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

Please answer the questions based on the text provided below. How do they combine audio and text sequences in their RNN?
Context 1: Introduction
Recently, deep learning algorithms have successfully addressed problems in various fields, such as image classification, machine translation, speech recognition, text-to-speech generation and other machine learning related areas BIBREF0 , BIBREF1 , BIBREF2   Similarly, substantial improvements in performance have been obtained when deep learning algorithms have been applied to statistical speech processing BIBREF3   These fundamental improvements have led researchers to investigate additional topics related to human nature, which have long been objects of study  One such topic involves understanding human emotions and reflecting it through machine intelligence, such as emotional dialogue models BIBREF4 , BIBREF5  
In developing emotionally aware intelligence, the very first step is building robust emotion classifiers that display good performance regardless of the application; this outcome is considered to be one of the fundamental research goals in affective computing BIBREF6   In particular, the speech emotion recognition task is one of the most important problems in the field of paralinguistics  This field has recently broadened its applications, as it is a crucial factor in optimal human-computer interactions, including dialog systems  The goal of speech emotion recognition is to predict the emotional content of speech and to classify speech according to one of several labels (i e , happy, sad, neutral, and angry)  Various types of deep learning methods have been applied to increase the performance of emotion classifiers; however, this task is still considered to be challenging for several reasons  First, insufficient data for training complex neural network-based models are available, due to the costs associated with human involvement  Second, the characteristics of emotions must be learned from low-level speech signals  Feature-based models display limited skills when applied to this problem 
To overcome these limitations, we propose a model that uses high-level text transcription, as well as low-level audio signals, to utilize the information contained within low-resource datasets to a greater degree  Given recent improvements in automatic speech recognition (ASR) technology BIBREF7 , BIBREF2 , BIBREF8 , BIBREF9 , speech transcription can be carried out using audio signals with considerable skill  The emotional content of speech is clearly indicated by the emotion words contained in a sentence BIBREF10 , such as “lovely” and “awesome,” which carry strong emotions compared to generic (non-emotion) words, such as “person” and “day ” Thus, we hypothesize that the speech emotion recognition model will be benefit from the incorporation of high-level textual input 
In this paper, we propose a novel deep dual recurrent encoder model that simultaneously utilizes audio and text data in recognizing emotions from speech  Extensive experiments are conducted to investigate the efficacy and properties of the proposed model  Our proposed model outperforms previous state-of-the-art methods by 68 8% to 71 8% when applied to the IEMOCAP dataset, which is one of the most well-studied datasets  Based on an error analysis of the models, we show that our proposed model accurately identifies emotion classes  Moreover, the neutral class misclassification bias frequently exhibited by previous models, which focus on audio features, is less pronounced in our model 
Related work
Classical machine learning algorithms, such as hidden Markov models (HMMs), support vector machines (SVMs), and decision tree-based methods, have been employed in speech emotion recognition problems BIBREF11 , BIBREF12 , BIBREF13   Recently, researchers have proposed various neural network-based architectures to improve the performance of speech emotion recognition  An initial study utilized deep neural networks (DNNs) to extract high-level features from raw audio data and demonstrated its effectiveness in speech emotion recognition BIBREF14   With the advancement of deep learning methods, more complex neural-based architectures have been proposed  Convolutional neural network (CNN)-based models have been trained on information derived from raw audio signals using spectrograms or audio features such as Mel-frequency cepstral coefficients (MFCCs) and low-level descriptors (LLDs) BIBREF15 , BIBREF16 , BIBREF17   These neural network-based models are combined to produce higher-complexity models BIBREF18 , BIBREF19 , and these models achieved the best-recorded performance when applied to the IEMOCAP dataset 
Another line of research has focused on adopting variant machine learning techniques combined with neural network-based models  One researcher utilized the multiobject learning approach and used gender and naturalness as auxiliary tasks so that the neural network-based model learned more features from a given dataset BIBREF20   Another researcher investigated transfer learning methods, leveraging external data from related domains BIBREF21  
As emotional dialogue is composed of sound and spoken content, researchers have also investigated the combination of acoustic features and language information, built belief network-based methods of identifying emotional key phrases, and assessed the emotional salience of verbal cues from both phoneme sequences and words BIBREF22 , BIBREF23   However, none of these studies have utilized information from speech signals and text sequences simultaneously in an end-to-end learning neural network-based model to classify emotions 
Model
This section describes the methodologies that are applied to the speech emotion recognition task  We start by introducing the recurrent encoder model for the audio and text modalities individually  We then propose a multimodal approach that encodes both audio and textual information simultaneously via a dual recurrent encoder 
Audio Recurrent Encoder (ARE)
