applications such as generating descriptions of products, hotels, furniture, etc , from a corresponding table of facts about the entity  Such a table typically contains {field, value} pairs where the field is a property of the entity (e g , color) and the value is a set of possible assignments to this property (e g , color = red)  Another example of this is the recently introduced task of generating one line biography descriptions from a given Wikipedia infobox BIBREF0   The Wikipedia infobox serves as a table of facts about a person and the first sentence from the corresponding article serves as a one line description of the person  Figure FIGREF2 illustrates an example input infobox which contains fields such as Born, Residence, Nationality, Fields, Institutions and Alma Mater  Each field further contains some words (e g , particle physics, many-body theory, etc )  The corresponding description is coherent with the information contained in the infobox 
Note that the number of fields in the infobox and the ordering of the fields within the infobox varies from person to person  Given the large size (700K examples) and heterogeneous nature of the dataset which contains biographies of people from different backgrounds (sports, politics, arts, etc ), it is hard to come up with simple rule-based templates for generating natural language descriptions from infoboxes, thereby making a case for data-driven models  Based on the recent success of data-driven neural models for various other NLG tasks BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , one simple choice is to treat the infobox as a sequence of {field, value} pairs and use a standard seq2seq model for this task  However, such a model is too generic and does not exploit the specific characteristics of this task as explained below  First, note that while generating such descriptions from structured data, a human keeps track of information at two levels  Specifically, at a macro level, she would first decide which field to mention next and then at a micro level decide which of the values in the field needs to be mentioned next  For example, she first decides that at the current step, the field occupation needs attention and then decides which is the next appropriate occupation to attend to from the set of occupations (actor, director, producer, etc )  To enable this, we use a bifocal attention mechanism which computes an attention over fields at a macro level and over values at a micro level  We then fuse these attention weights such that the attention weight for a field also influences the attention over the values within it  Finally, we feed a fused context vector to the decoder which contains both field level and word level information  Note that such two-level attention mechanisms BIBREF6 , BIBREF7 , BIBREF8 have been used in the context of unstructured data (as opposed to structured data in our case), where at a macro level one needs to pay attention to sentences and at a micro level to words in the sentences 
Next, we observe that while rendering the output, once the model pays attention to a field (say, occupation) it needs to stay on this field for a few timesteps (till all the occupations are produced in the output)  We refer to this as the stay on behavior  Further, we note that once the tokens of a field are referred to, they are usually not referred to later  For example, once all the occupations have been listed in the output we will never visit the occupation field again because there is nothing left to say about it  We refer to this as the never look back behavior  To model the stay on behaviour, we introduce a forget (or remember) gate which acts as a signal to decide when to forget the current field (or equivalently to decide till when to remember the current field)  To model the never look back behaviour we introduce a gated orthogonalization mechanism which ensures that once a field is forgotten, subsequent field context vectors fed to the decoder are orthogonal to (or different from) the previous field context vectors 
We experiment with the WikiBio dataset BIBREF0 which contains around 700K {infobox, description} pairs and has a vocabulary of around 400K words  We show that the proposed model gives a relative improvement of 21% and 20% as compared to current state of the art models BIBREF0 , BIBREF9 on this dataset  The proposed model also gives a relative improvement of 10% as compared to the basic seq2seq model  Further, we introduce new datasets for French and German on the same lines as the English WikiBio dataset  Even on these two datasets, our model outperforms the state of the art methods mentioned above 
Related work
Natural Language Generation has always been of interest to the research community and has received a lot of attention in the past  The approaches for NLG range from (i) rule based approaches (e g , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 ) (ii) modular statistical approaches which divide the process into three phases (planning, selection and surface realization) and use data driven approaches for one or more of these phases BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 (iii) hybrid approaches which rely on a combination of handcrafted rules and corpus statistics BIBREF20 , BIBREF21 , BIBREF22 and (iv) the more recent neural network based models BIBREF1  
