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 dataset did they use?
Context 1: Introduction
Automatic classification of sentiment has mainly focused on categorizing tweets in either two (binary sentiment analysis) or three (ternary sentiment analysis) categories BIBREF0   In this work we study the problem of fine-grained sentiment classification where tweets are classified according to a five-point scale ranging from VeryNegative to VeryPositive  To illustrate this, Table TABREF3 presents examples of tweets associated with each of these categories  Five-point scales are widely adopted in review sites like Amazon and TripAdvisor, where a user's sentiment is ordered with respect to its intensity  From a sentiment analysis perspective, this defines a classification problem with five categories  In particular, Sebastiani et al  BIBREF1 defined such classification problems whose categories are explicitly ordered to be ordinal classification problems  To account for the ordering of the categories, learners are penalized according to how far from the true class their predictions are 
Although considering different scales, the various settings of sentiment classification are related  First, one may use the same feature extraction and engineering approaches to represent the text spans such as word membership in lexicons, morpho-syntactic statistics like punctuation or elongated word counts BIBREF2 , BIBREF3   Second, one would expect that knowledge from one task can be transfered to the others and this would benefit the performance  Knowing that a tweet is “Positive” in the ternary setting narrows the classification decision between the VeryPositive and Positive categories in the fine-grained setting  From a research perspective this raises the question of whether and how one may benefit when tackling such related tasks and how one can transfer knowledge from one task to another during the training phase 
Our focus in this work is to exploit the relation between the sentiment classification settings and demonstrate the benefits stemming from combining them  To this end, we propose to formulate the different classification problems as a multitask learning problem and jointly learn them  Multitask learning BIBREF4 has shown great potential in various domains and its benefits have been empirically validated BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 using different types of data and learning approaches  An important benefit of multitask learning is that it provides an elegant way to access resources developed for similar tasks  By jointly learning correlated tasks, the amount of usable data increases  For instance, while for ternary classification one can label data using distant supervision with emoticons BIBREF9 , there is no straightforward way to do so for the fine-grained problem  However, the latter can benefit indirectly, if the ternary and fine-grained tasks are learned jointly 
The research question that the paper attempts to answer is the following: Can twitter sentiment classification problems, and fine-grained sentiment classification in particular, benefit from multitask learning? To answer the question, the paper brings the following two main contributions: (i) we show how jointly learning the ternary and fine-grained sentiment classification problems in a multitask setting improves the state-of-the-art performance, and (ii) we demonstrate that recurrent neural networks outperform models previously proposed without access to huge corpora while being flexible to incorporate different sources of data 
Multitask Learning for Twitter Sentiment Classification
In his work, Caruana BIBREF4 proposed a multitask approach in which a learner takes advantage of the multiplicity of interdependent tasks while jointly learning them  The intuition is that if the tasks are correlated, the learner can learn a model jointly for them while taking into account the shared information which is expected to improve its generalization ability  People express their opinions online on various subjects (events, products  ), on several languages and in several styles (tweets, paragraph-sized reviews  ), and it is exactly this variety that motivates the multitask approaches  Specifically for Twitter for instance, the different settings of classification like binary, ternary and fine-grained are correlated since their difference lies in the sentiment granularity of the classes which increases while moving from binary to fine-grained problems 
There are two main decisions to be made in our approach: the learning algorithm, which learns a decision function, and the data representation  With respect to the former, neural networks are particularly suitable as one can design architectures with different properties and arbitrary complexity  Also, as training neural network usually relies on back-propagation of errors, one can have shared parts of the network trained by estimating errors on the joint tasks and others specialized for particular tasks  Concerning the data representation, it strongly depends on the data type available  For the task of sentiment classification of tweets with neural networks, distributed embeddings of words have shown great potential  Embeddings are defined as low-dimensional, dense representations of words that can be obtained in an unsupervised fashion by training on large quantities of text BIBREF10  
Concerning the neural network architecture, we focus on Recurrent Neural Networks (RNNs) that are capable of modeling short-range and long-range dependencies like those exhibited in sequence data of arbitrary length like text  While in the traditional information retrieval paradigm such dependencies are captured using INLINEFORM0 -grams and skip-grams, RNNs learn to capture them automatically BIBREF11   To circumvent the problems with capturing long-range dependencies and preventing gradients from vanishing, the long short-term memory network (LSTM) was proposed BIBREF12   In this work, we use an extended version of LSTM called bidirectional LSTM (biLSTM)  While standard LSTMs access information only from the past (previous words), biLSTMs capture both past and future information effectively BIBREF13 , BIBREF11   They consist of two LSTM networks, for propagating text forward and backwards with the goal being to capture the dependencies better  Indeed, previous work on multitask learning showed the effectiveness of biLSTMs in a variety of problems: BIBREF14 tackled sequence prediction, while BIBREF6 and BIBREF15 used biLSTMs for Named Entity Recognition and dependency parsing respectively 