Motivated by the architecture used in BIBREF24 , BIBREF25 , we build an audio recurrent encoder (ARE) to predict the class of a given audio signal  Once MFCC features have been extracted from an audio signal, a subset of the sequential features is fed into the RNN (i e , gated recurrent units (GRUs)), which leads to the formation of the network's internal hidden state INLINEFORM0 to model the time series patterns  This internal hidden state is updated at each time step with the input data INLINEFORM1 and the hidden state of the previous time step INLINEFORM2 as follows: DISPLAYFORM0
where INLINEFORM0 is the RNN function with weight parameter INLINEFORM1 , INLINEFORM2 represents the hidden state at t- INLINEFORM3 time step, and INLINEFORM4 represents the t- INLINEFORM5 MFCC features in INLINEFORM6   After encoding the audio signal INLINEFORM7 with the RNN, the last hidden state of the RNN, INLINEFORM8 , is considered to be the representative vector that contains all of the sequential audio data  This vector is then concatenated with another prosodic feature vector, INLINEFORM9 , to generate a more informative vector representation of the signal, INLINEFORM10   The MFCC and the prosodic features are extracted from the audio signal using the openSMILE toolkit BIBREF26 , INLINEFORM11 , respectively  Finally, the emotion class is predicted by applying the softmax function to the vector INLINEFORM12   For a given audio sample INLINEFORM13 , we assume that INLINEFORM14 is the true label vector, which contains all zeros but contains a one at the correct class, and INLINEFORM15 is the predicted probability distribution from the softmax layer  The training objective then takes the following form: DISPLAYFORM0
where INLINEFORM0 is the calculated representative vector of the audio signal with dimensionality INLINEFORM1   The INLINEFORM2 and the bias INLINEFORM3 are learned model parameters  C is the total number of classes, and N is the total number of samples used in training  The upper part of Figure shows the architecture of the ARE model 
Text Recurrent Encoder (TRE)
We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7   We attempt to use the processed textual information as another modality in predicting the emotion class of a given signal  To use textual information, a speech transcript is tokenized and indexed into a sequence of tokens using the Natural Language Toolkit (NLTK) BIBREF27   Each token is then passed through a word-embedding layer that converts a word index to a corresponding 300-dimensional vector that contains additional contextual meaning between words  The sequence of embedded tokens is fed into a text recurrent encoder (TRE) in such a way that the audio MFCC features are encoded using the ARE represented by equation EQREF2   In this case, INLINEFORM0 is the t- INLINEFORM1 embedded token from the text input  Finally, the emotion class is predicted from the last hidden state of the text-RNN using the softmax function 
We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 is last hidden state of the text-RNN, INLINEFORM1 , and the INLINEFORM2 and bias INLINEFORM3 are learned model parameters  The lower part of Figure indicates the architecture of the TRE model 
Multimodal Dual Recurrent Encoder (MDRE)
We present a novel architecture called the multimodal dual recurrent encoder (MDRE) to overcome the limitations of existing approaches  In this study, we consider multiple modalities, such as MFCC features, prosodic features and transcripts, which contain sequential audio information, statistical audio information and textual information, respectively  These types of data are the same as those used in the ARE and TRE cases  The MDRE model employs two RNNs to encode data from the audio signal and textual inputs independently  The audio-RNN encodes MFCC features from the audio signal using equation EQREF2   The last hidden state of the audio-RNN is concatenated with the prosodic features to form the final vector representation INLINEFORM0 , and this vector is then passed through a fully connected neural network layer to form the audio encoding vector A  On the other hand, the text-RNN encodes the word sequence of the transcript using equation EQREF2   The final hidden states of the text-RNN are also passed through another fully connected neural network layer to form a textual encoding vector T  Finally, the emotion class is predicted by applying the softmax function to the concatenation of the vectors A and T  We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 is the feed-forward neural network with weight parameter INLINEFORM1 , and INLINEFORM2 , INLINEFORM3 are final encoding vectors from the audio-RNN and text-RNN, respectively  INLINEFORM4 and the bias INLINEFORM5 are learned model parameters 
Multimodal Dual Recurrent Encoder with Attention (MDREA)
Inspired by the concept of the attention mechanism used in neural machine translation BIBREF28 , we propose a novel multimodal attention method to focus on the specific parts of a transcript that contain strong emotional information, conditioning on the audio information  Figure shows the architecture of the MDREA model  First, the audio data and text data are encoded with the audio-RNN and text-RNN using equation EQREF2   We then consider the final audio encoding vector INLINEFORM0 as a context vector  As seen in equation EQREF9 , during each time step t, the dot product between the context vector e and the hidden state of the text-RNN at each t-th sequence INLINEFORM1 is evaluated to calculate a similarity score INLINEFORM2   Using this score INLINEFORM3 as a weight parameter, the weighted sum of the sequences of the hidden state of the text-RNN, INLINEFORM4 , is calculated to generate an attention-application vector Z  This attention-application vector is concatenated with the final encoding vector of the audio-RNN INLINEFORM5 (equation EQREF7 ), which will be passed through the softmax function to predict the emotion class  We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 and the bias INLINEFORM1 are learned model parameters 
Dataset