Neural models for NLG have been proposed in the context of various tasks such as machine translation BIBREF1 , document summarization BIBREF2 , BIBREF4 , paraphrase generation BIBREF23 , image captioning BIBREF24 , video summarization BIBREF25 , query based document summarization BIBREF5 and so on  Most of these models are data hungry and are trained on large amounts of data  On the other hand, NLG from structured data has largely been studied in the context of small datasets such as WeatherGov BIBREF26 , RoboCup BIBREF27 , NFL Recaps BIBREF15 , Prodigy-Meteo BIBREF28 and TUNA Challenge BIBREF29   Recently weather16 proposed RNN/LSTM based neural encoder-decoder models with attention for WeatherGov and RoboCup datasets 
Unlike the datasets mentioned above, the biography dataset introduced by lebret2016neural is larger (700K {table, descriptions} pairs) and has a much larger vocabulary (400K words as opposed to around 350 or fewer words in the above datasets)  Further, unlike the feed-forward neural network based model proposed by BIBREF0 we use a sequence to sequence model and introduce components to address the peculiar characteristics of the task  Specifically, we introduce neural components to address the need for attention at two levels and to address the stay on and never look back behaviour required by the decoder  KiddonZC16 have explored the use of checklists to track previously visited ingredients while generating recipes from ingredients  Note that two-level attention mechanisms have also been used in the context of summarization BIBREF6 , document classification BIBREF7 , dialog systems BIBREF8 , etc  However, these works deal with unstructured data (sentences at the higher level and words at a lower level) as opposed to structured data in our case 
Proposed model
As input we are given an infobox INLINEFORM0 , which is a set of pairs INLINEFORM1 where INLINEFORM2 corresponds to field names and INLINEFORM3 is the sequence of corresponding values and INLINEFORM4 is the total number of fields in INLINEFORM5   For example, INLINEFORM6 could be one such pair in this set  Given such an input, the task is to generate a description INLINEFORM7 containing INLINEFORM8 words  A simple solution is to treat the infobox as a sequence of fields followed by the values corresponding to the field in the order of their appearance in the infobox  For example, the infobox could be flattened to produce the following input sequence (the words in bold are field names which act as delimiters)
[Name] John Doe [Birth_Date] 19 March 1981 [Nationality] Indian      
The problem can then be cast as a seq2seq generation problem and can be modeled using a standard neural architecture comprising of three components (i) an input encoder (using GRU/LSTM cells), (ii) an attention mechanism to attend to important values in the input sequence at each time step and (iii) a decoder to decode the output one word at a time (again, using GRU/LSTM cells)  However, this standard model is too generic and does not exploit the specific characteristics of this task  We propose additional components, viz , (i) a fused bifocal attention mechanism which operates on fields (macro) and values (micro) and (ii) a gated orthogonalization mechanism to model stay on and never look back behavior 
Fused Bifocal Attention Mechanism
Intuitively, when a human writes a description from a table she keeps track of information at two levels  At the macro level, it is important to decide which is the appropriate field to attend to next and at a micro level (i e , within a field) it is important to know which values to attend to next  To capture this behavior, we use a bifocal attention mechanism as described below 
Macro Attention: Consider the INLINEFORM0 -th field INLINEFORM1 which has values INLINEFORM2   Let INLINEFORM3 be the representation of this field in the infobox  This representation can either be (i) the word embedding of the field name or (ii) some function INLINEFORM4 of the values in the field or (iii) a concatenation of (i) and (ii)  The function INLINEFORM5 could simply be the sum or average of the embeddings of the values in the field  Alternately, this function could be a GRU (or LSTM) which treats these values within a field as a sequence and computes the field representation as the final representation of this sequence (i e , the representation of the last time-step)  We found that bidirectional GRU is a better choice for INLINEFORM6 and concatenating the embedding of the field name with this GRU representation works best  Further, using a bidirectional GRU cell to take contextual information from neighboring fields also helps (these are the orange colored cells in the top-left block in Figure FIGREF3 with macro attention)  Given these representations INLINEFORM7 for all the INLINEFORM8 fields we compute an attention over the fields (macro level)  DISPLAYFORM0
where INLINEFORM0 is the state of the decoder at time step INLINEFORM1   INLINEFORM2 and INLINEFORM3 are parameters, INLINEFORM4 is the total number of fields in the input, INLINEFORM5 is the macro (field level) context vector at the INLINEFORM6 -th time step of the decoder 
