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. By how much does their model outperform the state of the art results?
Context 1: Introduction
Recently, deep learning algorithms have successfully addressed problems in various fields, such as image classification, machine translation, speech recognition, text-to-speech generation and other machine learning related areas BIBREF0 , BIBREF1 , BIBREF2   Similarly, substantial improvements in performance have been obtained when deep learning algorithms have been applied to statistical speech processing BIBREF3   These fundamental improvements have led researchers to investigate additional topics related to human nature, which have long been objects of study  One such topic involves understanding human emotions and reflecting it through machine intelligence, such as emotional dialogue models BIBREF4 , BIBREF5  
In developing emotionally aware intelligence, the very first step is building robust emotion classifiers that display good performance regardless of the application; this outcome is considered to be one of the fundamental research goals in affective computing BIBREF6   In particular, the speech emotion recognition task is one of the most important problems in the field of paralinguistics  This field has recently broadened its applications, as it is a crucial factor in optimal human-computer interactions, including dialog systems  The goal of speech emotion recognition is to predict the emotional content of speech and to classify speech according to one of several labels (i e , happy, sad, neutral, and angry)  Various types of deep learning methods have been applied to increase the performance of emotion classifiers; however, this task is still considered to be challenging for several reasons  First, insufficient data for training complex neural network-based models are available, due to the costs associated with human involvement  Second, the characteristics of emotions must be learned from low-level speech signals  Feature-based models display limited skills when applied to this problem 
To overcome these limitations, we propose a model that uses high-level text transcription, as well as low-level audio signals, to utilize the information contained within low-resource datasets to a greater degree  Given recent improvements in automatic speech recognition (ASR) technology BIBREF7 , BIBREF2 , BIBREF8 , BIBREF9 , speech transcription can be carried out using audio signals with considerable skill  The emotional content of speech is clearly indicated by the emotion words contained in a sentence BIBREF10 , such as “lovely” and “awesome,” which carry strong emotions compared to generic (non-emotion) words, such as “person” and “day ” Thus, we hypothesize that the speech emotion recognition model will be benefit from the incorporation of high-level textual input 
In this paper, we propose a novel deep dual recurrent encoder model that simultaneously utilizes audio and text data in recognizing emotions from speech  Extensive experiments are conducted to investigate the efficacy and properties of the proposed model  Our proposed model outperforms previous state-of-the-art methods by 68 8% to 71 8% when applied to the IEMOCAP dataset, which is one of the most well-studied datasets  Based on an error analysis of the models, we show that our proposed model accurately identifies emotion classes  Moreover, the neutral class misclassification bias frequently exhibited by previous models, which focus on audio features, is less pronounced in our model 
Related work
Classical machine learning algorithms, such as hidden Markov models (HMMs), support vector machines (SVMs), and decision tree-based methods, have been employed in speech emotion recognition problems BIBREF11 , BIBREF12 , BIBREF13   Recently, researchers have proposed various neural network-based architectures to improve the performance of speech emotion recognition  An initial study utilized deep neural networks (DNNs) to extract high-level features from raw audio data and demonstrated its effectiveness in speech emotion recognition BIBREF14   With the advancement of deep learning methods, more complex neural-based architectures have been proposed  Convolutional neural network (CNN)-based models have been trained on information derived from raw audio signals using spectrograms or audio features such as Mel-frequency cepstral coefficients (MFCCs) and low-level descriptors (LLDs) BIBREF15 , BIBREF16 , BIBREF17   These neural network-based models are combined to produce higher-complexity models BIBREF18 , BIBREF19 , and these models achieved the best-recorded performance when applied to the IEMOCAP dataset 
Another line of research has focused on adopting variant machine learning techniques combined with neural network-based models  One researcher utilized the multiobject learning approach and used gender and naturalness as auxiliary tasks so that the neural network-based model learned more features from a given dataset BIBREF20   Another researcher investigated transfer learning methods, leveraging external data from related domains BIBREF21  
As emotional dialogue is composed of sound and spoken content, researchers have also investigated the combination of acoustic features and language information, built belief network-based methods of identifying emotional key phrases, and assessed the emotional salience of verbal cues from both phoneme sequences and words BIBREF22 , BIBREF23   However, none of these studies have utilized information from speech signals and text sequences simultaneously in an end-to-end learning neural network-based model to classify emotions 
Model
This section describes the methodologies that are applied to the speech emotion recognition task  We start by introducing the recurrent encoder model for the audio and text modalities individually  We then propose a multimodal approach that encodes both audio and textual information simultaneously via a dual recurrent encoder 
Audio Recurrent Encoder (ARE)
Motivated by the architecture used in BIBREF24 , BIBREF25 , we build an audio recurrent encoder (ARE) to predict the class of a given audio signal  Once MFCC features have been extracted from an audio signal, a subset of the sequential features is fed into the RNN (i e , gated recurrent units (GRUs)), which leads to the formation of the network's internal hidden state INLINEFORM0 to model the time series patterns  This internal hidden state is updated at each time step with the input data INLINEFORM1 and the hidden state of the previous time step INLINEFORM2 as follows: DISPLAYFORM0