Figure FIGREF2 presents the architecture we use for multitask learning  In the top-left of the figure a biLSTM network (enclosed by the dashed line) is fed with embeddings INLINEFORM0 that correspond to the INLINEFORM1 words of a tokenized tweet  Notice, as discussed above, the biLSTM consists of two LSTMs that are fed with the word sequence forward and backwards  On top of the biLSTM network one (or more) hidden layers INLINEFORM2 transform its output  The output of INLINEFORM3 is led to the softmax layers for the prediction step  There are INLINEFORM4 softmax layers and each is used for one of the INLINEFORM5 tasks of the multitask setting  In tasks such as sentiment classification, additional features like membership of words in sentiment lexicons or counts of elongated/capitalized words can be used to enrich the representation of tweets before the classification step BIBREF3   The lower part of the network illustrates how such sources of information can be incorporated to the process  A vector “Additional Features” for each tweet is transformed from the hidden layer(s) INLINEFORM6 and then is combined by concatenation with the transformed biLSTM output in the INLINEFORM7 layer 
Experimental setup
Our goal is to demonstrate how multitask learning can be successfully applied on the task of sentiment classification of tweets  The particularities of tweets are to be short and informal text spans  The common use of abbreviations, creative language etc , makes the sentiment classification problem challenging  To validate our hypothesis, that learning the tasks jointly can benefit the performance, we propose an experimental setting where there are data from two different twitter sentiment classification problems: a fine-grained and a ternary  We consider the fine-grained task to be our primary task as it is more challenging and obtaining bigger datasets, e g  by distant supervision, is not straightforward and, hence we report the performance achieved for this task 
Ternary and fine-grained sentiment classification were part of the SemEval-2016 “Sentiment Analysis in Twitter” task BIBREF16   We use the high-quality datasets the challenge organizers released  The dataset for fine-grained classification is split in training, development, development_test and test parts  In the rest, we refer to these splits as train, development and test, where train is composed by the training and the development instances  Table TABREF7 presents an overview of the data  As discussed in BIBREF16 and illustrated in the Table, the fine-grained dataset is highly unbalanced and skewed towards the positive sentiment: only INLINEFORM0 of the training examples are labeled with one of the negative classes 
Feature representation We report results using two different feature sets  The first one, dubbed nbow, is a neural bag-of-words that uses text embeddings to generate low-dimensional, dense representations of the tweets  To construct the nbow representation, given the word embeddings dictionary where each word is associated with a vector, we apply the average compositional function that averages the embeddings of the words that compose a tweet  Simple compositional functions like average were shown to be robust and efficient in previous work BIBREF17   Instead of training embeddings from scratch, we use the pre-trained on tweets GloVe embeddings of BIBREF10   In terms of resources required, using only nbow is efficient as it does not require any domain knowledge  However, previous research on sentiment analysis showed that using extra resources, like sentiment lexicons, can benefit significantly the performance BIBREF3 , BIBREF2   To validate this and examine at which extent neural networks and multitask learning benefit from such features we evaluate the models using an augmented version of nbow, dubbed nbow+  The feature space of the latter, is augmented using 1,368 extra features consisting mostly of counts of punctuation symbols ('!?#@'), emoticons, elongated words and word membership features in several sentiment lexicons  Due to space limitations, for a complete presentation of these features, we refer the interested reader to BIBREF2 , whose open implementation we used to extract them 
Evaluation measure To reproduce the setting of the SemEval challenges BIBREF16 , we optimize our systems using as primary measure the macro-averaged Mean Absolute Error ( INLINEFORM0 ) given by: INLINEFORM1
where INLINEFORM0 is the number of categories, INLINEFORM1 is the set of instances whose true class is INLINEFORM2 , INLINEFORM3 is the true label of the instance INLINEFORM4 and INLINEFORM5 the predicted label  The measure penalizes decisions far from the true ones and is macro-averaged to account for the fact that the data are unbalanced  Complementary to INLINEFORM6 , we report the performance achieved on the micro-averaged INLINEFORM7 measure, which is a commonly used measure for classification 
The models To evaluate the multitask learning approach, we compared it with several other models  Support Vector Machines (SVMs) are maximum margin classification algorithms that have been shown to achieve competitive performance in several text classification problems BIBREF16   SVM INLINEFORM0 stands for an SVM with linear kernel and an one-vs-rest approach for the multi-class problem  Also, SVM INLINEFORM1 is an SVM with linear kernel that employs the crammer-singer strategy BIBREF18 for the multi-class problem  Logistic regression (LR) is another type of linear classification method, with probabilistic motivation  Again, we use two types of Logistic Regression depending on the multi-class strategy: LR INLINEFORM2 that uses an one-vs-rest approach and multinomial Logistic Regression also known as the MaxEnt classifier that uses a multinomial criterion 