Micro Attention: Let INLINEFORM0 be the representation of the INLINEFORM1 -th value in a given field  This representation could again either be (i) simply the embedding of this value (ii) or a contextual representation computed using a function INLINEFORM2 which also considers the other values in the field  For example, if INLINEFORM3 are the values in a field then these values can be treated as a sequence and the representation of the INLINEFORM4 -th value can be computed using a bidirectional GRU over this sequence  Once again, we found that using a bi-GRU works better then simply using the embedding of the value  Once we have such a representation computed for all values across all the fields, we compute the attention over these values (micro level) as shown below : DISPLAYFORM0
where INLINEFORM0 is the state of the decoder at time step INLINEFORM1   INLINEFORM2 and INLINEFORM3 are parameters, INLINEFORM4 is the total number of values across all the fields 
Fused Attention: Intuitively, the attention weights assigned to a field should have an influence on all the values belonging to the particular field  To ensure this, we reweigh the micro level attention weights based on the corresponding macro level attention weights  In other words, we fuse the attention weights at the two levels as: DISPLAYFORM0
where INLINEFORM0 is the field corresponding to the INLINEFORM1 -th value, INLINEFORM2 is the macro level context vector 
Gated Orthogonalization for Modeling Stay-On and Never Look Back behaviour
We now describe a series of choices made to model stay-on and never look back behavior  We first begin with the stay-on property which essentially implies that if we have paid attention to the field INLINEFORM0 at timestep INLINEFORM1 then we are likely to pay attention to the same field for a few more time steps  For example, if we are focusing on the occupation field at this timestep then we are likely to focus on it for the next few timesteps till all relevant values in this field have been included in the generated description  In other words, we want to remember the field context vector INLINEFORM2 for a few timesteps  One way of ensuring this is to use a remember (or forget) gate as given below which remembers the previous context vector when required and forgets it when it is time to move on from that field  DISPLAYFORM0
where INLINEFORM0 are parameters to be learned  The job of the forget gate is to ensure that INLINEFORM1 is similar to INLINEFORM2 when required (i e , by learning INLINEFORM3 when we want to continue focusing on the same field) and different when it is time to move on (by learning that INLINEFORM4 ) 
Next, the never look back property implies that once we have moved away from a field we are unlikely to pay attention to it again  For example, once we have rendered all the occupations in the generated description there is no need to return back to the occupation field  In other words, once we have moved on ( INLINEFORM0 ), we want the successive field context vectors INLINEFORM1 to be very different from the previous field vectors INLINEFORM2   One way of ensuring this is to orthogonalize successive field vectors using DISPLAYFORM0
where INLINEFORM0 is the dot product between vectors INLINEFORM1 and INLINEFORM2   The above equation essentially subtracts the component of INLINEFORM3 along INLINEFORM4   INLINEFORM5 is a learned parameter which controls the degree of orthogonalization thereby allowing a soft orthogonalization (i e , the entire component along INLINEFORM6 is not subtracted but only a fraction of it)  The above equation only ensures that INLINEFORM7 is soft-orthogonal to INLINEFORM8   Alternately, we could pass the sequence of context vectors, INLINEFORM9 generated so far through a GRU cell  The state of this GRU cell at each time step would thus be aware of the history of the field vectors till that timestep  Now instead of orthogonalizing INLINEFORM10 to INLINEFORM11 we could orthogonalize INLINEFORM12 to the hidden state of this GRU at time-step INLINEFORM13   In practice, we found this to work better as it accounts for all the field vectors in the history instead of only the previous field vector 
In summary, Equation provides a mechanism for remembering the current field vector when appropriate (thus capturing stay-on behavior) using a remember gate  On the other hand, Equation EQREF10 explicitly ensures that the field vector is very different (soft-orthogonal) from the previous field vectors once it is time to move on (thus capturing never look back behavior)  The value of INLINEFORM0 computed in Equation EQREF10 is then used in Equation   The INLINEFORM1 (macro) thus obtained is then concatenated with INLINEFORM2 (micro) and fed to the decoder (see Fig  FIGREF3 )
Experimental setup
We now describe our experimental setup:
Datasets
We use the WikiBio dataset introduced by lebret2016neural  It consists of INLINEFORM0 biography articles from English Wikipedia  A biography article corresponds to a person (sportsman, politician, historical figure, actor, etc )  Each Wikipedia article has an accompanying infobox which serves as the structured input and the task is to generate the first sentence of the article (which typically is a one-line description of the person)  We used the same train, valid and test sets which were made publicly available by lebret2016neural 