We evaluate our model using the Interactive Emotional Dyadic Motion Capture (IEMOCAP) BIBREF18 dataset  This dataset was collected following theatrical theory in order to simulate natural dyadic interactions between actors  We use categorical evaluations with majority agreement  We use only four emotional categories happy, sad, angry, and neutral to compare the performance of our model with other research using the same categories  The IEMOCAP dataset includes five sessions, and each session contains utterances from two speakers (one male and one female)  This data collection process resulted in 10 unique speakers  For consistent comparison with previous work, we merge the excitement dataset with the happiness dataset  The final dataset contains a total of 5531 utterances (1636 happy, 1084 sad, 1103 angry, 1708 neutral) 
Feature extraction
To extract speech information from audio signals, we use MFCC values, which are widely used in analyzing audio signals  The MFCC feature set contains a total of 39 features, which include 12 MFCC parameters (1-12) from the 26 Mel-frequency bands and log-energy parameters, 13 delta and 13 acceleration coefficients The frame size is set to 25 ms at a rate of 10 ms with the Hamming function  According to the length of each wave file, the sequential step of the MFCC features is varied  To extract additional information from the data, we also use prosodic features, which show effectiveness in affective computing  The prosodic features are composed of 35 features, which include the F0 frequency, the voicing probability, and the loudness contours  All of these MFCC and prosodic features are extracted from the data using the OpenSMILE toolkit BIBREF26  
Implementation details
Among the variants of the RNN function, we use GRUs as they yield comparable performance to that of the LSTM and include a smaller number of weight parameters BIBREF29   We use a max encoder step of 750 for the audio input, based on the implementation choices presented in BIBREF30 and 128 for the text input because it covers the maximum length of the transcripts  The vocabulary size of the dataset is 3,747, including the “_UNK_" token, which represents unknown words, and the “_PAD_" token, which is used to indicate padding information added while preparing mini-batch data  The number of hidden units and the number of layers in the RNN for each model (ARE, TRE, MDRE and MDREA) are selected based on extensive hyperparameter search experiments  The weights of the hidden units are initialized using orthogonal weights BIBREF31 ], and the text embedding layer is initialized from pretrained word-embedding vectors BIBREF32  
In preparing the textual dataset, we first use the released transcripts of the IEMOCAP dataset for simplicity  To investigate the practical performance, we then process all of the IEMOCAP audio data using an ASR system (the Google Cloud Speech API) and retrieve the transcripts  The performance of the Google ASR system is reflected by its word error rate (WER) of 5 53% 
Performance evaluation
As the dataset is not explicitly split beforehand into training, development, and testing sets, we perform 5-fold cross validation to determine the overall performance of the model  The data in each fold are split into training, development, and testing datasets (8:0 5:1 5, respectively)  After training the model, we measure the weighted average precision (WAP) over the 5-fold dataset  We train and evaluate the model 10 times per fold, and the model performance is assessed in terms of the mean score and standard deviation 
We examine the WAP values, which are shown in Table 1  First, our ARE model shows the baseline performance because we use minimal audio features, such as the MFCC and prosodic features with simple architectures  On the other hand, the TRE model shows higher performance gain compared to the ARE  From this result, we note that textual data are informative in emotion prediction tasks, and the recurrent encoder model is effective in understanding these types of sequential data  Second, the newly proposed model, MDRE, shows a substantial performance gain  It thus achieves the state-of-the-art performance with a WAP value of 0 718  This result shows that multimodal information is a key factor in affective computing  Lastly, the attention model, MDREA, also outperforms the best existing research results (WAP 0 690 to 0 688) BIBREF19   However, the MDREA model does not match the performance of the MDRE model, even though it utilizes a more complex architecture  We believe that this result arises because insufficient data are available to properly determine the complex model parameters in the MDREA model  Moreover, we presume that this model will show better performance when the audio signals are aligned with the textual sequence while applying the attention mechanism  We leave the implementation of this point as a future research direction 
To investigate the practical performance of the proposed models, we conduct further experiments with the ASR-processed transcript data (see “-ASR” models in Table )  The label accuracy of the processed transcripts is 5 53% WER  The TRE-ASR, MDRE-ASR and MDREA-ASR models reflect degraded performance compared to that of the TRE, MDRE and MDREA models  However, the performance of these models is still competitive; in particular, the MDRE-ASR model outperforms the previous best-performing model, 3CNN-LSTM10H (WAP 0 691 to 0 688) 
Error analysis
We analyze the predictions of the ARE, TRE, and MDRE models  Figure shows the confusion matrix of each model  The ARE model (Fig  ) incorrectly classifies most instances of happy as neutral (43 51%); thus, it shows reduced accuracy (35 15%) in predicting the the happy class  Overall, most of the emotion classes are frequently confused with the neutral class  This observation is in line with the findings of BIBREF30 , who noted that the neutral class is located in the center of the activation-valence space, complicating its discrimination from the other classes 
Interestingly, the TRE model (Fig  ) shows greater prediction gains in predicting the happy class when compared to the ARE model (35 15% to 75 73%)  This result seems plausible because the model can benefit from the differences among the distributions of words in happy and neutral expressions, which gives more emotional information to the model than that of the audio signal data  On the other hand, it is striking that the TRE model incorrectly predicts instances of the sad class as the happy class 16 20% of the time, even though these emotional states are opposites of one another 