Both SVMs and LRs as discussed above treat the problem as a multi-class one, without considering the ordering of the classes  For these four models, we tuned the hyper-parameter INLINEFORM0 that controls the importance of the L INLINEFORM1 regularization part in the optimization problem with grid-search over INLINEFORM2 using 10-fold cross-validation in the union of the training and development data and then retrained the models with the selected values  Also, to account for the unbalanced classification problem we used class weights to penalize more the errors made on the rare classes  These weights were inversely proportional to the frequency of each class  For the four models we used the implementations of Scikit-learn BIBREF19  
For multitask learning we use the architecture shown in Figure FIGREF2 , which we implemented with Keras BIBREF20   The embeddings are initialized with the 50-dimensional GloVe embeddings while the output of the biLSTM network is set to dimension 50  The activation function of the hidden layers is the hyperbolic tangent  The weights of the layers were initialized from a uniform distribution, scaled as described in BIBREF21   We used the Root Mean Square Propagation optimization method  We used dropout for regularizing the network  We trained the network using batches of 128 examples as follows: before selecting the batch, we perform a Bernoulli trial with probability INLINEFORM0 to select the task to train for  With probability INLINEFORM1 we pick a batch for the fine-grained sentiment classification problem, while with probability INLINEFORM2 we pick a batch for the ternary problem  As shown in Figure FIGREF2 , the error is backpropagated until the embeddings, that we fine-tune during the learning process  Notice also that the weights of the network until the layer INLINEFORM3 are shared and therefore affected by both tasks 
To tune the neural network hyper-parameters we used 5-fold cross validation  We tuned the probability INLINEFORM0 of dropout after the hidden layers INLINEFORM1 and for the biLSTM for INLINEFORM2 , the size of the hidden layer INLINEFORM3 and the probability INLINEFORM4 of the Bernoulli trials from INLINEFORM5   During training, we monitor the network's performance on the development set and apply early stopping if the performance on the validation set does not improve for 5 consecutive epochs 
Experimental results Table TABREF9 illustrates the performance of the models for the different data representations  The upper part of the Table summarizes the performance of the baselines  The entry “Balikas et al ” stands for the winning system of the 2016 edition of the challenge BIBREF2 , which to the best of our knowledge holds the state-of-the-art  Due to the stochasticity of training the biLSTM models, we repeat the experiment 10 times and report the average and the standard deviation of the performance achieved 
Several observations can be made from the table  First notice that, overall, the best performance is achieved by the neural network architecture that uses multitask learning  This entails that the system makes use of the available resources efficiently and improves the state-of-the-art performance  In conjunction with the fact that we found the optimal probability INLINEFORM0 , this highlights the benefits of multitask learning over single task learning  Furthermore, as described above, the neural network-based models have only access to the training data as the development are hold for early stopping  On the other hand, the baseline systems were retrained on the union of the train and development sets  Hence, even with fewer resources available for training on the fine-grained problem, the neural networks outperform the baselines  We also highlight the positive effect of the additional features that previous research proposed  Adding the features both in the baselines and in the biLSTM-based architectures improves the INLINEFORM1 scores by several points 
Lastly, we compare the performance of the baseline systems with the performance of the state-of-the-art system of BIBREF2   While BIBREF2 uses n-grams (and character-grams) with INLINEFORM0 , the baseline systems (SVMs, LRs) used in this work use the nbow+ representation, that relies on unigrams  Although they perform on par, the competitive performance of nbow highlights the potential of distributed representations for short-text classification  Further, incorporating structure and distributed representations leads to the gains of the biLSTM network, in the multitask and single task setting 
Similar observations can be drawn from Figure FIGREF10 that presents the INLINEFORM0 scores  Again, the biLSTM network with multitask learning achieves the best performance  It is also to be noted that although the two evaluation measures are correlated in the sense that the ranking of the models is the same, small differences in the INLINEFORM1 have large effect on the scores of the INLINEFORM2 measure 
Conclusion
In this paper, we showed that by jointly learning the tasks of ternary and fine-grained classification with a multitask learning model, one can greatly improve the performance on the second  This opens several avenues for future research  Since sentiment is expressed in different textual types like tweets and paragraph-sized reviews, in different languages (English, German,   ) and in different granularity levels (binary, ternary,  ) one can imagine multitask approaches that could benefit from combining such resources  Also, while we opted for biLSTM networks here, one could use convolutional neural networks or even try to combine different types of networks and tasks to investigate the performance effect of multitask learning  Lastly, while our approach mainly relied on the foundations of BIBREF4 , the internal mechanisms and the theoretical guarantees of multitask learning remain to be better understood 
Acknowledgements
This work is partially supported by the CIFRE N 28/2015 
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

ground_truth:  high-quality datasets  from SemEval-2016 “Sentiment Analysis in Twitter” task

 SemEval-2016 “Sentiment Analysis in Twitter”

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 previous RNN models do they compare with?