We also introduce two new biography datasets, one in French and one in German  These datasets were created and pre-processed using the same procedure as outlined in lebret2016neural  Specifically, we extracted the infoboxes and the first sentence from the corresponding Wikipedia article  As with the English dataset, we split the French and German datasets randomly into train (80%), test (10%) and valid (10%)  The French and German datasets extracted by us has been made publicly available  The number of examples was 170K and 50K and the vocabulary size was 297K and 143K for French and German respectively  Although in this work we focus only on generating descriptions in one language, we hope that this dataset will also be useful for developing models which jointly learn to generate descriptions from structured data in multiple languages 
Models compared
We compare with the following models:
1  BIBREF0 : This is a conditional language model which uses a feed-forward neural network to predict the next word in the description conditioned on local characteristics (i e , words within a field) and global characteristics (i e , overall structure of the infobox) 
2  BIBREF9 : This model was proposed in the context of the WeatherGov and RoboCup datasets which have a much smaller vocabulary  They use an improved attention model with additional regularizer terms which influence the weights assigned to the fields 
3  Basic Seq2Seq: This is the vanilla encode-attend-decode model BIBREF1   Further, to deal with the large vocabulary ( INLINEFORM0 400K words) we use a copying mechanism as a post-processing step  Specifically, we identify the time steps at which the decoder produces unknown words (denoted by the special symbol UNK)  For each such time step, we look at the attention weights on the input words and replace the UNK word by that input word which has received maximum attention at this timestep  This process is similar to the one described in BIBREF30   Even lebret2016neural have a copying mechanism tightly integrated with their model 
Hyperparameter tuning
We tuned the hyperparameters of all the models using a validation set  As mentioned earlier, we used a bidirectional GRU cell as the function INLINEFORM0 for computing the representation of the fields and the values (see Section SECREF4 )  For all the models, we experimented with GRU state sizes of 128, 256 and 512  The total number of unique words in the corpus is around 400K (this includes the words in the infobox and the descriptions)  Of these, we retained only the top 20K words in our vocabulary (same as BIBREF0 )  We initialized the embeddings of these words with 300 dimensional Glove embeddings BIBREF31   We used Adam BIBREF32 with a learning rate of INLINEFORM1 , INLINEFORM2 and INLINEFORM3   We trained the model for a maximum of 20 epochs and used early stopping with the patience set to 5 epochs 
Results and Discussions
We now discuss the results of our experiments 
Comparison of different models
Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics  We first make a few observations based on the results on the English dataset (Table TABREF15 )  The basic seq2seq model, as well as the model proposed by weather16, perform better than the model proposed by lebret2016neural  Our final model with bifocal attention and gated orthogonalization gives the best performance and does 10% (relative) better than the closest baseline (basic seq2seq) and 21% (relative) better than the current state of the art method BIBREF0   In Table TABREF16 , we show some qualitative examples of the output generated by different models 
Human Evaluations
To make a qualitative assessment of the generated sentences, we conducted a human study on a sample of 500 Infoboxes which were sampled from English dataset  The annotators for this task were undergraduate and graduate students  For each of these infoboxes, we generated summaries using the basic seq2seq model and our final model with bifocal attention and gated orthogonalization  For each description and for each model, we asked three annotators to rank the output of the systems based on i) adequacy (i e  does it capture relevant information from the infobox), (ii) fluency (i e  grammar) and (iii) relative preference (i e , which of the two outputs would be preferred)  Overall the average fluency/adequacy (on a scale of 5) for basic seq2seq model was INLINEFORM0 and INLINEFORM1 for our model respectively 
The results from Table TABREF17 suggest that in general gated orthogonalization model performs better than the basic seq2seq model  Additionally, annotators were asked to verify if the generated summaries look natural (i e, as if they were generated by humans)  In 423 out of 500 cases, the annotators said “Yes” suggesting that gated orthogonalization model indeed produces good descriptions 
Performance on different languages
