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 much improvement is gained from Adversarial Reward Augmented Maximum Likelihood (ARAML)?
Context 1: Introduction
Natural text generation, as a key task in NLP, has been advanced substantially thanks to the flourish of neural models BIBREF0 , BIBREF1   Typical frameworks such as sequence-to-sequence (seq2seq) have been applied to various generation tasks, including machine translation BIBREF2 and dialogue generation BIBREF3   The standard paradigm to train such neural models is maximum likelihood estimation (MLE), which maximizes the log-likelihood of observing each word in the text given the ground-truth proceeding context BIBREF4  
Although widely used, MLE suffers from the exposure bias problem BIBREF5 , BIBREF6 : during test, the model sequentially predicts the next word conditioned on its previous generated words while during training conditioned on ground-truth words  To tackle this problem, generative adversarial networks (GAN) with reinforcement learning (RL) training approaches have been introduced to text generation tasks BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , where the discriminator is trained to distinguish real and generated text samples to provide reward signals for the generator, and the generator is optimized via policy gradient BIBREF7  
However, recent studies have shown that potential issues of training GANs on discrete data are more severe than exposure bias BIBREF14 , BIBREF15   One of the fundamental issues when generating discrete text samples with GANs is training instability  Updating the generator with policy gradient always leads to an unstable training process because it's difficult for the generator to derive positive and stable reward signals from the discriminator even with careful pre-training BIBREF8   As a result, the generator gets lost due to the high variance of reward signals and the training process may finally collapse BIBREF16  
In this paper, we propose a novel adversarial training framework called Adversarial Reward Augmented Maximum Likelihood (ARAML) to deal with the instability issue of training GANs for text generation  At each iteration of adversarial training, we first train the discriminator to assign higher rewards to real data than to generated samples  Then, inspired by reward augmented maximum likelihood (RAML) BIBREF17 , the generator is updated on the samples acquired from a stationary distribution with maximum likelihood estimation (MLE), weighted by the discriminator's rewards  This stationary distribution is designed to guarantee that training samples are surrounding the real data, thus the exploration space of our generator is indeed restricted by the MLE training objective, resulting in more stable training  Compared to other text GANs with RL training techniques, our framework acquires samples from the stationary distribution rather than the generator's distribution, and uses RAML training paradigm to optimize the generator instead of policy gradient  Our contributions are mainly as follows:
Related Work
Recently, text generation has been widely studied with neural models trained with maximum likelihood estimation BIBREF4   However, MLE tends to generate universal text BIBREF18   Various methods have been proposed to enhance the generation quality by refining the objective function BIBREF18 , BIBREF19 or modifying the generation distribution with external information like topic BIBREF20 , sentence type BIBREF21 , emotion BIBREF22 and knowledge BIBREF23  
As mentioned above, MLE suffers from the exposure bias problem BIBREF5 , BIBREF6   Thus, reinforcement learning has been introduced to text generation tasks such as policy gradient BIBREF6 and actor-critic BIBREF24   BIBREF17 proposed an efficient and stable approach called Reward Augmented Maximum Likelihood (RAML), which connects the log-likelihood and expected rewards to incorporate MLE training objective into RL framework 
Since some text generation tasks have no explicit metrics to be directly optimized, adversarial training has been applied to generating discrete text samples with a discriminator to learn a proper reward  For instance, SeqGAN BIBREF7 devised a discriminator to distinguish the real data and generated samples, and a generator to maximize the reward from the discriminator via policy gradient  Other variants of GANs have been proposed to improve the generator or the discriminator  To improve the generator, MaliGAN BIBREF8 developed a normalized maximum likelihood optimization target for the generator to stably model the discrete sequences  LeakGAN BIBREF11 guided the generator with reward signals leaked from the discriminator at all generation steps to deal with long text generation task  MaskGAN BIBREF10 employed an actor-critic architecture to make the generator fill in missing text conditioned on the surrounding context, which is expected to mitigate the problem of mode collapse  As for the discriminator, RankGAN BIBREF9 replaced traditional discriminator with a ranker to learn the relative ranking information between the real texts and generated ones  Inverse reinforcement learning BIBREF12 used a trainable reward approximator as the discriminator to provide dense reward signals at each generation step  DPGAN BIBREF13 introduced a language model based discriminator and regarded cross-entropy as rewards to promote the diversity of generation results 
The most similar works to our model are RAML BIBREF17 and MaliGAN BIBREF8 : 1) Compared with RAML, our model adds a discriminator to learn the reward signals instead of choosing existing metrics as rewards  We believe that our model can adapt to various text generation tasks, particularly those without explicit evaluation metrics  2) Unlike MaliGAN, we acquire samples from a fixed distribution near the real data rather than the generator's distribution, which is expected to make the training process more stable 
Task Definition and Model Overview