Context 2: Introduction
Long short term memory (LSTM) units BIBREF1 are popular for many sequence modeling tasks and are used extensively in language modeling  A key to their success is their articulated gating structure, which allows for more control over the information passed along the recurrence  However, despite the sophistication of the gating mechanisms employed in LSTMs and similar recurrent units, the input and context vectors are treated with simple linear transformations prior to gating  Non-linear transformations such as convolutions BIBREF2 have been used, but these have not achieved the performance of well regularized LSTMs for language modeling BIBREF3  
A natural way to improve the expressiveness of linear transformations is to increase the number of dimensions of the input and context vectors, but this comes with a significant increase in the number of parameters which may limit generalizability  An example is shown in Figure FIGREF1 , where LSTMs performance decreases with the increase in dimensions of the input and context vectors  Moreover, the semantics of the input and context vectors are different, suggesting that each may benefit from specialized treatment 
Guided by these insights, we introduce a new recurrent unit, the Pyramidal Recurrent Unit (PRU), which is based on the LSTM gating structure  Figure FIGREF2 provides an overview of the PRU  At the heart of the PRU is the pyramidal transformation (PT), which uses subsampling to effect multiple views of the input vector  The subsampled representations are combined in a pyramidal fusion structure, resulting in richer interactions between the individual dimensions of the input vector than is possible with a linear transformation  Context vectors, which have already undergone this transformation in the previous cell, are modified with a grouped linear transformation (GLT) which allows the network to learn latent representations in high dimensional space with fewer parameters and better generalizability (see Figure FIGREF1 ) 
We show that PRUs can better model contextual information and demonstrate performance gains on the task of language modeling  The PRU improves the perplexity of the current state-of-the-art language model BIBREF0 by up to 1 3 points, reaching perplexities of 56 56 and 64 53 on the Penn Treebank and WikiText2 datasets while learning 15-20% fewer parameters  Replacing an LSTM with a PRU results in improvements in perplexity across a variety of experimental settings  We provide detailed ablations which motivate the design of the PRU architecture, as well as detailed analysis of the effect of the PRU on other components of the language model 
Related work
Multiple methods, including a variety of gating structures and transformations, have been proposed to improve the performance of recurrent neural networks (RNNs)  We first describe these approaches and then provide an overview of recent work in language modeling 
Pyramidal Recurrent Units
We introduce Pyramidal Recurrent Units (PRUs), a new RNN architecture which improves modeling of context by allowing for higher dimensional vector representations while learning fewer parameters  Figure FIGREF2 provides an overview of PRU  We first elaborate on the details of the pyramidal transformation and the grouped linear transformation  We then describe our recurrent unit, PRU 
Pyramidal transformation for input
The basic transformation in many recurrent units is a linear transformation INLINEFORM0 defined as: DISPLAYFORM0
where INLINEFORM0 are learned weights that linearly map INLINEFORM1 to INLINEFORM2   To simplify notation, we omit the biases 
Motivated by successful applications of sub-sampling in computer vision (e g , BIBREF22 , BIBREF23 , BIBREF9 , BIBREF24 ), we subsample input vector INLINEFORM0 into INLINEFORM1 pyramidal levels to achieve representation of the input vector at multiple scales  This sub-sampling operation produces INLINEFORM2 vectors, represented as INLINEFORM3 , where INLINEFORM4 is the sampling rate and INLINEFORM5   We learn scale-specific transformations INLINEFORM6 for each INLINEFORM7   The transformed subsamples are concatenated to produce the pyramidal analog to INLINEFORM8 , here denoted as INLINEFORM9 : DISPLAYFORM0
where INLINEFORM0 indicates concatenation  We note that pyramidal transformation with INLINEFORM1 is the same as the linear transformation 
To improve gradient flow inside the recurrent unit, we combine the input and output using an element-wise sum (when dimension matches) to produce residual analog of pyramidal transformation, as shown in Figure FIGREF2 BIBREF25  
We sub-sample the input vector INLINEFORM0 into INLINEFORM1 pyramidal levels using the kernel-based approach BIBREF8 , BIBREF9   Let us assume that we have a kernel INLINEFORM2 with INLINEFORM3 elements  Then, the input vector INLINEFORM4 can be sub-sampled as: DISPLAYFORM0
where INLINEFORM0 represents the stride and INLINEFORM1  
The number of parameters learned by the linear transformation and the pyramidal transformation with INLINEFORM0 pyramidal levels to map INLINEFORM1 to INLINEFORM2 are INLINEFORM3 and INLINEFORM4 respectively  Thus, pyramidal transformation reduces the parameters of a linear transformation by a factor of INLINEFORM5   For example, the pyramidal transformation (with INLINEFORM6 and INLINEFORM7 ) learns INLINEFORM8 fewer parameters than the linear transformation 
Grouped linear transformation for context
Many RNN architectures apply linear transformations to both the input and context vector  However, this may not be ideal due to the differing semantics of each vector  In many NLP applications including language modeling, the input vector is a dense word embedding which is shared across all contexts for a given word in a dataset  In contrast, the context vector is highly contextualized by the current sequence  The differences between the input and context vector motivate their separate treatment in the PRU architecture 