where INLINEFORM0 is the RNN function with weight parameter INLINEFORM1 , INLINEFORM2 represents the hidden state at t- INLINEFORM3 time step, and INLINEFORM4 represents the t- INLINEFORM5 MFCC features in INLINEFORM6   After encoding the audio signal INLINEFORM7 with the RNN, the last hidden state of the RNN, INLINEFORM8 , is considered to be the representative vector that contains all of the sequential audio data  This vector is then concatenated with another prosodic feature vector, INLINEFORM9 , to generate a more informative vector representation of the signal, INLINEFORM10   The MFCC and the prosodic features are extracted from the audio signal using the openSMILE toolkit BIBREF26 , INLINEFORM11 , respectively  Finally, the emotion class is predicted by applying the softmax function to the vector INLINEFORM12   For a given audio sample INLINEFORM13 , we assume that INLINEFORM14 is the true label vector, which contains all zeros but contains a one at the correct class, and INLINEFORM15 is the predicted probability distribution from the softmax layer  The training objective then takes the following form: DISPLAYFORM0
where INLINEFORM0 is the calculated representative vector of the audio signal with dimensionality INLINEFORM1   The INLINEFORM2 and the bias INLINEFORM3 are learned model parameters  C is the total number of classes, and N is the total number of samples used in training  The upper part of Figure shows the architecture of the ARE model 
Text Recurrent Encoder (TRE)
We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7   We attempt to use the processed textual information as another modality in predicting the emotion class of a given signal  To use textual information, a speech transcript is tokenized and indexed into a sequence of tokens using the Natural Language Toolkit (NLTK) BIBREF27   Each token is then passed through a word-embedding layer that converts a word index to a corresponding 300-dimensional vector that contains additional contextual meaning between words  The sequence of embedded tokens is fed into a text recurrent encoder (TRE) in such a way that the audio MFCC features are encoded using the ARE represented by equation EQREF2   In this case, INLINEFORM0 is the t- INLINEFORM1 embedded token from the text input  Finally, the emotion class is predicted from the last hidden state of the text-RNN using the softmax function 
We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 is last hidden state of the text-RNN, INLINEFORM1 , and the INLINEFORM2 and bias INLINEFORM3 are learned model parameters  The lower part of Figure indicates the architecture of the TRE model 
Multimodal Dual Recurrent Encoder (MDRE)
We present a novel architecture called the multimodal dual recurrent encoder (MDRE) to overcome the limitations of existing approaches  In this study, we consider multiple modalities, such as MFCC features, prosodic features and transcripts, which contain sequential audio information, statistical audio information and textual information, respectively  These types of data are the same as those used in the ARE and TRE cases  The MDRE model employs two RNNs to encode data from the audio signal and textual inputs independently  The audio-RNN encodes MFCC features from the audio signal using equation EQREF2   The last hidden state of the audio-RNN is concatenated with the prosodic features to form the final vector representation INLINEFORM0 , and this vector is then passed through a fully connected neural network layer to form the audio encoding vector A  On the other hand, the text-RNN encodes the word sequence of the transcript using equation EQREF2   The final hidden states of the text-RNN are also passed through another fully connected neural network layer to form a textual encoding vector T  Finally, the emotion class is predicted by applying the softmax function to the concatenation of the vectors A and T  We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 is the feed-forward neural network with weight parameter INLINEFORM1 , and INLINEFORM2 , INLINEFORM3 are final encoding vectors from the audio-RNN and text-RNN, respectively  INLINEFORM4 and the bias INLINEFORM5 are learned model parameters 
Multimodal Dual Recurrent Encoder with Attention (MDREA)
Inspired by the concept of the attention mechanism used in neural machine translation BIBREF28 , we propose a novel multimodal attention method to focus on the specific parts of a transcript that contain strong emotional information, conditioning on the audio information  Figure shows the architecture of the MDREA model  First, the audio data and text data are encoded with the audio-RNN and text-RNN using equation EQREF2   We then consider the final audio encoding vector INLINEFORM0 as a context vector  As seen in equation EQREF9 , during each time step t, the dot product between the context vector e and the hidden state of the text-RNN at each t-th sequence INLINEFORM1 is evaluated to calculate a similarity score INLINEFORM2   Using this score INLINEFORM3 as a weight parameter, the weighted sum of the sequences of the hidden state of the text-RNN, INLINEFORM4 , is calculated to generate an attention-application vector Z  This attention-application vector is concatenated with the final encoding vector of the audio-RNN INLINEFORM5 (equation EQREF7 ), which will be passed through the softmax function to predict the emotion class  We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 and the bias INLINEFORM1 are learned model parameters 
Dataset
We evaluate our model using the Interactive Emotional Dyadic Motion Capture (IEMOCAP) BIBREF18 dataset  This dataset was collected following theatrical theory in order to simulate natural dyadic interactions between actors  We use categorical evaluations with majority agreement  We use only four emotional categories happy, sad, angry, and neutral to compare the performance of our model with other research using the same categories  The IEMOCAP dataset includes five sessions, and each session contains utterances from two speakers (one male and one female)  This data collection process resulted in 10 unique speakers  For consistent comparison with previous work, we merge the excitement dataset with the happiness dataset  The final dataset contains a total of 5531 utterances (1636 happy, 1084 sad, 1103 angry, 1708 neutral) 
Feature extraction
To extract speech information from audio signals, we use MFCC values, which are widely used in analyzing audio signals  The MFCC feature set contains a total of 39 features, which include 12 MFCC parameters (1-12) from the 26 Mel-frequency bands and log-energy parameters, 13 delta and 13 acceleration coefficients The frame size is set to 25 ms at a rate of 10 ms with the Hamming function  According to the length of each wave file, the sequential step of the MFCC features is varied  To extract additional information from the data, we also use prosodic features, which show effectiveness in affective computing  The prosodic features are composed of 35 features, which include the F0 frequency, the voicing probability, and the loudness contours  All of these MFCC and prosodic features are extracted from the data using the OpenSMILE toolkit BIBREF26  
Implementation details