Text generation can be formulated as follows: given the real data distribution INLINEFORM0 , the task is to train a generative model INLINEFORM1 where INLINEFORM2 can fit INLINEFORM3 well  In this formulation, INLINEFORM4 and INLINEFORM5 denotes a word in the vocabulary INLINEFORM6  
Figure FIGREF3 shows the overview of our model ARAML  This adversarial training framework consists of two phases: 1) The discriminator is trained to assign higher rewards to real data than to generated data  2) The generator is trained on the samples acquired from a stationary distribution with reward augmented MLE training objective  This training paradigm of the generator indeed constrains the search space with the MLE training objective, which alleviates the issue of unstable training 
Discriminator
The discriminator INLINEFORM0 aims to distinguish real data and generated data like other GANs  Inspired by Least-Square GAN BIBREF25 , we devise the loss function as follows: DISPLAYFORM0
This loss function forces the discriminator to assign higher rewards to real data than to generated data, so the discriminator can learn to provide more proper rewards as the training proceeds 
Generator
The training objective of our generator INLINEFORM0 is derived from the objective of other discrete GANs with RL training method: DISPLAYFORM0
where INLINEFORM0 denotes the rewards from the discriminator INLINEFORM1 and the entropy regularized term INLINEFORM2 encourages INLINEFORM3 to generate diverse text samples  INLINEFORM4 is a temperature hyper-parameter to balance these two terms 
As mentioned above, discrete GANs suffer from the instability issue due to policy gradient, thus they are consequently difficult to train  Inspired by RAML BIBREF17 , we introduce an exponential payoff distribution INLINEFORM0 to connect RL loss with RAML loss: DISPLAYFORM0
where INLINEFORM0   Thus, we can rewrite INLINEFORM1 with INLINEFORM2 and INLINEFORM3 as follows: DISPLAYFORM0
Following RAML, we remove the constant term and optimize the KL divergence in the opposite direction: DISPLAYFORM0
where INLINEFORM0 is a constant in the training phase of the generator  It has been proved that INLINEFORM1 and INLINEFORM2 are equivalent up to their first order Taylor approximations, and they have the same global optimum BIBREF17   INLINEFORM3 can be trained in a MLE-like fashion but sampling from the distribution INLINEFORM4 is intractable in the adversarial setting, because INLINEFORM5 varies with the discriminator INLINEFORM6   Thus, we introduce importance sampling to separate sampling process from INLINEFORM7 and obtain the final loss function: DISPLAYFORM0
where INLINEFORM0 denotes a stationary distribution and INLINEFORM1   To optimize this loss function, we first construct the fixed distribution INLINEFORM2 to get samples, and devise the proper reward function INLINEFORM3 to train the generator in a stable and effective way 
We construct the distribution INLINEFORM0 based on INLINEFORM1 : DISPLAYFORM0
In this way, INLINEFORM0 can be designed to guarantee that INLINEFORM1 is near INLINEFORM2 , leading to a more stable training process  To obtain a new sample INLINEFORM3 from a real data sample INLINEFORM4 , we can design three steps which contain sampling an edit distance INLINEFORM5 , the positions INLINEFORM6 for substitution and the new words INLINEFORM7 filled into the corresponding positions  Thus, INLINEFORM8 can be decomposed into three terms: DISPLAYFORM0
The first step is to sample an edit distance based on a real data sample INLINEFORM0 , where INLINEFORM1 is a sequence of length INLINEFORM2   The number of sentences which have the edit distance INLINEFORM3 to some input sentence can be computed approximately as below: DISPLAYFORM0
where INLINEFORM0 denotes the number of sentences which have an edit distance INLINEFORM1 to a sentence of length INLINEFORM2 , and INLINEFORM3 indicates the size of vocabulary  We then follow BIBREF17 to re-scale the counts by INLINEFORM4 and do normalization, so that we can sample an edit distance INLINEFORM5 from: DISPLAYFORM0
where INLINEFORM0 , as a temperature hyper-parameter, restricts the search space surrounding the original sentence  Larger INLINEFORM1 brings more samples with long edit distances 
The next step is to select positions for substitution based on the sampled edit distance INLINEFORM0   Intuitively, we can randomly choose INLINEFORM1 distinct positions in INLINEFORM2 to be replaced by new words  The probability of choosing the position INLINEFORM3 is calculated as follows: DISPLAYFORM0
Following this sampling strategy, we can obtain the position set INLINEFORM0   This strategy approximately guarantees that the edit distance between a new sentence and the original sentence is INLINEFORM1  
At the final step, our model determines new words for substitution at each sampled position INLINEFORM0   We can formulate this sampling process from the original sequence INLINEFORM1 to a new sample INLINEFORM2 as a sequential transition INLINEFORM3   At each step from INLINEFORM4 to INLINEFORM5 INLINEFORM6 , we first sample a new word INLINEFORM7 from the distribution INLINEFORM8 , then replace the old word at position INLINEFORM9 of INLINEFORM10 to obtain INLINEFORM11   The whole sampling process can be decomposed as follows: DISPLAYFORM0
There are two common sampling strategies to model INLINEFORM0 , i e  random sampling and constrained sampling  Random sampling strategy samples a new word INLINEFORM1 according to the uniform distribution over the vocabulary INLINEFORM2 BIBREF17 , while constrained sampling strategy samples INLINEFORM3 to maximize the language model score of the target sentence INLINEFORM4 BIBREF26 , BIBREF27   Here, we adopt constrained sampling in our model and compare the performances of two strategies in the experiment 
We devise the reward function INLINEFORM0 according to the discriminator's output INLINEFORM1 and the stationary distribution INLINEFORM2 : DISPLAYFORM0
Intuitively, this reward function encourages the generator to generate sentences with large sampling probability and high rewards from the discriminator  Thus, the weight of samples INLINEFORM0 can be calculated as follows: DISPLAYFORM0