The results on the French and German datasets are summarized in Tables TABREF20 and TABREF20 respectively  Note that the code of BIBREF0 is not publicly available, hence we could not report numbers for French and German using their model  We observe that our final model gives the best performance - though the bifocal attention model performs poorly as compared to the basic seq2seq model on French  However, the overall performance for French and German are much smaller than those for English  There could be multiple reasons for this  First, the amount of training data in these two languages is smaller than that in English  Specifically, the amount of training data available in French (German) is only INLINEFORM0 ( INLINEFORM1 )% of that available for English  Second, on average the descriptions in French and German are longer than that in English (EN: INLINEFORM2 words, FR: INLINEFORM3 words and DE: INLINEFORM4 words)  Finally, a manual inspection across the three languages suggests that the English descriptions have a more consistent structure than the French descriptions  For example, most English descriptions start with name followed by date of birth but this is not the case in French  However, this is only a qualitative observation and it is hard to quantify this characteristic of the French and German datasets 
Visualizing Attention Weights
If the proposed model indeed works well then we should see attention weights that are consistent with the stay on and never look back behavior  To verify this, we plotted the attention weights in cases where the model with gated orthogonalization does better than the model with only bifocal attention  Figure FIGREF21 shows the attention weights corresponding to infobox in Figure FIGREF25   Notice that the model without gated orthogonalization has attention on both name field and article title while rendering the name  The model with gated orthogonalization, on the other hand, stays on the name field for as long as it is required but then moves and never returns to it (as expected) 
Due to lack of space, we do not show similar plots for French and German but we would like to mention that, in general, the differences between the attention weights learned by the model with and without gated orthogonalization were more pronounced for the French/German dataset than the English dataset  This is in agreement with the results reported in Table TABREF20 and TABREF20 where the improvements given by gated orthogonalization are more for French/German than for English 
Out of domain results
What if the model sees a different INLINEFORM0 of person at test time? For example, what if the training data does not contain any sportspersons but at test time we encounter the infobox of a sportsperson  This is the same as seeing out-of-domain data at test time  Such a situation is quite expected in the products domain where new products with new features (fields) get frequently added to the catalog  We were interested in three questions here  First, we wanted to see if testing the model on out-of-domain data indeed leads to a drop in the performance  For this, we compared the performance of our best model in two scenarios (i) trained on data from all domains (including the target domain) and tested on the target domain (sports, arts) and (ii) trained on data from all domains except the target domain and tested on the target domain  Comparing rows 1 and 2 of Table TABREF32 we observed a significant drop in the performance  Note that the numbers for sports domain in row 1 are much better than the Arts domain because roughly 40% of the WikiBio training data contains sportspersons 
Next, we wanted to see if we can use a small amount of data from the target domain to fine tune a model trained on the out of domain data  We observe that even with very small amounts of target domain data the performance starts improving significantly (see rows 3 and 4 of Table TABREF32 )  Note that if we train a model from scratch with only limited data from the target domain instead of fine-tuning a model trained on a different source domain then the performance is very poor  In particular, training a model from scratch with 10K training instances we get a BLEU score of INLINEFORM0 and INLINEFORM1 for arts and sports respectively  Finally, even though the actual words used for describing a sportsperson (footballer, cricketer, etc ) would be very different from the words used to describe an artist (actor, musician, etc ) they might share many fields (for example, date of birth, occupation, etc )  As seen in Figure FIGREF28 (attention weights corresponding to the infobox in Figure FIGREF27 ), the model predicts the attention weights correctly for common fields (such as occupation) but it is unable to use the right vocabulary to describe the occupation (since it has not seen such words frequently in the training data)  However, once we fine tune the model with limited data from the target domain we see that it picks up the new vocabulary and produces a correct description of the occupation 
Conclusion
We present a model for generating natural language descriptions from structured data  To address specific characteristics of the problem we propose neural components for fused bifocal attention and gated orthogonalization to address stay on and never look back behavior while decoding  Our final model outperforms an existing state of the art model on a large scale WikiBio dataset by 21%  We also introduce datasets for French and German and demonstrate that our model gives state of the art results on these datasets  Finally, we perform experiments with an out-of-domain model and show that if such a model is fine-tuned with small amounts of in domain data then it can give an improved performance on the target domain 