Among the variants of the RNN function, we use GRUs as they yield comparable performance to that of the LSTM and include a smaller number of weight parameters BIBREF29   We use a max encoder step of 750 for the audio input, based on the implementation choices presented in BIBREF30 and 128 for the text input because it covers the maximum length of the transcripts  The vocabulary size of the dataset is 3,747, including the “_UNK_" token, which represents unknown words, and the “_PAD_" token, which is used to indicate padding information added while preparing mini-batch data  The number of hidden units and the number of layers in the RNN for each model (ARE, TRE, MDRE and MDREA) are selected based on extensive hyperparameter search experiments  The weights of the hidden units are initialized using orthogonal weights BIBREF31 ], and the text embedding layer is initialized from pretrained word-embedding vectors BIBREF32  
In preparing the textual dataset, we first use the released transcripts of the IEMOCAP dataset for simplicity  To investigate the practical performance, we then process all of the IEMOCAP audio data using an ASR system (the Google Cloud Speech API) and retrieve the transcripts  The performance of the Google ASR system is reflected by its word error rate (WER) of 5 53% 
Performance evaluation
As the dataset is not explicitly split beforehand into training, development, and testing sets, we perform 5-fold cross validation to determine the overall performance of the model  The data in each fold are split into training, development, and testing datasets (8:0 5:1 5, respectively)  After training the model, we measure the weighted average precision (WAP) over the 5-fold dataset  We train and evaluate the model 10 times per fold, and the model performance is assessed in terms of the mean score and standard deviation 
We examine the WAP values, which are shown in Table 1  First, our ARE model shows the baseline performance because we use minimal audio features, such as the MFCC and prosodic features with simple architectures  On the other hand, the TRE model shows higher performance gain compared to the ARE  From this result, we note that textual data are informative in emotion prediction tasks, and the recurrent encoder model is effective in understanding these types of sequential data  Second, the newly proposed model, MDRE, shows a substantial performance gain  It thus achieves the state-of-the-art performance with a WAP value of 0 718  This result shows that multimodal information is a key factor in affective computing  Lastly, the attention model, MDREA, also outperforms the best existing research results (WAP 0 690 to 0 688) BIBREF19   However, the MDREA model does not match the performance of the MDRE model, even though it utilizes a more complex architecture  We believe that this result arises because insufficient data are available to properly determine the complex model parameters in the MDREA model  Moreover, we presume that this model will show better performance when the audio signals are aligned with the textual sequence while applying the attention mechanism  We leave the implementation of this point as a future research direction 
To investigate the practical performance of the proposed models, we conduct further experiments with the ASR-processed transcript data (see “-ASR” models in Table )  The label accuracy of the processed transcripts is 5 53% WER  The TRE-ASR, MDRE-ASR and MDREA-ASR models reflect degraded performance compared to that of the TRE, MDRE and MDREA models  However, the performance of these models is still competitive; in particular, the MDRE-ASR model outperforms the previous best-performing model, 3CNN-LSTM10H (WAP 0 691 to 0 688) 
Error analysis
We analyze the predictions of the ARE, TRE, and MDRE models  Figure shows the confusion matrix of each model  The ARE model (Fig  ) incorrectly classifies most instances of happy as neutral (43 51%); thus, it shows reduced accuracy (35 15%) in predicting the the happy class  Overall, most of the emotion classes are frequently confused with the neutral class  This observation is in line with the findings of BIBREF30 , who noted that the neutral class is located in the center of the activation-valence space, complicating its discrimination from the other classes 
Interestingly, the TRE model (Fig  ) shows greater prediction gains in predicting the happy class when compared to the ARE model (35 15% to 75 73%)  This result seems plausible because the model can benefit from the differences among the distributions of words in happy and neutral expressions, which gives more emotional information to the model than that of the audio signal data  On the other hand, it is striking that the TRE model incorrectly predicts instances of the sad class as the happy class 16 20% of the time, even though these emotional states are opposites of one another 
The MDRE model (Fig  ) compensates for the weaknesses of the previous two models (ARE and TRE) and benefits from their strengths to a surprising degree  The values arranged along the diagonal axis show that all of the accuracies of the correctly predicted class have increased  Furthermore, the occurrence of the incorrect “sad-to-happy" cases in the TRE model is reduced from 16 20% to 9 15% 
Conclusions
In this paper, we propose a novel multimodal dual recurrent encoder model that simultaneously utilizes text data, as well as audio signals, to permit the better understanding of speech data  Our model encodes the information from audio and text sequences using dual RNNs and then combines the information from these sources using a feed-forward neural model to predict the emotion class  Extensive experiments show that our proposed model outperforms other state-of-the-art methods in classifying the four emotion categories, and accuracies ranging from 68 8% to 71 8% are obtained when the model is applied to the IEMOCAP dataset  In particular, it resolves the issue in which predictions frequently incorrectly yield the neutral class, as occurs in previous models that focus on audio features 
In the future work, we aim to extend the modalities to audio, text and video inputs  Furthermore, we plan to investigate the application of the attention mechanism to data derived from multiple modalities  This approach seems likely to uncover enhanced learning schemes that will increase performance in both speech emotion recognition and other multimodal classification tasks 
Acknowledgments
K  Jung is with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul, Korea  This work was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (No 10073144) 
Context 2: Introduction
Ancient Chinese is the writing language in ancient China  It is a treasure of Chinese culture which brings together the wisdom and ideas of the Chinese nation and chronicles the ancient cultural heritage of China  Learning ancient Chinese not only helps people to understand and inherit the wisdom of the ancients, but also promotes people to absorb and develop Chinese culture 