So far, we can successfully optimize the generator's loss INLINEFORM0 via Equation EQREF12   This training paradigm makes our generator avoid possible variances caused by policy gradient and get more stable reward signals from the discriminator, because our generator is restricted to explore the training samples near the real data 
[htb] Adversarial Reward Augmented Maximum Likelihood [1]
Total adversarial training iterations: INLINEFORM0
Steps of training generator: INLINEFORM0
Steps of training discriminator: INLINEFORM0
Pre-train the generator INLINEFORM0 with MLE loss Generate samples from INLINEFORM1 Pre-train the discriminator INLINEFORM2 via Eq ( EQREF6 ) Construct INLINEFORM3 via Eq ( EQREF14 ) - Eq ( EQREF19 ) each INLINEFORM4 each INLINEFORM5 Update INLINEFORM6 via Eq ( EQREF12 ) each INLINEFORM7 Update INLINEFORM8 via Eq ( EQREF6 )
Extension to Conditional Text Generation
We have shown our adversarial training framework for text generation tasks without an input  Actually, it can also be extended to conditional text generation tasks like dialogue generation  Given the data distribution INLINEFORM0 where INLINEFORM1 denote contexts and responses respectively, the objective function of ARAML's generator can be modified as below: DISPLAYFORM0
where INLINEFORM0 and INLINEFORM1 is trained to distinguish whether INLINEFORM2 is the true response to INLINEFORM3  
Comparison with RAML and MaliGAN
The most similar works to our framework are RAML BIBREF17 and MaliGAN BIBREF8   The main difference among them is the training objective of their generators  We have shown different objective functions in Table TABREF26   For comparison, we use the form with no input for all the three models 
Our model is greatly inspired by RAML, which gets samples from a non-parametric distribution INLINEFORM0 constructed based on a specific reward  Compared to RAML, our reward comes from a learnable discriminator which varies as the adversarial training proceeds rather than a specific reward function  This difference equips our framework with the ability to adapt to the text generation tasks with no explicit evaluation metrics as rewards 
Our model is also similar to MaliGAN, which gets samples from the generator's distribution  In MaliGAN's training objective, INLINEFORM0 also indicates the generator's distribution but it's used in the sampling phase and fixed at each optimization step  The weight of samples INLINEFORM1   Different from our model, MaliGAN acquires samples from the generator's distribution INLINEFORM2 , which usually brings samples with low rewards even with careful pre-training for the generator, leading to training instability  Instead, our framework gets samples from a stationary distribution INLINEFORM3 around real data, thus our training process is more stable 
Datasets
We evaluated ARAML on three datasets: COCO image caption dataset BIBREF28 , EMNLP2017 WMT dataset and WeiboDial single-turn dialogue dataset BIBREF29   COCO and EMNLP2017 WMT are the common benchmarks with no input to evaluate the performance of discrete GANs, and we followed the existing works to preprocess these datasets BIBREF12 , BIBREF11   WeiboDial, as a dialogue dataset, was applied to test the performance of our model with input trigger  We simply removed post-response pairs containing low-frequency words and randomly selected a subset for our training/test set  The statistics of three datasets are presented in Table TABREF28  
Baselines
We compared our model with MLE, RL and GAN baselines  Since COCO and EMNLP2017 WMT don't have input while WeiboDial regards posts as input, we chose the following baselines respectively:
MLE: a RNN model trained with MLE objective BIBREF4   Its extension, Seq2Seq, can work on the dialogue dataset BIBREF2  
SeqGAN: The first text GAN model that updates the generator with policy gradient based on the rewards from the discriminator BIBREF7  
LeakGAN: A variant of SeqGAN that provides rewards based on the leaked information of the discriminator for the generator BIBREF11  
MaliGAN: A variant of SeqGAN that optimizes the generator with a normalized maximum likelihood objective BIBREF8  
IRL: This inverse reinforcement learning method replaces the discriminator with a reward approximator to provide dense rewards BIBREF12  
RAML: A RL approach to incorporate MLE objective into RL training framework, which regards BLEU as rewards BIBREF17  
DialogGAN: An extension of SeqGAN tuned to dialogue generation task with MLE objective added to the adversarial objective BIBREF16  
DPGAN: A variant of DialogGAN which uses a language model based discriminator and regards cross-entropy as rewards BIBREF13  
Note that MLE, SeqGAN, LeakGAN, MaliGAN and IRL are the baselines on COCO and EMNLP2017 WMT, while MLE, RAML, DialogGAN, and DPGAN on WeiboDial  The original codes are used to test the baselines 
Implementation Details
The implementation details of our model are shown in Table TABREF31   For COCO / EMNLP2017, the generator is a LSTM unit BIBREF30 with 128 cells, and the discriminator is implemented based on BIBREF7   For WeiboDial, the generator is an encoder-decoder structure with attention mechanism, where both the encoder and the decoder consist of a two-layer GRU BIBREF31 with 128 cells  The discriminator is implemented based on BIBREF32   The language model used in the constrained sampling of ARAML is implemented in the same setting as the generators, and is pre-trained on the training set of each dataset  The codes and the datasets are available at https://github com/kepei1106/ARAML 
As for the details of the baselines, the generators of all the baselines except LeakGAN are the same as ours  Note that the generator of LeakGAN consists of a hierarchical LSTM unit, thus we followed the implementation in the original paper  In terms of the differences, the discriminators of GAN baselines are implemented based on the original papers  Other hyper-parameters of baselines including batch size, learning rate, and pre-training epochs, were set based on the original codes, because the convergence of baselines is sensitive to these hyper-parameters 
Language Generation on COCO and EMNLP2017 WMT