Given the multilingual nature of the new datasets, as future work, we would like to build models which can jointly learn to generate natural language descriptions from structured data in multiple languages  One idea is to replace the concepts in the input infobox by Wikidata concept ids which are language agnostic  A large amount of input vocabulary could thus be shared across languages thereby facilitating joint learning 
Acknowledgements
We thank Google for supporting Preksha Nema through their Google India Ph D  Fellowship program  We also thank Microsoft Research India for supporting Shreyas Shetty through their generous travel grant for attending the conference 

ground_truth: English datasets including CoNLL2003 and OntoNotes5.0, our proposed method outperforms BERT-MRCBIBREF38 by +0.29 and +0.96 respectively, Chinese datasets, achieving F1 improvements by +0.97 and +2.36 on MSRA and OntoNotes4.0, respectively

For English datasets including CoNLL2003 and OntoNotes5.0, our proposed method outperforms BERT-MRCBIBREF38 by +0.29 and +0.96 respectively., huge performance boosts on Chinese datasets, achieving F1 improvements by +0.97 and +2.36 on MSRA and OntoNotes4.0, respectively

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. Do they use pretrained embeddings?
Context 4:  To gain a better understanding of the significance of those differences, we use the Kolmogorov-Smirnov test with the null hypothesis that both distributions are equal 
Results
The sample collected consisted on 1 785 855 tweets published by 848 196 different users  Within our sample, we identified 1327 tweets that went viral (retweeted more than 1000 times by the 8th of November 2016) produced by 643 users  Such small subset of viral tweets were retweeted on 290 841 occasions in the observed time-window 
The 1327 `viral' tweets were manually annotated as containing fake news or not  The annotation was carried out by a single person in order to obtain a consistent annotation throughout the dataset  Out of those 1327 tweets, we identified 136 as potentially containing fake news (according to the categories previously described), and the rest were classified as `non containing fake news'  Note that the categorization is far from being perfect given the ambiguity of fake news themselves and human judgement involved in the process of categorization  Because of this, we do not claim that this dataset can be considered a ground truth 
The following results detail characteristics of these tweets along the previously mentioned dimensions  Table TABREF23 reports the actual differences (together with their associated p-values) of the distributions of viral tweets containing fake news and viral tweets not containing them for every variable considered 
Exposure
Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently  As such, Twitter users were exposed to fake news related to the election for a shorter period of time 
However, in terms of retweets, Figure FIGREF25 shows no apparent difference between containing fake news or not containing them  That is confirmed by the Kolmogorov-Smirnoff test, which does not discard the hypothesis that the associated distributions are equal 
In relation to the number of favourites, users that generated at least a viral tweet containing fake news appear to have, on average, less favourites than users that do not generate them  Figure FIGREF26 shows the distribution of favourites  Despite the apparent visual differences, the difference are not statistically significant 
Finally, the number of hashtags used in viral fake news appears to be larger than those in other viral tweets  Figure FIGREF27 shows the density distribution of the number of hashtags used  However, once again, we were not able to find any statistical difference between the average number of hashtags in a viral tweet and the average number of hashtags in viral fake news 
Characterization
We found that 82 users within our sample were spreading fake news (i e  they produced at least one tweet which was labelled as fake news)  Out of those, 34 had verified accounts, and the rest were unverified  From the 48 unverified accounts, 6 have been suspended by Twitter at the date of writing, 3 tried to imitate legitimate accounts of others, and 4 accounts have been already deleted  Figure FIGREF28 shows the proportion of verified accounts to unverified accounts for viral tweets (containing fake news vs  not containing fake news)  From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts 
Turning to friends, accounts distributing fake news appear to have, on average, the same number of friends than those distributing tweets with no fake news  However, the density distribution of friends from the accounts (Figure FIGREF29 ) shows that there is indeed a statistically significant difference in their distributions 
If we take into consideration the number of followers, accounts generating viral tweets with fake news do have a very different distribution on this dimension, compared to those accounts generating viral tweets with no fake news (see Figure FIGREF30 )  In fact, such differences are statistically significant 
A useful representation for friends and followers is the ratio between friends/followers  Figures FIGREF31 and FIGREF32 show this representation  Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers  The distribution of those accounts not generating fake news is more evenly distributed 