The MDRE model (Fig  ) compensates for the weaknesses of the previous two models (ARE and TRE) and benefits from their strengths to a surprising degree  The values arranged along the diagonal axis show that all of the accuracies of the correctly predicted class have increased  Furthermore, the occurrence of the incorrect “sad-to-happy" cases in the TRE model is reduced from 16 20% to 9 15% 
Conclusions
In this paper, we propose a novel multimodal dual recurrent encoder model that simultaneously utilizes text data, as well as audio signals, to permit the better understanding of speech data  Our model encodes the information from audio and text sequences using dual RNNs and then combines the information from these sources using a feed-forward neural model to predict the emotion class  Extensive experiments show that our proposed model outperforms other state-of-the-art methods in classifying the four emotion categories, and accuracies ranging from 68 8% to 71 8% are obtained when the model is applied to the IEMOCAP dataset  In particular, it resolves the issue in which predictions frequently incorrectly yield the neutral class, as occurs in previous models that focus on audio features 
In the future work, we aim to extend the modalities to audio, text and video inputs  Furthermore, we plan to investigate the application of the attention mechanism to data derived from multiple modalities  This approach seems likely to uncover enhanced learning schemes that will increase performance in both speech emotion recognition and other multimodal classification tasks 
Acknowledgments
K  Jung is with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul, Korea  This work was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (No 10073144) 
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 
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
Text simplification aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning, which can help children, non-native speakers, and people with cognitive disabilities, to understand text better  One of the methods of automatic text simplification can be generally divided into three categories: lexical simplification (LS) BIBREF0 , BIBREF1 , rule-based BIBREF2 , and machine translation (MT) BIBREF3 , BIBREF4   LS is mainly used to simplify text by substituting infrequent and difficult words with frequent and easier words  However, there are several challenges for the LS approach: a great number of transformation rules are required for reasonable coverage and should be applied based on the specific context; third, the syntax and semantic meaning of the sentence is hard to retain  Rule-based approaches use hand-crafted rules for lexical and syntactic simplification, for example, substituting difficult words in a predefined vocabulary  However, such approaches need a lot of human-involvement to manually define these rules, and it is impossible to give all possible simplification rules  MT-based approach has attracted great attention in the last several years, which addresses text simplification as a monolingual machine translation problem translating from 'ordinary' and 'simplified' sentences 
In recent years, neural Machine Translation (NMT) is a newly-proposed deep learning approach and achieves very impressive results BIBREF5 , BIBREF6 , BIBREF7   Unlike the traditional phrased-based machine translation system which operates on small components separately, NMT system is being trained end-to-end, without the need to have external decoders, language models or phrase tables  Therefore, the existing architectures in NMT are used for text simplification BIBREF8 , BIBREF4   However, most recent work using NMT is limited to the training data that are scarce and expensive to build  Language models trained on simplified corpora have played a central role in statistical text simplification BIBREF9 , BIBREF10   One main reason is the amount of available simplified corpora typically far exceeds the amount of parallel data  The performance of models can be typically improved when trained on more data  Therefore, we expect simplified corpora to be especially helpful for NMT models 
In contrast to previous work, which uses the existing NMT models, we explore strategy to include simplified training corpora in the training process without changing the neural network architecture  We first propose to pair simplified training sentences with synthetic ordinary sentences during training, and treat this synthetic data as additional training data  We obtain synthetic ordinary sentences through back-translation, i e  an automatic translation of the simplified sentence into the ordinary sentence BIBREF11   Then, we mix the synthetic data into the original (simplified-ordinary) data to train NMT model  Experimental results on two publicly available datasets show that we can improve the text simplification quality of NMT models by mixing simplified sentences into the training set over NMT model only using the original training data 
Related Work
Automatic TS is a complicated natural language processing (NLP) task, which consists of lexical and syntactic simplification levels BIBREF12   It has attracted much attention recently as it could make texts more accessible to wider audiences, and used as a pre-processing step, improve performances of various NLP tasks and systems BIBREF13 , BIBREF14 , BIBREF15   Usually, hand-crafted, supervised, and unsupervised methods based on resources like English Wikipedia and Simple English Wikipedia (EW-SEW) BIBREF10 are utilized for extracting simplification rules  It is very easy to mix up the automatic TS task and the automatic summarization task BIBREF3 , BIBREF16 , BIBREF6   TS is different from text summarization as the focus of text summarization is to reduce the length and redundant content 