However, it is difficult for modern people to read ancient Chinese  Firstly, compared with modern Chinese, ancient Chinese is more concise and shorter  The grammatical order of modern Chinese is also quite different from that of ancient Chinese  Secondly, most modern Chinese words are double syllables, while the most of the ancient Chinese words are monosyllabic  Thirdly, there is more than one polysemous phenomenon in ancient Chinese  In addition, manual translation has a high cost  Therefore, it is meaningful and useful to study the automatic translation from ancient Chinese to modern Chinese  Through ancient-modern Chinese translation, the wisdom, talent and accumulated experience of the predecessors can be passed on to more people 
Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 has achieved remarkable performance on many bilingual translation tasks  It is an end-to-end learning approach for machine translation, with the potential to show great advantages over the statistic machine translation (SMT) systems  However, NMT approach has not been widely applied to the ancient-modern Chinese translation task  One of the main reasons is the limited high-quality parallel data resource 
The most popular method of acquiring translation examples is bilingual text alignment BIBREF5   This kind of method can be classified into two types: lexical-based and statistical-based  The lexical-based approaches BIBREF6 , BIBREF7 focus on lexical information, which utilize the bilingual dictionary BIBREF8 , BIBREF9 or lexical features  Meanwhile, the statistical-based approaches BIBREF10 , BIBREF11 rely on statistical information, such as sentence length ratio in two languages and align mode probability 
However, these methods are designed for other bilingual language pairs that are written in different language characters (e g  English-French, Chinese-Japanese)  The ancient-modern Chinese has some characteristics that are quite different from other language pairs  For example, ancient and modern Chinese are both written in Chinese characters, but ancient Chinese is highly concise and its syntactical structure is different from modern Chinese  The traditional methods do not take these characteristics into account  In this paper, we propose an effective ancient-modern Chinese text alignment method at the level of clause based on the characteristics of these two languages  The proposed method combines both lexical-based information and statistical-based information, which achieves 94 2 F1-score on Test set  Recently, a simple longest common subsequence based approach for ancient-modern Chinese sentence alignment is proposed in BIBREF12   Our experiments showed that our proposed alignment approach performs much better than their method 
We apply the proposed method to create a large translation parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  Furthermore, we test SMT models and various NMT models on the created dataset and provide a strong baseline for this task 
Overview
There are four steps to build the ancient-modern Chinese translation dataset: (i) The parallel corpus crawling and cleaning  (ii) The paragraph alignment  (iii) The clause alignment based on aligned paragraphs  (iv) Augmenting data by merging aligned adjacent clauses  The most critical step is the third step 
Clause Alignment
In the clause alignment step, we combine both statistical-based and lexical-based information to measure the score for each possible clause alignment between ancient and modern Chinese strings  The dynamic programming is employed to further find overall optimal alignment paragraph by paragraph  According to the characteristics of the ancient and modern Chinese languages, we consider the following factors to measure the alignment score INLINEFORM0 between a bilingual clause pair:
Lexical Matching  The lexical matching score is used to calculate the matching coverage of the ancient clause INLINEFORM0   It contains two parts: exact matching and dictionary matching  An ancient Chinese character usually corresponds to one or more modern Chinese words  In the first part, we carry out Chinese Word segmentation to the modern Chinese clause INLINEFORM1   Then we match the ancient characters and modern words in the order from left to right  In further matching, the words that have been matched will be deleted from the original clauses 
However, some ancient characters do not appear in its corresponding modern Chinese words  An ancient Chinese dictionary is employed to address this issue  We preprocess the ancient Chinese dictionary and remove the stop words  In this dictionary matching step, we retrieve the dictionary definition of each unmatched ancient character and use it to match the remaining modern Chinese words  To reduce the impact of universal word matching, we use Inverse Document Frequency (IDF) to weight the matching words  The lexical matching score is calculated as: DISPLAYFORM0
The above equation is used to calculate the matching coverage of the ancient clause INLINEFORM0   The first term of equation ( EQREF8 ) represents exact matching score  INLINEFORM1 denotes the length of INLINEFORM2 , INLINEFORM3 denotes each ancient character in INLINEFORM4 , and the indicator function INLINEFORM5 indicates whether the character INLINEFORM6 can match the words in the clause INLINEFORM7   The second term is dictionary matching score  Here INLINEFORM8 and INLINEFORM9 represent the remaining unmatched strings of INLINEFORM10 and INLINEFORM11 , respectively  INLINEFORM12 denotes the INLINEFORM13 -th character in the dictionary definition of the INLINEFORM14 and its IDF score is denoted as INLINEFORM15   The INLINEFORM16 is a predefined parameter which is used to normalize the IDF score  We tuned the value of this parameter on the Dev set 
Statistical Information  Similar to BIBREF11 and BIBREF6 , the statistical information contains alignment mode and length information  There are many alignment modes between ancient and modern Chinese languages  If one ancient Chinese clause aligns two adjacent modern Chinese clauses, we call this alignment as 1-2 alignment mode  We show some examples of different alignment modes in Figure FIGREF9   In this paper, we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes which account for INLINEFORM0 of the Dev set  We estimate the probability Pr INLINEFORM1 n-m INLINEFORM2 of each alignment mode n-m on the Dev set  To utilize length information, we make an investigation on length correlation between these two languages  Based on the assumption of BIBREF11 that each character in one language gives rise to a random number of characters in the other language and those random variables INLINEFORM3 are independent and identically distributed with a normal distribution, we estimate the mean INLINEFORM4 and standard deviation INLINEFORM5 from the paragraph aligned parallel corpus  Given a clause pair INLINEFORM6 , the statistical information score can be calculated by: DISPLAYFORM0
where INLINEFORM0 denotes the normal distribution probability density function 