The weights learned using the linear transformation (Eq  EQREF9 ) are reused over multiple time steps, which makes them prone to over-fitting BIBREF26   To combat over-fitting, various methods, such as variational dropout BIBREF26 and weight dropout BIBREF0 , have been proposed to regularize these recurrent connections  To further improve generalization abilities while simultaneously enabling the recurrent unit to learn representations at very high dimensional space, we propose to use grouped linear transformation (GLT) instead of standard linear transformation for recurrent connections BIBREF27   While pyramidal and linear transformations can be applied to transform context vectors, our experimental results in Section SECREF39 suggests that GLTs are more effective 
The linear transformation INLINEFORM0 maps INLINEFORM1 linearly to INLINEFORM2   Grouped linear transformations break the linear interactions by factoring the linear transformation into two steps  First, a GLT splits the input vector INLINEFORM3 into INLINEFORM4 smaller groups such that INLINEFORM5   Second, a linear transformation INLINEFORM6 is applied to map INLINEFORM7 linearly to INLINEFORM8 , for each INLINEFORM9   The INLINEFORM10 resultant output vectors INLINEFORM11 are concatenated to produce the final output vector INLINEFORM12   DISPLAYFORM0
GLTs learn representations at low dimensionality  Therefore, a GLT requires INLINEFORM0 fewer parameters than the linear transformation  We note that GLTs are subset of linear transformations  In a linear transformation, each neuron receives an input from each element in the input vector while in a GLT, each neuron receives an input from a subset of the input vector  Therefore, GLT is the same as a linear transformation when INLINEFORM1  
Pyramidal Recurrent Unit
We extend the basic gating architecture of LSTM with the pyramidal and grouped linear transformations outlined above to produce the Pyramidal Recurrent Unit (PRU), whose improved sequence modeling capacity is evidenced in Section SECREF4  
At time INLINEFORM0 , the PRU combines the input vector INLINEFORM1 and the previous context vector (or previous hidden state vector) INLINEFORM2 using the following transformation function as: DISPLAYFORM0
where INLINEFORM0 indexes the various gates in the LSTM model, and INLINEFORM1 and INLINEFORM2 represents the pyramidal and grouped linear transformations defined in Eqns  EQREF10 and EQREF15 , respectively 
We will now incorporate INLINEFORM0 into LSTM gating architecture to produce PRU  At time INLINEFORM1 , a PRU cell takes INLINEFORM2 , INLINEFORM3 , and INLINEFORM4 as inputs to produce forget INLINEFORM5 , input INLINEFORM6 , output INLINEFORM7 , and content INLINEFORM8 gate signals  The inputs are combined with these gate signals to produce context vector INLINEFORM9 and cell state INLINEFORM10   Mathematically, the PRU with the LSTM gating architecture can be defined as: DISPLAYFORM0
where INLINEFORM0 represents the element-wise multiplication operation, and INLINEFORM1 and INLINEFORM2 are the sigmoid and hyperbolic tangent activation functions  We note that LSTM is a special case of PRU when INLINEFORM3 = INLINEFORM4 =1 
Experiments
To showcase the effectiveness of the PRU, we evaluate the performance on two standard datasets for word-level language modeling and compare with state-of-the-art methods  Additionally, we provide a detailed examination of the PRU and its behavior on the language modeling tasks 
Set-up
Following recent works, we compare on two widely used datasets, the Penn Treebank (PTB) BIBREF28 as prepared by BIBREF29 and WikiText2 (WT-2) BIBREF20   For both datasets, we follow the same training, validation, and test splits as in BIBREF0  
We extend the language model, AWD-LSTM BIBREF0 , by replacing LSTM layers with PRU  Our model uses 3-layers of PRU with an embedding size of 400  The number of parameters learned by state-of-the-art methods vary from 18M to 66M with majority of the methods learning about 22M to 24M parameters on the PTB dataset  For a fair comparison with state-of-the-art methods, we fix the model size to 19M and vary the value of INLINEFORM0 and hidden layer sizes so that total number of learned parameters is similar across different configurations  We use 1000, 1200, and 1400 as hidden layer sizes for values of INLINEFORM1 =1,2, and 4, respectively  We use the same settings for the WT-2 dataset  We set the number of pyramidal levels INLINEFORM2 to two in our experiments and use average pooling for sub-sampling  These values are selected based on our ablation experiments on the validation set (Section SECREF39 )  We measure the performance of our models in terms of word-level perplexity  We follow the same training strategy as in BIBREF0  
To understand the effect of regularization methods on the performance of PRUs, we perform experiments under two different settings: (1) Standard dropout: We use a standard dropout BIBREF12 with probability of 0 5 after embedding layer, the output between LSTM layers, and the output of final LSTM layer  (2) Advanced dropout: We use the same dropout techniques with the same dropout values as in BIBREF0   We call this model as AWD-PRU 
Results
Table TABREF23 compares the performance of the PRU with state-of-the-art methods  We can see that the PRU achieves the best performance with fewer parameters 
PRUs achieve either the same or better performance than LSTMs  In particular, the performance of PRUs improves with the increasing value of INLINEFORM0   At INLINEFORM1 , PRUs outperform LSTMs by about 4 points on the PTB dataset and by about 3 points on the WT-2 dataset  This is explained in part by the regularization effect of the grouped linear transformation (Figure FIGREF1 )  With grouped linear and pyramidal transformations, PRUs learn rich representations at very high dimensional space while learning fewer parameters  On the other hand, LSTMs overfit to the training data at such high dimensions and learn INLINEFORM2 to INLINEFORM3 more parameters than PRUs 