At the lexical level, lexical simplification systems often substitute difficult words using more common words, which only require a large corpus of regular text to obtain word embeddings to get words similar to the complex word BIBREF1 , BIBREF9   Biran et al  BIBREF0 adopted an unsupervised method for learning pairs of complex and simpler synonyms from a corpus consisting of Wikipedia and Simple Wikipedia  At the sentence level, a sentence simplification model was proposed by tree transformation based on statistical machine translation (SMT) BIBREF3   Woodsend and Lapata BIBREF17 presented a data-driven model based on a quasi-synchronous grammar, a formalism that can naturally capture structural mismatches and complex rewrite operations  Wubben et al  BIBREF18 proposed a phrase-based machine translation (PBMT) model that is trained on ordinary-simplified sentence pairs  Xu et al  BIBREF19 proposed a syntax-based machine translation model using simplification-specific objective functions and features to encourage simpler output 
Compared with SMT, neural machine translation (NMT) has shown to produce state-of-the-art results BIBREF5 , BIBREF7   The central approach of NMT is an encoder-decoder architecture implemented by recurrent neural networks, which can represent the input sequence as a vector, and then decode that vector into an output sequence  Therefore, NMT models were used for text simplification task, and achieved good results BIBREF8 , BIBREF4 , BIBREF20   The main limitation of the aforementioned NMT models for text simplification depended on the parallel ordinary-simplified sentence pairs  Because ordinary-simplified sentence pairs are expensive and time-consuming to build, the available largest data is EW-SEW that only have 296,402 sentence pairs  The dataset is insufficiency for NMT model if we want to NMT model can obtain the best parameters  Considering simplified data plays an important role in boosting fluency for phrase-based text simplification, and we investigate the use of simplified data for text simplification  We are the first to show that we can effectively adapt neural translation models for text simplifiation with simplified corpora 
Simplified Corpora
We collected a simplified dataset from Simple English Wikipedia that are freely available, which has been previously used for many text simplification methods BIBREF0 , BIBREF10 , BIBREF3   The simple English Wikipedia is pretty easy to understand than normal English Wikipedia  We downloaded all articles from Simple English Wikipedia  For these articles, we removed stubs, navigation pages and any article that consisted of a single sentence  We then split them into sentences with the Stanford CorNLP BIBREF21 , and deleted these sentences whose number of words are smaller than 10 or large than 40  After removing repeated sentences, we chose 600K sentences as the simplified data with 11 6M words, and the size of vocabulary is 82K 
Text Simplification using Neural Machine Translation
Our work is built on attention-based NMT BIBREF5 as an encoder-decoder network with recurrent neural networks (RNN), which simultaneously conducts dynamic alignment and generation of the target simplified sentence 
The encoder uses a bidirectional RNN that consists of forward and backward RNN  Given a source sentence INLINEFORM0 , the forward RNN and backward RNN calculate forward hidden states INLINEFORM1 and backward hidden states INLINEFORM2 , respectively  The annotation vector INLINEFORM3 is obtained by concatenating INLINEFORM4 and INLINEFORM5  
The decoder is a RNN that predicts a target simplificated sentence with Gated Recurrent Unit (GRU) BIBREF22   Given the previously generated target (simplified) sentence INLINEFORM0 , the probability of next target word INLINEFORM1 is DISPLAYFORM0
where INLINEFORM0 is a non-linear function, INLINEFORM1 is the embedding of INLINEFORM2 , and INLINEFORM3 is a decoding state for time step INLINEFORM4  
State INLINEFORM0 is calculated by DISPLAYFORM0
where INLINEFORM0 is the activation function GRU 
The INLINEFORM0 is the context vector computed as a weighted annotation INLINEFORM1 , computed by DISPLAYFORM0
where the weight INLINEFORM0 is computed by DISPLAYFORM0 DISPLAYFORM1
where INLINEFORM0 , INLINEFORM1 and INLINEFORM2 are weight matrices  The training objective is to maximize the likelihood of the training data  Beam search is employed for decoding 
Synthetic Simplified Sentences
We train an auxiliary system using NMT model from the simplified sentence to the ordinary sentence, which is first trained on the available parallel data  For leveraging simplified sentences to improve the quality of NMT model for text simplification, we propose to adapt the back-translation approach proposed by Sennrich et al  BIBREF11 to our scenario  More concretely, Given one sentence in simplified sentences, we use the simplified-ordinary system in translate mode with greedy decoding to translate it to the ordinary sentences, which is denoted as back-translation  This way, we obtain a synthetic parallel simplified-ordinary sentences  Both the synthetic sentences and the available parallel data are used as training data for the original NMT system 
Evaluation
We evaluate the performance of text simplification using neural machine translation on available parallel sentences and additional simplified sentences 
Dataset  We use two simplification datasets (WikiSmall and WikiLarge)  WikiSmall consists of ordinary and simplified sentences from the ordinary and simple English Wikipedias, which has been used as benchmark for evaluating text simplification BIBREF17 , BIBREF18 , BIBREF8   The training set has 89,042 sentence pairs, and the test set has 100 pairs  WikiLarge is also from Wikipedia corpus whose training set contains 296,402 sentence pairs BIBREF19 , BIBREF20   WikiLarge includes 8 (reference) simplifications for 2,359 sentences split into 2,000 for development and 359 for testing 
Metrics  Three metrics in text simplification are chosen in this paper  BLEU BIBREF5 is one traditional machine translation metric to assess the degree to which translated simplifications differed from reference simplifications  FKGL measures the readability of the output BIBREF23   A small FKGL represents simpler output  SARI is a recent text-simplification metric by comparing the output against the source and reference simplifications BIBREF20  