Edit Distance  Because ancient and modern Chinese are both written in Chinese characters, we also consider using the edit distance  It is a way of quantifying the dissimilarity between two strings by counting the minimum number of operations (insertion, deletion, and substitution) required to transform one string into the other  Here we define the edit distance score as: DISPLAYFORM0
Dynamic Programming  The overall alignment score for each possible clause alignment is as follows: DISPLAYFORM0
Here INLINEFORM0 and INLINEFORM1 are pre-defined interpolation factors  We use dynamic programming to find the overall optimal alignment paragraph by paragraph  Let INLINEFORM2 be total alignment scores of aligning the first to INLINEFORM3 -th ancient Chinese clauses with the first to to INLINEFORM4 -th modern Chinese clauses, and the recurrence then can be described as follows: DISPLAYFORM0
Where INLINEFORM0 denotes concatenate clause INLINEFORM1 to clause INLINEFORM2   As we discussed above, here we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes 
Ancient-Modern Chinese Dataset
Data Collection  To build the large ancient-modern Chinese dataset, we collected 1 7K bilingual ancient-modern Chinese articles from the internet  More specifically, a large part of the ancient Chinese data we used come from ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era  They used plain and accurate words to express what happened at that time, and thus ensure the generality of the translated materials 
Paragraph Alignment  To further ensure the quality of the new dataset, the work of paragraph alignment is manually completed  After data cleaning and manual paragraph alignment, we obtained 35K aligned bilingual paragraphs 
Clause Alignment  We applied our clause alignment algorithm on the 35K aligned bilingual paragraphs and obtained 517K aligned bilingual clauses  The reason we use clause alignment algorithm instead of sentence alignment is because we can construct more aligned sentences more flexibly and conveniently  To be specific, we can get multiple additional sentence level bilingual pairs by “data augmentation” 
Data Augmentation  We augmented the data in the following way: Given an aligned clause pair, we merged its adjacent clause pairs as a new sample pair  For example, suppose we have three adjacent clause level bilingual pairs: ( INLINEFORM0 , INLINEFORM1 ), ( INLINEFORM2 , INLINEFORM3 ), and ( INLINEFORM4 , INLINEFORM5 )  We can get some additional sentence level bilingual pairs, such as: ( INLINEFORM6 , INLINEFORM7 ) and ( INLINEFORM8 , INLINEFORM9 )  Here INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 are adjacent clauses in the original paragraph, and INLINEFORM13 denotes concatenate clause INLINEFORM14 to clause INLINEFORM15   The advantage of using this data augmentation method is that compared with only using ( INLINEFORM16 , INLINEFORM17 ) as the training data, we can also use ( INLINEFORM18 , INLINEFORM19 ) and ( INLINEFORM20 , INLINEFORM21 ) as the training data, which can provide richer supervision information for the model and make the model learn the align information between the source language and the target language better  After the data augmentation, we filtered the sentences which are longer than 50 or contain more than four clause pairs 
Dataset Creation  Finally, we split the dataset into three sets: training (Train), development (Dev) and testing (Test)  Note that the unaugmented dataset contains 517K aligned bilingual clause pairs from 35K aligned bilingual paragraphs  To keep all the sentences in different sets come from different articles, we split the 35K aligned bilingual paragraphs into Train, Dev and Test sets following these ratios respectively: 80%, 10%, 10%  Before data augmentation, the unaugmented Train set contains INLINEFORM0 aligned bilingual clause pairs from 28K aligned bilingual paragraphs  Then we augmented the Train, Dev and Test sets respectively  Note that the augmented Train, Dev and Test sets also contain the unaugmented data  The statistical information of the three data sets is shown in Table TABREF17   We show some examples of data in Figure FIGREF14  
RNN-based NMT model
We first briefly introduce the RNN based Neural Machine Translation (RNN-based NMT) model  The RNN-based NMT with attention mechanism BIBREF0 has achieved remarkable performance on many translation tasks  It consists of encoder and decoder part 
We firstly introduce the encoder part  The input word sequence of source language are individually mapped into a INLINEFORM0 -dimensional vector space INLINEFORM1   Then a bi-directional RNN BIBREF15 with GRU BIBREF16 or LSTM BIBREF17 cell converts these vectors into a sequences of hidden states INLINEFORM2  
For the decoder part, another RNN is used to generate target sequence INLINEFORM0   The attention mechanism BIBREF0 , BIBREF18 is employed to allow the decoder to refer back to the hidden state sequence and focus on a particular segment  The INLINEFORM1 -th hidden state INLINEFORM2 of decoder part is calculated as: DISPLAYFORM0
Here g INLINEFORM0 is a linear combination of attended context vector c INLINEFORM1 and INLINEFORM2 is the word embedding of (i-1)-th target word: DISPLAYFORM0
The attended context vector c INLINEFORM0 is computed as a weighted sum of the hidden states of the encoder: DISPLAYFORM0
The probability distribution vector of the next word INLINEFORM0 is generated according to the following: DISPLAYFORM0
We take this model as the basic RNN-based NMT model in the following experiments 
Transformer-NMT
Recently, the Transformer model BIBREF4 has made remarkable progress in machine translation  This model contains a multi-head self-attention encoder and a multi-head self-attention decoder 
As proposed by BIBREF4 , an attention function maps a query and a set of key-value pairs to an output, where the queries INLINEFORM0 , keys INLINEFORM1 , and values INLINEFORM2 are all vectors  The input consists of queries and keys of dimension INLINEFORM3 , and values of dimension INLINEFORM4   The attention function is given by: DISPLAYFORM0
Multi-head attention mechanism projects queries, keys and values to INLINEFORM0 different representation subspaces and calculates corresponding attention  The attention function outputs are concatenated and projected again before giving the final output  Multi-head attention allows the model to attend to multiple features at different positions 
The encoder is composed of a stack of INLINEFORM0 identical layers  Each layer has two sub-layers: multi-head self-attention mechanism and position-wise fully connected feed-forward network  Similarly, the decoder is also composed of a stack of INLINEFORM1 identical layers  In addition to the two sub-layers in each encoder layer, the decoder contains a third sub-layer which performs multi-head attention over the output of the encoder stack (see more details in BIBREF4 ) 
Experiments
Our experiments revolve around the following questions: Q1: As we consider three factors for clause alignment, do all these factors help? How does our method compare with previous methods? Q2: How does the NMT and SMT models perform on this new dataset we build?