With respect to the number of mentions, Figure FIGREF33 shows that viral tweets labelled as containing fake news appear to use mentions to other users less frequently than viral tweets not containing fake news  In other words, tweets containing fake news mostly contain 1 mention, whereas other tweets tend to have two)  Such differences are statistically significant 
The analysis (Figure FIGREF34 ) of the presence of media in the tweets in our dataset shows that tweets labelled as not containing fake news appear to present more media elements than those labelled as fake news  However, the difference is not statistically significant 
On the other hand, Figure FIGREF35 shows that viral tweets containing fake news appear to include more URLs to other sites than viral tweets that do not contain fake news  In fact, the difference between the two distributions is statistically significant (assuming INLINEFORM0 ) 
Polarization
Finally, manual inspection of the text field of those viral tweets labelled as containing fake news shows that 117 of such tweets expressed support for Donald Trump, while only 8 supported Hillary Clinton  The remaining tweets contained fake news related to other topics, not expressing support for any of the candidates 
Discussion
As a summary, and constrained by our existing dataset, we made the following observations regarding differences between viral tweets labelled as containing fake news and viral tweets labelled as not containing them:
These findings (related to our initial hypothesis in Table TABREF44 ) clearly suggest that there are specific pieces of meta-data about tweets that may allow the identification of fake news  One such parameter is the time of exposure  Viral tweets containing fake news are shorter-lived than those containing other type of content  This notion seems to resonate with our findings showing that a number of accounts spreading fake news have already been deleted or suspended by Twitter by the time of writing  If one considers that researchers using different data have found similar results BIBREF9 , it appears that the lifetime of accounts, together with the age of the questioned viral content could be useful to identify fake news  In the light of this finding, accounts newly created should probably put under higher scrutiny than older ones  This in fact, would be a nice a-priori bias for a Bayesian classifier 
Accounts spreading fake news appear to have a larger proportion of friends/followers (i e  they have, on average, the same number of friends but a smaller number of followers) than those spreading viral content only  Together with the fact that, on average, tweets containing fake news have more URLs than those spreading viral content, it is possible to hypothesize that, both, the ratio of friends/followers of the account producing a viral tweet and number of URLs contained in such a tweet could be useful to single-out fake news in Twitter  Not only that, but our finding related to the number of URLs is in line with intuitions behind the incentives to create fake news commonly found in the literature BIBREF9 (in particular that of obtaining revenue through click-through advertising) 
Finally, it is interesting to notice that the content of viral fake news was highly polarized  This finding is also in line with those of Alcott et al  BIBREF9   This feature suggests that textual sentiment analysis of the content of tweets (as most researchers do), together with the above mentioned parameters from meta-data, may prove useful for identifying fake news 
Conclusions
With the election of Donald Trump as President of the United States, the concept of fake news has become a broadly-known phenomenon that is getting tremendous attention from governments and media companies  We have presented a preliminary study on the meta-data of a publicly available dataset of tweets that became viral during the day of the 2016 US presidential election  Our aim is to advance the understanding of which features might be characteristic of viral tweets containing fake news in comparison with viral tweets without fake news 
We believe that the only way to automatically identify those deceitful tweets (i e  containing fake news) is by actually understanding and modelling them  Only then, the automation of the processes of tagging and blocking these tweets can be successfully performed  In the same way that spam was fought, we anticipate fake news will suffer a similar evolution, with social platforms implementing tools to deal with them  With most works so far focusing on the actual content of the tweets, ours is a novel attempt from a different, but also complementary, angle 
Within the used dataset, we found there are differences around exposure, characteristics of accounts spreading fake news and the tone of the content  Those findings suggest that it is indeed possible to model and automatically detect fake news  We plan to replicate and validate our experiments in an extended sample of tweets (until 4 months after the US election), and tests the predictive power of the features we found relevant within our sample 
Author Disclosure Statement
No competing financial interest exist 

ground_truth: Yes

Yes