We evaluate the output of all systems using human evaluation  The metric is denoted as Simplicity BIBREF8   The three non-native fluent English speakers are shown reference sentences and output sentences  They are asked whether the output sentence is much simpler (+2), somewhat simpler (+1), equally (0), somewhat more difficult (-1), and much more difficult (-2) than the reference sentence 
Methods  We use OpenNMT BIBREF24 as the implementation of the NMT system for all experiments BIBREF5   We generally follow the default settings and training procedure described by Klein et al (2017)  We replace out-of-vocabulary words with a special UNK symbol  At prediction time, we replace UNK words with the highest probability score from the attention layer  OpenNMT system used on parallel data is the baseline system  To obtain a synthetic parallel training set, we back-translate a random sample of 100K sentences from the collected simplified corpora  OpenNMT used on parallel data and synthetic data is our model  The benchmarks are run on a Intel(R) Core(TM) i7-5930K CPU@3 50GHz, 32GB Mem, trained on 1 GPU GeForce GTX 1080 (Pascal) with CUDA v  8 0 
We choose three statistical text simplification systems  PBMT-R is a phrase-based method with a reranking post-processing step BIBREF18   Hybrid performs sentence splitting and deletion operations based on discourse representation structures, and then simplifies sentences with PBMT-R BIBREF25   SBMT-SARI BIBREF19 is syntax-based translation model using PPDB paraphrase database BIBREF26 and modifies tuning function (using SARI)  We choose two neural text simplification systems  NMT is a basic attention-based encoder-decoder model which uses OpenNMT framework to train with two LSTM layers, hidden states of size 500 and 500 hidden units, SGD optimizer, and a dropout rate of 0 3 BIBREF8   Dress is an encoder-decoder model coupled with a deep reinforcement learning framework, and the parameters are chosen according to the original paper BIBREF20   For the experiments with synthetic parallel data, we back-translate a random sample of 60 000 sentences from the collected simplified sentences into ordinary sentences  Our model is trained on synthetic data and the available parallel data, denoted as NMT+synthetic 
Results  Table 1 shows the results of all models on WikiLarge dataset  We can see that our method (NMT+synthetic) can obtain higher BLEU, lower FKGL and high SARI compared with other models, except Dress on FKGL and SBMT-SARI on SARI  It verified that including synthetic data during training is very effective, and yields an improvement over our baseline NMF by 2 11 BLEU, 1 7 FKGL and 1 07 SARI  We also substantially outperform Dress, who previously reported SOTA result  The results of our human evaluation using Simplicity are also presented in Table 1  NMT on synthetic data is significantly better than PBMT-R, Dress, and SBMT-SARI on Simplicity  It indicates that our method with simplified data is effective at creating simpler output 
Results on WikiSmall dataset are shown in Table 2  We see substantial improvements (6 37 BLEU) than NMT from adding simplified training data with synthetic ordinary sentences  Compared with statistical machine translation models (PBMT-R, Hybrid, SBMT-SARI), our method (NMT+synthetic) still have better results, but slightly worse FKGL and SARI  Similar to the results in WikiLarge, the results of our human evaluation using Simplicity outperforms the other models  In conclusion, Our method produces better results comparing with the baselines, which demonstrates the effectiveness of adding simplified training data 
Conclusion
In this paper, we propose one simple method to use simplified corpora during training of NMT systems, with no changes to the network architecture  In the experiments on two datasets, we achieve substantial gains in all tasks, and new SOTA results, via back-translation of simplified sentences into the ordinary sentences, and treating this synthetic data as additional training data  Because we do not change the neural network architecture to integrate simplified corpora, our method can be easily applied to other Neural Text Simplification (NTS) systems  We expect that the effectiveness of our method not only varies with the quality of the NTS system used for back-translation, but also depends on the amount of available parallel and simplified corpora  In the paper, we have only utilized data from Wikipedia for simplified sentences  In the future, many other text sources are available and the impact of not only size, but also of domain should be investigated 

ground_truth: combines the information from these sources using a feed-forward neural model

encodes the information from audio and text sequences using dual RNNs and then combines the information from these sources using a feed-forward neural model

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 4: Introduction
Abusive language refers to any type of insult, vulgarity, or profanity that debases the target; it also can be anything that causes aggravation BIBREF0 , BIBREF1   Abusive language is often reframed as, but not limited to, offensive language BIBREF2 , cyberbullying BIBREF3 , othering language BIBREF4 , and hate speech BIBREF5  
Recently, an increasing number of users have been subjected to harassment, or have witnessed offensive behaviors online BIBREF6   Major social media companies (i e  Facebook, Twitter) have utilized multiple resources—artificial intelligence, human reviewers, user reporting processes, etc —in effort to censor offensive language, yet it seems nearly impossible to successfully resolve the issue BIBREF7 , BIBREF8  
The major reason of the failure in abusive language detection comes from its subjectivity and context-dependent characteristics BIBREF9   For instance, a message can be regarded as harmless on its own, but when taking previous threads into account it may be seen as abusive, and vice versa  This aspect makes detecting abusive language extremely laborious even for human annotators; therefore it is difficult to build a large and reliable dataset BIBREF10  