With the advanced dropouts, the performance of PRUs improves by about 4 points on the PTB dataset and 7 points on the WT-2 dataset  This further improves with finetuning on the PTB (about 2 points) and WT-2 (about 1 point) datasets 
For similar number of parameters, the PRU with standard dropout outperforms most of the state-of-the-art methods by large margin on the PTB dataset (e g  RAN BIBREF7 by 16 points with 4M less parameters, QRNN BIBREF33 by 16 points with 1M more parameters, and NAS BIBREF31 by 1 58 points with 6M less parameters)  With advanced dropouts, the PRU delivers the best performance  On both datasets, the PRU improves the perplexity by about 1 point while learning 15-20% fewer parameters 
PRU is a drop-in replacement for LSTM, therefore, it can improve language models with modern inference techniques such as dynamic evaluation BIBREF21   When we evaluate PRU-based language models (only with standard dropout) with dynamic evaluation on the PTB test set, the perplexity of PRU ( INLINEFORM0 ) improves from 62 42 to 55 23 while the perplexity of an LSTM ( INLINEFORM1 ) with similar settings improves from 66 29 to 58 79; suggesting that modern inference techniques are equally applicable to PRU-based language models 
Analysis
It is shown above that the PRU can learn representations at higher dimensionality with more generalization power, resulting in performance gains for language modeling  A closer analysis of the impact of the PRU in a language modeling system reveals several factors that help explain how the PRU achieves these gains 
As exemplified in Table TABREF34 , the PRU tends toward more confident decisions, placing more of the probability mass on the top next-word prediction than the LSTM  To quantify this effect, we calculate the entropy of the next-token distribution for both the PRU and the LSTM using 3687 contexts from the PTB validation set  Figure FIGREF32 shows a histogram of the entropies of the distribution, where bins of size 0 23 are used to effect categories  We see that the PRU more often produces lower entropy distributions corresponding to higher confidences for next-token choices  This is evidenced by the mass of the red PRU curve lying in the lower entropy ranges compared to the blue LSTM's curve  The PRU can produce confident decisions in part because more information is encoded in the higher dimensional context vectors 
The PRU has the ability to model individual words at different resolutions through the pyramidal transform; which provides multiple paths for the gradient to the embedding layer (similar to multi-task learning) and improves the flow of information  When considering the embeddings by part of speech, we find that the pyramid level 1 embeddings exhibit higher variance than the LSTM across all POS categories (Figure FIGREF33 ), and that pyramid level 2 embeddings show extremely low variance  We hypothesize that the LSTM must encode both coarse group similarities and individual word differences into the same vector space, reducing the space between individual words of the same category  The PRU can rely on the subsampled embeddings to account for coarse-grained group similarities, allowing for finer individual word distinctions in the embedding layer  This hypothesis is strengthened by the entropy results described above: a model which can make finer distinctions between individual words can more confidently assign probability mass  A model that cannot make these distinctions, such as the LSTM, must spread its probability mass across a larger class of similar words 
Saliency analysis using gradients help identify relevant words in a test sequence that contribute to the prediction BIBREF34 , BIBREF35 , BIBREF36   These approaches compute the relevance as the squared norm of the gradients obtained through back-propagation  Table TABREF34 visualizes the heatmaps for different sequences  PRUs, in general, give more relevance to contextual words than LSTMs, such as southeast (sample 1), cost (sample 2), face (sample 4), and introduced (sample 5), which help in making more confident decisions  Furthermore, when gradients during back-propagation are visualized BIBREF37 (Table TABREF34 ), we find that PRUs have better gradient coverage than LSTMs, suggesting PRUs use more features than LSTMs that contributes to the decision  This also suggests that PRUs update more parameters at each iteration which results in faster training  Language model in BIBREF0 takes 500 and 750 epochs to converge with PRU and LSTM as a recurrent unit, respectively 
Ablation studies
In this section, we provide a systematic analysis of our design choices  Our training methodology is the same as described in Section SECREF19 with the standard dropouts  For a thorough understanding of our design choices, we use a language model with a single layer of PRU and fix the size of embedding and hidden layers to 600  The word-level perplexities are reported on the validation sets of the PTB and the WT-2 datasets 
The two hyper-parameters that control the trade-off between performance and number of parameters in PRUs are the number of pyramidal levels INLINEFORM0 and groups INLINEFORM1   Figure FIGREF35 provides a trade-off between perplexity and recurrent unit (RU) parameters 
Variable INLINEFORM0 and fixed INLINEFORM1 : When we increase the number of pyramidal levels INLINEFORM2 at a fixed value of INLINEFORM3 , the performance of the PRU drops by about 1 to 4 points while reducing the total number of recurrent unit parameters by up to 15%  We note that the PRU with INLINEFORM4 at INLINEFORM5 delivers similar performance as the LSTM while learning about 15% fewer recurrent unit parameters 