We adopted forward/reverse perplexity BIBREF33 and Self-BLEU BIBREF34 to evaluate the quality of generated texts  Forward perplexity (PPL-F) indicates the perplexity on the generated data provided by a language model trained on real data to measure the fluency of generated samples  Reverse perplexity (PPL-R) switches the roles of generated data and real data to reflect the discrepancy between the generated distribution and the data distribution  Self-BLEU (S-BLEU) regards each sentence in the generated collection as hypothesis and the others as reference to obtain BLEU scores, which evaluates the diversity of generated results 
Results are shown in Table TABREF33   LeakGAN performs best on forward perplexity because it can generate more fluent samples  As for reverse perplexity, our model ARAML beats other baselines, showing that our model can fit the data distribution better  Other GANs, particularly LeakGAN, obtain high reverse perplexity due to mode collapse BIBREF12 , thus they only capture limited fluent expressions, resulting in large discrepancy between the generated distribution and data distribution  ARAML also outperforms the baselines in terms of Self-BLEU, indicating that our model doesn't fall into mode collapse with the help of the MLE training objective and has the ability to generate more diverse sentences 
We also provide standard deviation of each metric in Table TABREF33 , reflecting the stability of each model's performance  Our model ARAML nearly achieves the smallest standard deviation in all the metrics, indicating that our framework outperforms policy gradient in the stability of adversarial training 
Dialogue Generation on WeiboDial
Dialogue evaluation is an open problem and existing works have found that automatic metrics have low correlation to human evaluation BIBREF35 , BIBREF36 , BIBREF37   Thus, we resorted to manual evaluation to assess the generation quality on WeiboDial  We randomly sampled 200 posts from the test set and collected the generated results from all the models  For each pair of responses (one from ARAML and the other from a baseline, given the same input post), five annotators were hired to label which response is better (i e  win, lose or tie) in terms of grammaticality (whether a response itself is grammatical and logical) and relevance (whether a response is appropriate and relevant to the post)  The two metrics were evaluated independently 
The evaluation results are shown in Table TABREF35   To measure the inter-annotator agreement, we calculated Fleiss' kappa BIBREF38 for each pair-wise comparison where results show moderate agreement ( INLINEFORM0 )  We also conducted sign test to check the significance of the differences 
As shown in Table TABREF35 , ARAML performs significantly better than other baselines in all the cases  This result indicates that the samples surrounding true responses provide stable rewards for the generator, and stable RAML training paradigm significantly enhances the performance in both metrics 
Further Analysis on Stability
To verify the training stability, we conducted experiments on COCO many times and chose the best 5 trials for SeqGAN, LeakGAN, IRL, MaliGAN and ARAML, respectively  Then, we presented the forward/reverse perplexity in the training process in Figure FIGREF38   We can see that our model with smaller standard deviation is more stable than other GAN baselines in both metrics  Although LeakGAN reaches the best forward perplexity, its standard deviation is extremely large and it performs badly in reverse perplexity, indicating that it generates limited expressions that are grammatical yet divergent from the data distribution 
Ablation Study
The temperature INLINEFORM0 controls the search space surrounding the real data as we analyze in Section UID13   To investigate its impact on the performance of our model, we fixed all the other hyper-parameters and test ARAML with different temperatures on COCO 
The experimental results are shown in Figure FIGREF41   We can see that as the temperature becomes larger, forward perplexity increases gradually while Self-BLEU decreases  As mentioned in Section UID13 , large temperatures encourage our generator to explore the samples that are distant from real data distribution, thus the diversity of generated results will be improved  However, these samples distant from the data distribution are more likely to be poor in fluency, leading to worse forward perplexity  Reverse perplexity is influenced by both generation quality and diversity, so the correlation between temperature and reverse perplexity is not intuitive  We can observe that the model with INLINEFORM0 reaches the best reverse perplexity 
We have mentioned two common sampling strategies in Section UID13 , i e  random sampling and constrained sampling  To analyze their impact, we keep all the model structures and hyper-parameters fixed and test ARAML with these two strategies on COCO 
Table TABREF45 shows the results  It's obvious that random sampling hurts the model performance except Self-BLEU-1, because it indeed allows low-quality samples available to the generator  Exploring these samples degrades the quality and diversity of generated results  Despite the worse performance on automatic metrics, random sampling doesn't affect the training stability of our framework  The standard deviation of ARAML-R is still smaller than other GAN baselines 
Case Study
Table TABREF47 presents the examples generated by the models on COCO  We can find that other baselines suffer from grammatical errors (e g  “in front of flying her kite" from MLE), repetitive expressions (e g  “A group of people" from IRL) and incoherent statements (e g  “A group of people sitting on a cell phone” from IRL)  By contrast, our model performs well in these sentences and has the ability to generate grammatical and coherent results 
Table TABREF48 shows the generated examples on WeiboDial  It's obvious that other baselines don't capture the topic word “late" in the post, thus generate irrelevant responses  ARAML can provide a response that is grammatical and closely relevant to the post 