Previously, datasets openly available in abusive language detection research on Twitter ranged from 10K to 35K in size BIBREF9 , BIBREF11   This quantity is not sufficient to train the significant number of parameters in deep learning models  Due to this reason, these datasets have been mainly studied by traditional machine learning methods  Most recently, Founta et al  founta2018large introduced Hate and Abusive Speech on Twitter, a dataset containing 100K tweets with cross-validated labels  Although this corpus has great potential in training deep models with its significant size, there are no baseline reports to date 
This paper investigates the efficacy of different learning models in detecting abusive language  We compare accuracy using the most frequently studied machine learning classifiers as well as recent neural network models  Reliable baseline results are presented with the first comparative study on this dataset  Additionally, we demonstrate the effect of different features and variants, and describe the possibility for further improvements with the use of ensemble models 
Related Work
The research community introduced various approaches on abusive language detection  Razavi et al  razavi2010offensive applied Naïve Bayes, and Warner and Hirschberg warner2012detecting used Support Vector Machine (SVM), both with word-level features to classify offensive language  Xiang et al  xiang2012detecting generated topic distributions with Latent Dirichlet Allocation BIBREF12 , also using word-level features in order to classify offensive tweets 
More recently, distributed word representations and neural network models have been widely applied for abusive language detection  Djuric et al  djuric2015hate used the Continuous Bag Of Words model with paragraph2vec algorithm BIBREF13 to more accurately detect hate speech than that of the plain Bag Of Words models  Badjatiya et al  badjatiya2017deep implemented Gradient Boosted Decision Trees classifiers using word representations trained by deep learning models  Other researchers have investigated character-level representations and their effectiveness compared to word-level representations BIBREF14 , BIBREF15  
As traditional machine learning methods have relied on feature engineering, (i e  n-grams, POS tags, user information) BIBREF1 , researchers have proposed neural-based models with the advent of larger datasets  Convolutional Neural Networks and Recurrent Neural Networks have been applied to detect abusive language, and they have outperformed traditional machine learning classifiers such as Logistic Regression and SVM BIBREF15 , BIBREF16   However, there are no studies investigating the efficiency of neural models with large-scale datasets over 100K 
Methodology
This section illustrates our implementations on traditional machine learning classifiers and neural network based models in detail  Furthermore, we describe additional features and variant models investigated 
Traditional Machine Learning Models
We implement five feature engineering based machine learning classifiers that are most often used for abusive language detection  In data preprocessing, text sequences are converted into Bag Of Words (BOW) representations, and normalized with Term Frequency-Inverse Document Frequency (TF-IDF) values  We experiment with word-level features using n-grams ranging from 1 to 3, and character-level features from 3 to 8-grams  Each classifier is implemented with the following specifications:
Naïve Bayes (NB): Multinomial NB with additive smoothing constant 1
Logistic Regression (LR): Linear LR with L2 regularization constant 1 and limited-memory BFGS optimization
Support Vector Machine (SVM): Linear SVM with L2 regularization constant 1 and logistic loss function
Random Forests (RF): Averaging probabilistic predictions of 10 randomized decision trees
Gradient Boosted Trees (GBT): Tree boosting with learning rate 1 and logistic loss function
Neural Network based Models
Along with traditional machine learning approaches, we investigate neural network based models to evaluate their efficacy within a larger dataset  In particular, we explore Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variant models  A pre-trained GloVe BIBREF17 representation is used for word-level features 
CNN: We adopt Kim's kim2014convolutional implementation as the baseline  The word-level CNN models have 3 convolutional filters of different sizes [1,2,3] with ReLU activation, and a max-pooling layer  For the character-level CNN, we use 6 convolutional filters of various sizes [3,4,5,6,7,8], then add max-pooling layers followed by 1 fully-connected layer with a dimension of 1024 
Park and Fung park2017one proposed a HybridCNN model which outperformed both word-level and character-level CNNs in abusive language detection  In order to evaluate the HybridCNN for this dataset, we concatenate the output of max-pooled layers from word-level and character-level CNN, and feed this vector to a fully-connected layer in order to predict the output 
All three CNN models (word-level, character-level, and hybrid) use cross entropy with softmax as their loss function and Adam BIBREF18 as the optimizer 
RNN: We use bidirectional RNN BIBREF19 as the baseline, implementing a GRU BIBREF20 cell for each recurrent unit  From extensive parameter-search experiments, we chose 1 encoding layer with 50 dimensional hidden states and an input dropout probability of 0 3  The RNN models use cross entropy with sigmoid as their loss function and Adam as the optimizer 
For a possible improvement, we apply a self-matching attention mechanism on RNN baseline models BIBREF21 so that they may better understand the data by retrieving text sequences twice  We also investigate a recently introduced method, Latent Topic Clustering (LTC) BIBREF22   The LTC method extracts latent topic information from the hidden states of RNN, and uses it for additional information in classifying the text data 
Feature Extension
While manually analyzing the raw dataset, we noticed that looking at the tweet one has replied to or has quoted, provides significant contextual information  We call these, “context tweets"

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. what are the sizes of both datasets?