Clause Alignment Results (Q1)
In order to evaluate our clause alignment algorithm, we manually aligned bilingual clauses from 37 bilingual ancient-modern Chinese articles, and finally got 4K aligned bilingual clauses as the Test set and 2K clauses as the Dev set 
Metrics  We used F1-score and precision score as the evaluation metrics  Suppose that we get INLINEFORM0 bilingual clause pairs after running the algorithm on the Test set, and there are INLINEFORM1 bilingual clause pairs of these INLINEFORM2 pairs are in the ground truth of the Test set, the precision score is defined as INLINEFORM3 (the algorithm gives INLINEFORM4 outputs, INLINEFORM5 of which are correct)  And suppose that the ground truth of the Test set contains INLINEFORM6 bilingual clause pairs, the recall score is INLINEFORM7 (there are INLINEFORM8 ground truth samples, INLINEFORM9 of which are output by the algorithm), then the F1-score is INLINEFORM10  
Baselines  Since the related work BIBREF10 , BIBREF11 can be seen as the ablation cases of our method (only statistical score INLINEFORM0 with dynamic programming), we compared the full proposed method with its variants on the Test set for ablation study  In addition, we also compared our method with the longest common subsequence (LCS) based approach proposed by BIBREF12   To the best of our knowledge, BIBREF12 is the latest related work which are designed for Ancient-Modern Chinese alignment 
Hyper-parameters  For the proposed method, we estimated INLINEFORM0 and INLINEFORM1 on all aligned paragraphs  The probability Pr INLINEFORM2 n-m INLINEFORM3 of each alignment mode n-m was estimated on the Dev set  For the hyper-parameters INLINEFORM4 , INLINEFORM5 and INLINEFORM6 , the grid search was applied to tune them on the Dev set  In order to show the effect of hyper-parameters INLINEFORM7 , INLINEFORM8 , and INLINEFORM9 , we reported the results of various hyper-parameters on the Dev set in Table TABREF26   Based on the results of grid search on the Dev set, we set INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 in the following experiment  The Jieba Chinese text segmentation is employed for modern Chinese word segmentation 
Results  The results on the Test set are shown in Table TABREF28 , the abbreviation w/o means removing a particular part from the setting  From the results, we can see that the lexical matching score is the most important among these three factors, and statistical information score is more important than edit distance score  Moreover, the dictionary term in lexical matching score significantly improves the performance  From these results, we obtain the best setting that involves all these three factors  We used this setting for dataset creation  Furthermore, the proposed method performs much better than LCS BIBREF12  
Translation Results (Q2)
In this experiment, we analyzed and compared the performance of the SMT and various NMT models on our built dataset  To verify the effectiveness of our data augmented method  We trained the NMT and SMT models on both unaugmented dataset (including 0 46M training pairs) and augmented dataset, and test all the models on the same Test set which is augmented  The models to be tested and their configurations are as follows:
SMT  The state-of-art Moses toolkit BIBREF19 was used to train SMT model  We used KenLM BIBREF20 to train a 5-gram language model, and the GIZA++ toolkit to align the data 
RNN-based NMT  The basic RNN-based NMT model is based on BIBREF0 which is introduced above  Both the encoder and decoder used 2-layer RNN with 1024 LSTM cells, and the encoder is a bi-directional RNN  The batch size, threshold of element-wise gradient clipping and initial learning rate of Adam optimizer BIBREF21 were set to 128, 5 0 and 0 001  When trained the model on augmented dataset, we used 4-layer RNN  Several techniques were investigated to train the model, including layer-normalization BIBREF22 , RNN-dropout BIBREF23 , and learning rate decay BIBREF1   The hyper-parameters were chosen empirically and adjusted in the Dev set  Furthermore, we tested the basic NMT model with several techniques, such as target language reversal BIBREF24 (reversing the order of the words in all target sentences, but not source sentences), residual connection BIBREF25 and pre-trained word2vec BIBREF26   For word embedding pre-training, we collected an external ancient corpus which contains INLINEFORM0 134M tokens 
Transformer-NMT  We also trained the Transformer model BIBREF4 which is a strong baseline of NMT on both augmented and unaugmented parallel corpus  The training configuration of the Transformer model is shown in Table TABREF32   The hyper-parameters are set based on the settings in the paper BIBREF4 and the sizes of our training sets 
For the evaluation, we used the average of 1 to 4 gram BLEUs multiplied by a brevity penalty BIBREF27 which computed by multi-bleu perl in Moses as metrics  The results are reported in Table TABREF34   For RNN-based NMT, we can see that target language reversal, residual connection, and word2vec can further improve the performance of the basic RNN-based NMT model  However, we find that word2vec and reversal tricks seem no obvious improvement when trained the RNN-based NMT and Transformer models on augmented parallel corpus  For SMT, it performs better than NMT models when they were trained on the unaugmented dataset  Nevertheless, when trained on the augmented dataset, both the RNN-based NMT model and Transformer based NMT model outperform the SMT model  In addition, as with other translation tasks BIBREF4 , the Transformer also performs better than RNN-based NMT 
Because the Test set contains both augmented and unaugmented data, it is not surprising that the RNN-based NMT model and Transformer based NMT model trained on unaugmented data would perform poorly  In order to further verify the effect of data augmentation, we report the test results of the models on only unaugmented test data (including 48K test pairs) in Table TABREF35   From the results, it can be seen that the data augmentation can still improve the models 
Analysis
The generated samples of various models are shown in Figure FIGREF36   Besides BLEU scores, we analyze these examples from a human perspective and draw some conclusions  At the same time, we design different metrics and evaluate on the whole Test set to support our conclusions as follows:
On the one hand, we further compare the translation results from the perspective of people  We find that although the original meaning can be basically translated by SMT, its translation results are less smooth when compared with the other two NMT models (RNN-based NMT and Transformer)  For example, the translations of SMT are usually lack of auxiliary words, conjunctions and function words, which is not consistent with human translation habits  To further confirm this conclusion, the average length of the translation results of the three models are measured (RNN-based NMT:17 12, SMT:15 50, Transformer:16 78, Reference:16 47)  We can see that the average length of the SMT outputs is shortest, and the length gaps between the SMT outputs and the references are largest  Meanwhile, the average length of the sentences translated by Transformer is closest to the average length of references  These results indirectly verify our point of view, and show that the NMT models perform better than SMT in this task 
On the other hand, there still exists some problems to be solved  We observe that translating proper nouns and personal pronouns (such as names, place names and ancient-specific appellations) is very difficult for all of these models  For instance, the ancient Chinese appellation `Zhen' should be translated into `Wo' in modern Chinese  Unfortunately, we calculate the accurate rate of some special words (such as `Zhen',`Chen' and `Gua'), and find that this rate is very low (the accurate rate of translating `Zhen' are: RNN-based NMT:0 14, SMT:0 16, Transformer:0 05)  We will focus on this issue in the future 
Conclusion and Future Work
We propose an effective ancient-modern Chinese clause alignment method which achieves 94 2 F1-score on Test set  Based on it, we build a large scale parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  In addition, we test the performance of the SMT and various NMT models on our built dataset and provide a strong NMT baseline for this task which achieves 27 16 BLEU score (4-gram)  We further analyze the performance of the SMT and various NMT models and summarize some specific problems that machine translation models will encounter when translating ancient Chinese 
For the future work, firstly, we are going to expand the dataset using the proposed method continually  Secondly, we will focus on solving the problem of proper noun translation and improve the translation system according to the features of ancient Chinese translation  Finally, we plan to introduce some techniques of statistical translation into neural machine translation to improve the performance 
This work is supported by National Natural Science Fund for Distinguished Young Scholar (Grant No  61625204) and partially supported by the State Key Program of National Science Foundation of China (Grant Nos  61836006 and 61432014) 
Context 3: Introduction
Abusive language refers to any type of insult, vulgarity, or profanity that debases the target; it also can be anything that causes aggravation BIBREF0 , BIBREF1   Abusive language is often reframed as, but not limited to, offensive language BIBREF2 , cyberbullying BIBREF3 , othering language BIBREF4 , and hate speech BIBREF5  
Recently, an increasing number of users have been subjected to harassment, or have witnessed offensive behaviors online BIBREF6   Major social media companies (i e  Facebook, Twitter) have utilized multiple resources—artificial intelligence, human reviewers, user reporting processes, etc —in effort to censor offensive language, yet it seems nearly impossible to successfully resolve the issue BIBREF7 , BIBREF8  
The major reason of the failure in abusive language detection comes from its subjectivity and context-dependent characteristics BIBREF9   For instance, a message can be regarded as harmless on its own, but when taking previous threads into account it may be seen as abusive, and vice versa  This aspect makes detecting abusive language extremely laborious even for human annotators; therefore it is difficult to build a large and reliable dataset BIBREF10  
Previously, datasets openly available in abusive language detection research on Twitter ranged from 10K to 35K in size BIBREF9 , BIBREF11   This quantity is not sufficient to train the significant number of parameters in deep learning models  Due to this reason, these datasets have been mainly studied by traditional machine learning methods  Most recently, Founta et al  founta2018large introduced Hate and Abusive Speech on Twitter, a dataset containing 100K tweets with cross-validated labels  Although this corpus has great potential in training deep models with its significant size, there are no baseline reports to date 
This paper investigates the efficacy of different learning models in detecting abusive language  We compare accuracy using the most frequently studied machine learning classifiers as well as recent neural network models  Reliable baseline results are presented with the first comparative study on this dataset  Additionally, we demonstrate the effect of different features and variants, and describe the possibility for further improvements with the use of ensemble models 
Related Work
The research community introduced various approaches on abusive language detection  Razavi et al  razavi2010offensive applied Naïve Bayes, and Warner and Hirschberg warner2012detecting used Support Vector Machine (SVM), both with word-level features to classify offensive language  Xiang et al  xiang2012detecting generated topic distributions with Latent Dirichlet Allocation BIBREF12 , also using word-level features in order to classify offensive tweets 
More recently, distributed word representations and neural network models have been widely applied for abusive language detection  Djuric et al  djuric2015hate used the Continuous Bag Of Words model with paragraph2vec algorithm BIBREF13 to more accurately detect hate speech than that of the plain Bag Of Words models  Badjatiya et al  badjatiya2017deep implemented Gradient Boosted Decision Trees classifiers using word representations trained by deep learning models  Other researchers have investigated character-level representations and their effectiveness compared to word-level representations BIBREF14 , BIBREF15  
As traditional machine learning methods have relied on feature engineering, (i e  n-grams, POS tags, user information) BIBREF1 , researchers have proposed neural-based models with the advent of larger datasets  Convolutional Neural Networks and Recurrent Neural Networks have been applied to detect abusive language, and they have outperformed traditional machine learning classifiers such as Logistic Regression and SVM BIBREF15 , BIBREF16   However, there are no studies investigating the efficiency of neural models with large-scale datasets over 100K 
Methodology
This section illustrates our implementations on traditional machine learning classifiers and neural network based models in detail  Furthermore, we describe additional features and variant models investigated 
Traditional Machine Learning Models