Conclusion
We propose a novel adversarial training framework to deal with the instability problem of current GANs for text generation  To address the instability issue caused by policy gradient, we incorporate RAML into the advesarial training paradigm to make our generator acquire stable rewards  Experiments show that our model performs better than several state-of-the-art GAN baselines with lower training variance, yet producing better performance on three text generation tasks 
Acknowledgments
This work was supported by the National Science Foundation of China (Grant No  61936010/61876096) and the National Key R&D Program of China (Grant No  2018YFC0830200)  We would like to thank THUNUS NExT Joint-Lab for the support 
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: ARAM has achieved improvement over all baseline methods using reverese perplexity and  slef-BLEU metric.  The maximum reverse perplexity improvement 936,16 is gained for EMNLP2017  WMT  dataset and  48,44 for COCO dataset.

Compared to the baselines, ARAML does not do better in terms of perplexity on COCO and EMNLP 2017 WMT datasets, but it does by up to 0.27 Self-BLEU points on COCO and 0.35 Self-BLEU on EMNLP 2017 WMT. In terms of Grammaticality and Relevance, it scores better than the baselines on up to 75.5% and 73% of the cases 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 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
Emotion detection has long been a topic of interest to scholars in natural language processing (NLP) domain  Researchers aim to recognize the emotion behind the text and distribute similar ones into the same group  Establishing an emotion classifier can not only understand each user's feeling but also be extended to various application, for example, the motivation behind a user's interests BIBREF0  Based on releasing of large text corpus on social media and the emotion categories proposed by BIBREF1, BIBREF2, numerous models have provided and achieved fabulous precision so far  For example, DeepMoji BIBREF3 which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence  CARER BIBREF4 learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts 
As methods become mature, text-based emotion detecting applications can be extended from a single utterance to a dialogue contributed by a series of utterances  Table TABREF2 illustrates the difference between single utterance and dialogue emotion recognition  The same utterances in Table TABREF2, even the same person said the same sentence, the emotion it convey may be various, which may depend on different background of the conversation, tone of speaking or personality  Therefore, for emotion detection, the information from preceding utterances in a conversation is relatively critical 
In SocialNLP 2019 EmotionX, the challenge is to recognize emotions for all utterances in EmotionLines dataset, a dataset consists of dialogues  According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT) BIBREF5, FriendsBERT and ChatBERT  In this paper, we introduce our approaches including causal utterance modeling, model pre-training, and fine-turning 
Dataset
EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues  The former comes from the scripts of the Friends TV sitcom  The other is made up of Facebook messenger chats  Each subset includes $1,000$ English dialogues, and each dialogue can be further divided into a few consecutive utterances  All the utterances are annotated by five annotators on a crowd-sourcing platform (Amazon Mechanical Turk), and the labeling work is only based on the textual content  Annotator votes for one of the seven emotions, namely Ekman’s six basic emotions BIBREF1, plus the neutral  If none of the emotion gets more than three votes, the utterance will be marked as “non-neutral” 
For the datasets, there are properties worth additional mentioning  Although Friends and EmotionPush share the same data format, they are quite different in nature  Friends is a speech-based dataset which is annotated dialogues from the TV sitcom  It means most of the utterances are generated by the a few main characters  The personality of a character often affects the way of speaking, and therefore “who is the speaker" might provide extra clues for emotion prediction  In contrast, EmotionPush does not have this trait due to the anonymous mechanism  In addition, features such as typo, hyperlink, and emoji that only appear in chat-based data will need some domain-specific techniques to process 
Incidentally, the objective of the challenge is to predict the emotion for each utterance  Just, according to EmotionX 2019 specification, there are only four emotions be selected as our label candidates, which are Joy, Sadness, Anger, and Neutral  These emotions will be considered during performance evaluation  The technical detail will also be introduced and discussed in following Section SECREF13 and Section SECREF26 
Model Description
For this challenge, we adapt BERT which is proposed by BIBREF5 to help understand the context at the same time  Technically, BERT, designed on end-to-end architecture, is a deep pre-trained transformer encoder that dynamically provides language representation and BERT already achieved multiple state-of-the-art results on GLUE benchmark BIBREF7 and many tasks  A quick recap for BERT's architecture and its pre-training tasks will be illustrated in the following subsections 
Model Description ::: Model Architecture
BERT, the Bidirectional Encoder Representations from Transformers, consists of several transformer encoder layers that enable the model to extract very deep language features on both token-level and sentence-level  Each transformer encoder contains multi-head self-attention layers that provide ability to learn multiple attention feature of each word from their bidirectional context  The transformer and its self-attention mechanism are proposed by BIBREF8  This self-attention mechanism can be interpreted as a key-value mapping given query  By given the embedding vector for token input, the query ($Q$), key ($K$) and value ($V$) are produced by the projection from each three parameter matrices where $W^Q \in \mathbb {R}^{d_{{\rm model}} \times d_{k}}, W^K \in \mathbb {R}^{d_{\rm model} \times d_{k}}$ and $W^V \in \mathbb {R}^{d_{\rm model} \times d_{v}}$  The self-attention BIBREF8 is formally represented as:
The $ d_k = d_v = d_{\rm model} = 1024$ in BERT large version and 768 in BERT base version  Once model can extract attention feature, we can extend one self-attention into multi-head self-attention, this extension makes sub-space features can be extracted in same time by this multi-head configuration  Overall, the multi-attention mechanism is adopt for each transformer encoder, and several of encoder layer will be stacked together to form a deep transformer encoder 
For the model input, BERT allow us take one sentence as input sequence or two sentences together as one input sequence, and the maximum length of input sequence is 512  The way that BERT was designed is for giving model the sentence-level and token-level understanding  In two sentences case, a special token ([SEP]) will be inserted between two sentences  In addition, the first input token is also a special token ([CLS]), and its corresponding ouput will be vector place for classification during fine-tuning  The outputs of the last encoder layer corresponding to each input token can be treated as word representations for each token, and the word representation of the first token ([CLS]) will be consider as classification (output) representation for further fine-tuning tasks  In BERT, this vector is denoted as $C \in \mathbb {R}^{d_{\rm model}} $, and a classification layer is denoted as $ W \in \mathbb {R}^{K \times d_{\rm model}}$, where $K$ is number of classification labels  Finally, the prediction $P$ of BERT is represented as $P = {\rm softmax}(CW^T)$ 
Model Description ::: Pre-training Tasks
In pre-training, intead of using unidirectional language models, BERT developed two pre-training tasks: (1) Masked LM (cloze test) and (2) Next Sentence Prediction  At the first pre-training task, bidirectional language modeling can be done at this cloze-like pre-training  In detail, 15% tokens of input sequence will be masked at random and model need to predict those masked tokens  The encoder will try to learn contextual representations from every given tokens due to masking tokens at random  Model will not know which part of the input is going to be masked, so that the information of each masked tokens should be inferred by remaining tokens  At Next Sentence Prediction, two sentences concatenated together will be considered as model input  In order to give model a good nature language understanding, knowing relationship between sentence is one of important abilities  When generating input sequences, 50% of time the sentence B is actually followed by sentence A, and rest 50% of the time the sentence B will be picked randomly from dataset, and model need to predict if the sentence B is next sentence of sentence A  That is, the attention information will be shared between sentences  Such sentence-level understanding may have difficulties to be learned at first pre-training task (Masked LM), therefore, the pre-training task (NSP) is developed as second training goal to capture the cross sentence relationship 
In this competition, limited by the size of dataset and the challenge in contextual emotion recognition, we consider BERT with both two pre-training tasks can give a good starting point to extract emotion changing during dialogue-like conversation  Especially the second pre-training task, it might be more important for dialogue-like conversation where the emotion may various by the context of continuous utterances  That is, given a set of continues conversations, the emotion of current utterance might be influenced by previous utterance  By this assumption and with supporting from the experiment results of BERT, we can take sentence A as one-sentence context and consider sentence B as the target sentence for emotion prediction  The detail will be described in Section SECREF4 
Methodology
The main goal of the present work is to predict the emotion of utterance within the dialogue  Following are four major difficulties we concern about:
The emotion of the utterances depends not only on the text but also on the interaction happened earlier 
The source of the two datasets are different  Friends is speech-based dialogues and EmotionPush is chat-based dialogues  It makes datasets possess different characteristics 
There are only $1,000$ dialogues in both training datasets which are not large enough for the stability of training a complex neural-based model 
The prediction targets (emotion labels) are highly unbalanced 
The proposed approach is summarized in Figure FIGREF3, which aims to overcome these challenges  The framework could be separated into three steps and described as follow:
Methodology ::: Causal Utterance Modeling
Given a dialogue $D^{(i)}$ which includes sequence of utterances denoted as $D^{(i)}=(u^{(i)}_{1}, u^{(i)}_{2},    , u^{(i)}_{n})$, where $i$ is the index in dataset and $n$ is the number of utterances in the given dialogue  In order to conserve the emotional information of both utterance and conversation, we rearrange each two consecutive utterances $u_{t}, u_{t-1}$ into a single sentence representation $x_{t}$ as
The corresponding sentence representation corpus $X^{(i)}$ are denoted as $X^{(i)}=(x^{(i)}_{1}, x^{(i)}_{2},    , x^{(i)}_{n})$  Note that the first utterance within a conversation does not have its causal utterance (previous sentence), therefore, the causal utterance will be set as [None]  A practical example of sentence representation is shown in Table TABREF11 
Since the characteristics of two datasets are not identical, we customize different causal utterance modeling strategies to refine the information in text 
For Friends, there are two specific properties  The first one is that most dialogues are surrounding with the six main characters, including Rachel, Monica, Phoebe, Joey, Chandler, and Ross

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 sources of the datasets?