Context 4:  As humans can better understand a tweet with the reference of its context, our assumption is that computers also benefit from taking context tweets into account in detecting abusive language 
As shown in the examples below, (2) is labeled abusive due to the use of vulgar language  However, the intention of the user can be better understood with its context tweet (1) 
(1) I hate when I'm sitting in front of the bus and somebody with a wheelchair get on 
INLINEFORM0 (2) I hate it when I'm trying to board a bus and there's already an as**ole on it 
Similarly, context tweet (3) is important in understanding the abusive tweet (4), especially in identifying the target of the malice 
(3) Survivors of #Syria Gas Attack Recount `a Cruel Scene' 
INLINEFORM0 (4) Who the HELL is “LIKE" ING this post? Sick people    
Huang et al  huang2016modeling used several attributes of context tweets for sentiment analysis in order to improve the baseline LSTM model  However, their approach was limited because the meta-information they focused on—author information, conversation type, use of the same hashtags or emojis—are all highly dependent on data 
In order to avoid data dependency, text sequences of context tweets are directly used as an additional feature of neural network models  We use the same baseline model to convert context tweets to vectors, then concatenate these vectors with outputs of their corresponding labeled tweets  More specifically, we concatenate max-pooled layers of context and labeled tweets for the CNN baseline model  As for RNN, the last hidden states of context and labeled tweets are concatenated 
Dataset
Hate and Abusive Speech on Twitter BIBREF10 classifies tweets into 4 labels, “normal", “spam", “hateful" and “abusive"  We were only able to crawl 70,904 tweets out of 99,996 tweet IDs, mainly because the tweet was deleted or the user account had been suspended  Table shows the distribution of labels of the crawled data 
Data Preprocessing
In the data preprocessing steps, user IDs, URLs, and frequently used emojis are replaced as special tokens  Since hashtags tend to have a high correlation with the content of the tweet BIBREF23 , we use a segmentation library BIBREF24 for hashtags to extract more information 
For character-level representations, we apply the method Zhang et al  zhang2015character proposed  Tweets are transformed into one-hot encoded vectors using 70 character dimensions—26 lower-cased alphabets, 10 digits, and 34 special characters including whitespace 
Training and Evaluation
In training the feature engineering based machine learning classifiers, we truncate vector representations according to the TF-IDF values (the top 14,000 and 53,000 for word-level and character-level representations, respectively) to avoid overfitting  For neural network models, words that appear only once are replaced as unknown tokens 
Since the dataset used is not split into train, development, and test sets, we perform 10-fold cross validation, obtaining the average of 5 tries; we divide the dataset randomly by a ratio of 85:5:10, respectively  In order to evaluate the overall performance, we calculate the weighted average of precision, recall, and F1 scores of all four labels, “normal”, “spam”, “hateful”, and “abusive” 
Empirical Results
As shown in Table , neural network models are more accurate than feature engineering based models (i e  NB, SVM, etc ) except for the LR model—the best LR model has the same F1 score as the best CNN model 
Among traditional machine learning models, the most accurate in classifying abusive language is the LR model followed by ensemble models such as GBT and RF  Character-level representations improve F1 scores of SVM and RF classifiers, but they have no positive effect on other models 
For neural network models, RNN with LTC modules have the highest accuracy score, but there are no significant improvements from its baseline model and its attention-added model  Similarly, HybridCNN does not improve the baseline CNN model  For both CNN and RNN models, character-level features significantly decrease the accuracy of classification 
The use of context tweets generally have little effect on baseline models, however they noticeably improve the scores of several metrics  For instance, CNN with context tweets score the highest recall and F1 for “hateful" labels, and RNN models with context tweets have the highest recall for “abusive" tweets 
Discussion and Conclusion
While character-level features are known to improve the accuracy of neural network models BIBREF16 , they reduce classification accuracy for Hate and Abusive Speech on Twitter  We conclude this is because of the lack of labeled data as well as the significant imbalance among the different labels  Unlike neural network models, character-level features in traditional machine learning classifiers have positive results because we have trained the models only with the most significant character elements using TF-IDF values 
Variants of neural network models also suffer from data insufficiency  However, these models show positive performances on “spam" (14%) and “hateful" (4%) tweets—the lower distributed labels  The highest F1 score for “spam" is from the RNN-LTC model (0 551), and the highest for “hateful" is CNN with context tweets (0 309)  Since each variant model excels in different metrics, we expect to see additional improvements with the use of ensemble models of these variants in future works 
In this paper, we report the baseline accuracy of different learning models as well as their variants on the recently introduced dataset, Hate and Abusive Speech on Twitter  Experimental results show that bidirectional GRU networks with LTC provide the most accurate results in detecting abusive language  Additionally, we present the possibility of using ensemble models of variant models and features for further improvements 
Acknowledgments
K  Jung is with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul, Korea  This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No  2016M3C4A7952632), the Technology Innovation Program (10073144) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) 
We would also like to thank Yongkeun Hwang and Ji Ho Park for helpful discussions and their valuable insights 

ground_truth: training set has 89,042 sentence pairs, and the test set has 100 pairs, training set contains 296,402, 2,000 for development and 359 for testing

WikiSmall  89 142 sentence pair and  WikiLarge 298 761 sentence pairs.