Fixed INLINEFORM0 and variable INLINEFORM1 : When we vary the value of INLINEFORM2 at fixed number of pyramidal levels INLINEFORM3 , the total number of recurrent unit parameters decreases significantly with a minimal impact on the perplexity  For example, PRUs with INLINEFORM4 and INLINEFORM5 learns 77% fewer recurrent unit parameters while its perplexity (lower is better) increases by about 12% in comparison to LSTMs  Moreover, the decrease in number of parameters at higher value of INLINEFORM6 enables PRUs to learn the representations in high dimensional space with better generalizability (Table TABREF23 ) 
Table TABREF43 shows the impact of different transformations of the input vector INLINEFORM0 and the context vector INLINEFORM1   We make following observations: (1) Using the pyramidal transformation for the input vectors improves the perplexity by about 1 point on both the PTB and WT-2 datasets while reducing the number of recurrent unit parameters by about 14% (see R1 and R4)  We note that the performance of the PRU drops by up to 1 point when residual connections are not used (R4 and R6)  (2) Using the grouped linear transformation for context vectors reduces the total number of recurrent unit parameters by about 75% while the performance drops by about 11% (see R3 and R4)  When we use the pyramidal transformation instead of the linear transformation, the performance drops by up to 2% while there is no significant drop in the number of parameters (R4 and R5) 
We set sub-sampling kernel INLINEFORM0 (Eq  EQREF12 ) with stride INLINEFORM1 and size of 3 ( INLINEFORM2 ) in four different ways: (1) Skip: We skip every other element in the input vector  (2) Convolution: We initialize the elements of INLINEFORM3 randomly from normal distribution and learn them during training the model  We limit the output values between -1 and 1 using INLINEFORM4 activation function to make training stable  (3) Avg  pool: We initialize the elements of INLINEFORM5 to INLINEFORM6   (4) Max pool: We select the maximum value in the kernel window INLINEFORM7  
Table TABREF45 compares the performance of the PRU with different sampling methods  Average pooling performs the best while skipping give comparable performance  Both of these methods enable the network to learn richer word representations while representing the input vector in different forms, thus delivering higher performance  Surprisingly, a convolution-based sub-sampling method does not perform as well as the averaging method  The INLINEFORM0 function used after convolution limits the range of output values which are further limited by the LSTM gating structure, thereby impeding in the flow of information inside the cell  Max pooling forces the network to learn representations from high magnitude elements, thus distinguishing features between elements vanishes, resulting in poor performance 
Conclusion
We introduce the Pyramidal Recurrent Unit, which better model contextual information by admitting higher dimensional representations with good generalizability  When applied to the task of language modeling, PRUs improve perplexity across several settings, including recent state-of-the-art systems  Our analysis shows that the PRU improves the flow of gradient and expand the word embedding subspace, resulting in more confident decisions  Here we have shown improvements for language modeling  In future, we plan to study the performance of PRUs on different tasks, including machine translation and question answering  In addition, we will study the performance of the PRU on language modeling with more recent inference techniques, such as dynamic evaluation and mixture of softmax 
Acknowledgments
This research was supported by NSF (IIS 1616112, III 1703166), Allen Distinguished Investigator Award, and gifts from Allen Institute for AI, Google, Amazon, and Bloomberg  We are grateful to Aaron Jaech, Hannah Rashkin, Mandar Joshi, Aniruddha Kembhavi, and anonymous reviewers for their helpful comments 
Context 3:  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: Variational LSTM, CharCNN, Pointer Sentinel-LSTM, RHN, NAS Cell, SRU, QRNN, RAN, 4-layer skip-connection LSTM, AWD-LSTM, Quantized LSTM

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:  The word error rates using four-gram language models were 9 2% and 17 3% respectively on Switchboard and CallHome portions of Eval2000 dataset 
Experiments ::: 20 newsgroups
20 newsgroups data set is one of the frequently used datasets in the text processing community for text classification and text clustering  This data set contains approximately 20,000 English documents from 20 topics to be identified, with 11314 documents for training and 7532 for testing  In this work, we used only 90% of documents for training and the remaining 10% for validation  For fair comparison with other publications, we used 53160 words vocabulary set available in the datasets website 
Experiments ::: Fisher
Fisher Phase 1 US English corpus is often used for automatic speech recognition in speech community  In this work, we used it for topic identification as in BIBREF3  The documents are 10-minute long telephone conversations between two people discussing a given topic  We used same training and test splits as BIBREF3 in which 1374 and 1372 documents are used for training and testing respectively  For validation of our model, we used 10% of training dataset and the remaining 90% was used for actual model training  The number of topics in this data set is 40 
Experiments ::: Dataset Statistics