Context 3:  The utterance ratio of given by the six roles is up to $83 4\%$  Second, the personal characteristics of the six characters are very clear  Each leading role has its own emotion undulated rule  To make use of these features, we introduce the personality tokenization which help learning the personality of the six characters  Personality tokenization concatenate the speaker and says tokens before the input utterance if the speaker is one of the six characters  The example is shown in Table TABREF12 
For EmotionPush, the text are informal chats which including like slang, acronym, typo, hyperlink, and emoji  Another characteristic is that the specific name entities are tokenized with random index  (e g  “organization_80”, “person_01”, and “time_12”)  We consider some of these informal text are related to expressing emotion such as repeated typing, purposed capitalization, and emoji (e g  “:D”, “:(”, and “<3”))  Therefore, we keep most informal expressions but only process hyperlinks, empty utterance, and name entities by unifying the tokens 
Methodology ::: Model Pre-training
Since the size of both datasets are not large enough for complex neural-based model training as well as BERT model is only pre-train on formal text datasets, the issues of overfitting and domain bias are important considerations for design the pre-training process 
To avoid our model overfitting on the training data and increase the understanding of informal text, we adapted BERT and derived two models, namely FriendsBERT and ChatBERT, with different pre-training tasks before the formal training process for Friends and EmotionPush dataset, respectively  The pre-training strategies are described below 
For pre-training FriendsBERT, we collect the completed scripts of all ten seasons of Friends TV shows from emorynlp which includes 3,107 scenes within 61,309 utterances  All the utterances are followed the preprocessing methods mentions above to compose the corpus for Masked language model pre-training task  The consequent utterances in the same scenes are considered as the consequent sentences to pre-train the Next Sentence Prediction task  In the pre-training process, the training loss is the sum of the mean likelihood of two pre-train tasks 
For pre-training ChatBERT, we pre-train our model on the Twitter dataset, since the text and writing style on Twitter are close to the chat text where both may involved with many informal words or emoticons as well  The Twitter emotion dataset, 8 basic emotions from emotion wheel BIBREF1, was collected by twitter streaming API with specific emotion-related hashtags, such as #anger, #joy, #cry, #sad and etc  The hashtags in tweets are treated as emotion label for model fine-tuning  The tweets were fine-grined processing followed the rules in BIBREF9, BIBREF4, including duplicate tweets removing, the emotion hashtags must appearing in the last position of a tweet, and etc  The statis of tweets were summarized in Table TABREF17  Each tweet and corresponding emotion label composes an emotion classification dataset for pre-training 
Methodology ::: Fine-tuning
Since our emotion recognition task is treated as a sequence-level classification task, the model would be fine-tuned on the processed training data  Following the BERT construction, we take the first embedding vector which corresponds to the special token [CLS] from the final hidden state of the Transformer encoder  This vector represents the embedding vector of the corresponding conversation utterances which is denoted as $\mathbf {C} \in \mathbb {R}^{H}$, where $H$ is the embedding size  A dense neural layer is treated as a classification layer which consists of parameters $\mathbf {W} \in \mathbb {R}^{K\times H}$ and $\mathbf {b} \in \mathbb {R}^{K}$, where $K$ is the number of emotion class  The emotion prediction probabilities $\mathbf {P} \in \mathbb {R}^{K}$ are computed by a softmax activation function as
All the parameters in BERT and the classification layer would be fine-turned together to minimize the Negative Log Likelihood (NLL) loss function, as Equation (DISPLAY_FORM22), based on the ground truth emotion label $c$ 
In order to tackle the problem of highly unbalanced emotion labels, we apply weighted balanced warming on NLL loss function, as Equation (DISPLAY_FORM23), in the first epoch of fine-tuning procedure 
where $\mathbf {w}$ are the weights of corresponding emotion label $c$ which are computed and normalize by the frequency as
By adding the weighted balanced warming on NLL loss, the model could learn to predict the minor emotions (e g  anger and sadness) earlier and make the training process more stable  Since the major evaluation metrics micro F1-score is effect by the number of each label, we only apply the weighted balanced warming in first epoch to optimize the performance 
Experiments
Since the EmotionX challenge only provided the gold labels in training data, we pick the best performance model (weights) to predict the testing data  In this section, we present the experiment and evaluation results 
Experiments ::: Experimental Setup