We implement five feature engineering based machine learning classifiers that are most often used for abusive language detection  In data preprocessing, text sequences are converted into Bag Of Words (BOW) representations, and normalized with Term Frequency-Inverse Document Frequency (TF-IDF) values  We experiment with word-level features using n-grams ranging from 1 to 3, and character-level features from 3 to 8-grams  Each classifier is implemented with the following specifications:
Naïve Bayes (NB): Multinomial NB with additive smoothing constant 1
Logistic Regression (LR): Linear LR with L2 regularization constant 1 and limited-memory BFGS optimization
Support Vector Machine (SVM): Linear SVM with L2 regularization constant 1 and logistic loss function
Random Forests (RF): Averaging probabilistic predictions of 10 randomized decision trees
Gradient Boosted Trees (GBT): Tree boosting with learning rate 1 and logistic loss function
Neural Network based Models
Along with traditional machine learning approaches, we investigate neural network based models to evaluate their efficacy within a larger dataset  In particular, we explore Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variant models  A pre-trained GloVe BIBREF17 representation is used for word-level features 
CNN: We adopt Kim's kim2014convolutional implementation as the baseline  The word-level CNN models have 3 convolutional filters of different sizes [1,2,3] with ReLU activation, and a max-pooling layer  For the character-level CNN, we use 6 convolutional filters of various sizes [3,4,5,6,7,8], then add max-pooling layers followed by 1 fully-connected layer with a dimension of 1024 
Park and Fung park2017one proposed a HybridCNN model which outperformed both word-level and character-level CNNs in abusive language detection  In order to evaluate the HybridCNN for this dataset, we concatenate the output of max-pooled layers from word-level and character-level CNN, and feed this vector to a fully-connected layer in order to predict the output 
All three CNN models (word-level, character-level, and hybrid) use cross entropy with softmax as their loss function and Adam BIBREF18 as the optimizer 
RNN: We use bidirectional RNN BIBREF19 as the baseline, implementing a GRU BIBREF20 cell for each recurrent unit  From extensive parameter-search experiments, we chose 1 encoding layer with 50 dimensional hidden states and an input dropout probability of 0 3  The RNN models use cross entropy with sigmoid as their loss function and Adam as the optimizer 
For a possible improvement, we apply a self-matching attention mechanism on RNN baseline models BIBREF21 so that they may better understand the data by retrieving text sequences twice  We also investigate a recently introduced method, Latent Topic Clustering (LTC) BIBREF22   The LTC method extracts latent topic information from the hidden states of RNN, and uses it for additional information in classifying the text data 
Feature Extension
While manually analyzing the raw dataset, we noticed that looking at the tweet one has replied to or has quoted, provides significant contextual information  We call these, “context tweets"

ground_truth: the attention model, MDREA, also outperforms the best existing research results (WAP 0.690 to 0.688)

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

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

ground_truth: RNN-based NMT model, Transformer-NMT

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

Please answer the questions based on the text provided below. What additional features and context are proposed?
Context 4:  In the named-entity recognition experiment, the average CCR of crowdworkers was 98 6%, while the CCR of the automated systems ranged from 77 2% to 96 7%  For four of the automated systems, detecting the entity Trump was more difficult than the other entities (e g , spaCy 72 7% for the entity Trump vs  above 91% for the other entities)  An example of incorrect NER is shown in Figure FIGREF1 top  The difficulties the automated tools had in NER may be explained by the fact that the tools were not trained on tweets, except for TwitterNLP, which was not in active development when the data was created BIBREF1 
In the sentiment analysis experiments, we found that a tweet may contain multiple sentiments  The groundtruth labels contain 210 positive sentiments, 521 neutral sentiments, and 305 negative sentiments to the candidates  We measured the CCR, across all tweets, to be 31 7% for Rosette Text Analytics, 43 2% for Google Cloud, 44 2% for TensiStrength, and 74 7% for the crowdworkers  This means the difference between the performance of the tools and the crowdworkers is significant – more than 30 percent points 
Crowdworkers correctly identified 62% of the neutral, 85% of the positive, and 92% of the negative sentiments  Google Cloud correctly identified 88% of the neutral sentiments, but only 3% of the positive, and 19% of the negative sentiments  TensiStrength correctly identified 87 2% of the neutral sentiments, but 10 5% of the positive, and 8 1% of the negative sentiments  Rosette Text Analytics correctly identified 22 7% of neutral sentiments, 38 1% of negative sentiments and 40 9% of positive sentiments  The lowest and highest CCR pertains to tweets about Trump and Sanders for both Google Cloud and TensiStrength, Trump and Clinton for Rosette Text Analytics, and Clinton and Cruz for crowdworkers  An example of incorrect ELS analysis is shown in Figure FIGREF1 bottom 
Conclusions and Future Work
Our results show that existing NLP systems cannot accurately perform sentiment analysis of political tweets in the dataset we experimented with  Labeling by humans, even non-expert crowdworkers, yields accuracy results that are well above the results of existing automated NLP systems  In future work we will therefore use a crowdworker-labeled dataset to train a new machine-learning based NLP system for tweet analysis  We will ensure that the training data is balanced among classes  Our plan is to use state-of-the-art deep neural networks and compare their performance for entity-level sentiment analysis of political tweets 
Acknowledgments
Partial support of this work by the Hariri Institute for Computing and Computational Science & Engineering at Boston University (to L G ) and a Google Faculty Research Award (to M B  and L G ) is gratefully acknowledged  Additionally, we would like to thank Daniel Khashabi for his help in running the CogComp-NLP Python API and Mike Thelwal for his help with TensiStrength  We are also grateful to the Stanford NLP group for clarifying some of the questions we had with regards to the Stanford NER tool 

ground_truth: using tweets that one has replied or quoted to as contextual information

text sequences of context tweets