Table TABREF22 shows statistics of our datasets  It can be observed that average length of Fisher is much higher than 20 newsgroups and CSAT  Cumulative distribution of document lengths for each dataset is shown in Fig  FIGREF21  It can be observed that almost all of the documents in Fisher dataset have length more than 1000 words  For CSAT, more than 50% of the documents have length greater than 500 and for 20newsgroups only 10% of the documents have length greater than 500  Note that, for CSAT and 20newsgroups, there are few documents with length more than 5000 
Experiments ::: Architecture and Training Details
In this work, we split document into segments of 200 tokens with a shift of 50 tokens to extract features from BERT model  For RoBERT, LSTM model is trained to minimize cross-entropy loss with Adam optimizer BIBREF19  The initial learning rate is set to $0 001$ and is reduced by a factor of $0 95$ if validation loss does not decrease for 3-epochs  For ToBERT, the Transformer is trained with the default BERT version of Adam optimizer BIBREF1 with an initial learning rate of $5e$-5  We report accuracy in all of our experiments  We chose a model with the best validation accuracy to calculate accuracy on the test set  To accomodate for non-determinism of some TensorFlow GPU operations, we report accuracy averaged over 5 runs 
Results
Table TABREF25 presents results using pre-trained BERT features  We extracted features from the pooled output of final transformer block as these were shown to be working well for most of the tasks BIBREF1  The features extracted from a pre-trained BERT model without any fine-tuning lead to a sub-par performance  However, We also notice that ToBERT model exploited the pre-trained BERT features better than RoBERT  It also converged faster than RoBERT  Table TABREF26 shows results using features extracted after fine-tuning BERT model with our datasets  Significant improvements can be observed compared to using pre-trained BERT features  Also, it can be noticed that ToBERT outperforms RoBERT on Fisher and 20newsgroups dataset by 13 63% and 0 81% respectively  On CSAT, ToBERT performs slightly worse than RoBERT but it is not statistically significant as this dataset is small 
Table TABREF27 presents results using fine-tuned BERT predictions instead of the pooled output from final transformer block  For each document, having obtained segment-wise predictions we can obtain final prediction for the whole document in three ways:
Compute the average of all segment-wise predictions and find the most probable class;
Find the most frequently predicted class;
Train a classification model 
It can be observed from Table TABREF27 that a simple averaging operation or taking most frequent predicted class works competitively for CSAT and 20newsgroups but not for the Fisher dataset  We believe the improvements from using RoBERT or ToBERT, compared to simple averaging or most frequent operations, are proportional to the fraction of long documents in the dataset  CSAT and 20newsgroups have (on average) significantly shorter documents than Fisher, as seen in Fig  FIGREF21  Also, significant improvements for Fisher could be because of less confident predictions from BERT model as this dataset has 40 classes  Fig  FIGREF31 presents the comparison of average voting and ToBERT for various document length ranges for Fisher dataset  We used fine-tuned BERT segment-level predictions (P) for this analysis  It can be observed that ToBERT outperforms average voting in every interval  To the best of our knowledge, this is a state-of-the-art result reported on the Fisher dataset 
Table TABREF32 presents the effect of position embeddings on the model performance  It can be observed that position embeddings did not significantly affect the model performance for Fisher and 20newsgroups, but they helped slightly in CSAT prediction (an absolute improvement of 0 64% F1-score)  We think that this is explained by the fact that Fisher and 20newsgroups are topic identification tasks, and the topic does not change much throughout these documents  However, CSAT may vary during the call, and in some cases a naive assumption that the sequential nature of the transcripts is irrelevant may lead to wrong conclusions 
Table TABREF33 compares our results with previous works  It can be seen that our model ToBERT outperforms CNN based experiments by significant margin on CSAT and Fisher datasets  For CSAT dataset, we used multi-scale CNN (MS-CNN) as the baseline, given its strong results on Fisher and 20newsgroups  The setup was replicated from BIBREF5 for comparison  We also see that our result on 20 newsgroups is 0 6% worse than the state-of-the-art 
Conclusions
In this paper, we presented two methods for long documents using BERT model: RoBERT and ToBERT  We evaluated our experiments on two classification tasks - customer satisfaction prediction and topic identification - using 3 datasets: CSAT, 20newsgroups and Fisher  We observed that ToBERT outperforms RoBERT on pre-trained BERT features and fine-tuned BERT features for all our tasks  Also, we noticed that fine-tuned BERT performs better than pre-trained BERT  We have shown that both RoBERT and ToBERT improved the simple baselines of taking an average (or the most frequent) of segment-wise predictions for long documents to obtain final prediction  Position embeddings did not significantly affect our models performance, but slightly improved the accuracy on the CSAT task  We obtained the best results on Fisher dataset and good improvements for CSAT task compared to the CNN baseline  It is interesting to note that the longer the average input in a given task, the bigger improvement we observe w r t  the baseline for that task  Our results confirm that both RoBERT and ToBERT can be used for long sequences with competitive performance and quick fine-tuning procedure  For future work, we shall focus on training models on long documents directly (i e  in an end-to-end manner) 

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.