The EmotionX challenge consists of $1,000$ dialogues for both Friends and EmotionPush  In all of our experiments, each dataset is separated into top 800 dialogues for training and last 200 dialogues for validation  Since the EmotionX challenge considers only the four emotions (anger, joy, neutral, and sadness) in the evaluation stage, we ignore all the data point corresponding to other emotions directly  The details of emotions distribution are shown in Table TABREF18 
The hyperparameters and training setup of our models (FriendsBERT and ChatBERT) are shown in Table TABREF25  Some common and easily implemented methods are selected as the baselines embedding methods and classification models  The baseline embedding methods are including bag-of-words (BOW), term frequency–inverse document frequency (TFIDF), and neural-based word embedding  The classification models are including Logistic Regression (LR), Random Forest (RF), TextCNN BIBREF10 with initial word embedding as GloVe BIBREF11, and our proposed model  All the experiment results are based on the best performances of validation results 
Experiments ::: Performance
The experiment results of validation on Friends are shown in Table TABREF19  The proposed model and baselines are evaluated based on the Precision (P ), Recall (R ), and F1-measure (F1) 
For the traditional baselines, namely BOW and TFIDF, we observe that they achieve surprising high F1 scores around $0 81$, however, the scores for Anger and Sadness are lower  This explains that traditional approaches tend to predict the labels with large sample size, such as Joy and Neutral, but fail to take of scarce samples even when an ensemble random forest classifier is adopted  In order to prevent the unbalanced learning, we choose the weighted loss mechanism for both TextCNN and causal modeling TextCNN (C-TextCNN), these models suffer less than the traditional baselines and achieve a slightly balance performance, where there are around 15% and 7% improvement on Anger and Sadness, respectively  We following adopt the casual utterance modeling to original TextCNN, mapping previous utterance as well as target utterance into model  The causal utterance modeling improve the C-TextCNN over TextCNN for 6%, 2% and 1% on Anger, Joy and overall F1 score  Motivated from these preliminary experiments, the proposed FriendsBERT also adopt the ideas of both weighted loss and causal utterance modeling  As compared to the original BERT, single sentence BERT (FriendsBERT-base-s), the proposed FriendsBERT-base improve 1% for Joy and overall F1, and 2% for Sadness  For the final validation performance, our proposed approach achieves the highest scores, which are $0 85$ and $0 86$ for FriendsBERT-base and FriendsBERT-large, respectively 
Overall, the proposed FriendsBERT successfully captures the sentence-level context-awarded information and outperforms all the baselines, which not only achieves high performance on large sample labels, but also on small sample labels  The similar settings are also adapted to EmotionPush dataset for the final evaluation 
Experiments ::: Evaluation Results
The testing dataset consists of 240 dialogues including $3,296$ and $3,536$ utterances in Friends and EmotionPush respectively  We re-train our FriendsBERT and ChatBERT with top 920 training dialogues and predict the evaluation results using the model performing the best validation results  The results are shown in Table TABREF29 and Table TABREF30  The present method achieves $81 5\%$ and $88 5\%$ micro F1-score on the testing dataset of Friends and EmotionPush, respectively 
Conclusion and Future work
In the present work, we propose FriendsBERT and ChatBERT for the multi-utterance emotion recognition task on EmotionLines dataset  The proposed models are adapted from BERT BIBREF5 with three main improvement during the model training procedure, which are the causal utterance modeling mechanism, specific model pre-training, and adapt weighted loss  The causal utterance modeling takes the advantages of the sentence-level context information during model inference  The specific model pre-training helps to against the bias in different text domain  The weighted loss avoids our model to only predict on large size sample  The effectiveness and generalizability of the proposed methods are demonstrated from the experiments 
In future work, we consider to include the conditional probabilistic constraint $P ({\rm Emo}_{B} | \hat{\rm Emo}_{A})$  Model should predict the emotion based on a certain understanding about context emotions  This might be more reasonable for guiding model than just predicting emotion of ${\rm Sentence}_B$ directly  In addition, due to the limitation of BERT input format, ambiguous number of input sentences is now becoming an important design requirement for our future work  Also, personality embedding development will be another future work of the emotion recognition  The personality embedding will be considered as sentence embedding injected into word embedding, and it seems this additional information can contribute some improvement potentially 

ground_truth: Friends TV sitcom, Facebook messenger chats