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. Which baselines are used for evaluation?
Context 1: Introduction
Headline generation is the process of creating a headline-style sentence given an input article  The research community has been regarding the task of headline generation as a summarization task BIBREF1, ignoring the fundamental differences between headlines and summaries  While summaries aim to contain most of the important information from the articles, headlines do not necessarily need to  Instead, a good headline needs to capture people's attention and serve as an irresistible invitation for users to read through the article  For example, the headline “$2 Billion Worth of Free Media for Trump”, which gives only an intriguing hint, is considered better than the summarization style headline “Measuring Trump’s Media Dominance” , as the former gets almost three times the readers as the latter  Generating headlines with many clicks is especially important in this digital age, because many of the revenues of journalism come from online advertisements and getting more user clicks means being more competitive in the market  However, most existing websites naively generate sensational headlines using only keywords or templates  Instead, this paper aims to learn a model that generates sensational headlines based on an input article without labeled data 
To generate sensational headlines, there are two main challenges  Firstly, there is a lack of sensationalism scorer to measure how sensational a headline is  Some researchers have tried to manually label headlines as clickbait or non-clickbait BIBREF2, BIBREF3  However, these human-annotated datasets are usually small and expensive to collect  To capture a large variety of sensationalization patterns, we need a cheap and easy way to collect a large number of sensational headlines  Thus, we propose a distant supervision strategy to collect a sensationalism dataset  We regard headlines receiving lots of comments as sensational samples and the headlines generated by a summarization model as non-sensational samples  Experimental results show that by distinguishing these two types of headlines, we can partially teach the model a sense of being sensational 
Secondly, after training a sensationalism scorer on our sensationalism dataset, a natural way to generate sensational headlines is to maximize the sensationalism score using reinforcement learning (RL)  However, the following shows an example of a RL model maximizing the sensationalism score by generating a very unnatural sentence, while its sensationalism scorer gave a very high score of 0 99996: UTF8gbsn 十个可穿戴产品的设计原则这消息消息可惜说明 Ten design principles for wearable devices, this message message pity introduction  This happens because the sensationalism scorer can make mistakes and RL can generate unnatural phrases which fools our sensationalism scorer  Thus, how to effectively leverage RL with noisy rewards remains an open problem  To deal with the noisy reward, we introduce Auto-tuned Reinforcement Learning (ARL)  Our model automatically tunes the ratio between MLE and RL based on how sensational the training headline is  In this way, we effectively take advantage of RL with a noisy reward to generate headlines that are both sensational and fluent 
The major contributions of this paper are as follows: 1) To the best of our knowledge, we propose the first-ever model that tackles the sensational headline generation task with reinforcement learning techniques  2) Without human-annotated data, we propose a distant supervision strategy to train a sensationalism scorer as a reward function 3) We propose a novel loss function, Auto-tuned Reinforcement Learning, to give dynamic weights to balance between MLE and RL  Our code will be released  
Sensationalism Scorer
To evaluate the sensationalism intensity score $\alpha _{\text{sen}}$ of a headline, we collect a sensationalism dataset and then train a sensationalism scorer  For the sensationalism dataset collection, we choose headlines with many comments from popular online websites as positive samples  For the negative samples, we propose to use the generated headlines from a sentence summarization model  Intuitively, the summarization model, which is trained to preserve the semantic meaning, will lose the sensationalization ability and thus the generated negative samples will be less sensational than the original one, similar to the obfuscation of style after back-translation BIBREF4  For example, an original headline like UTF8gbsn“一趟挣10万？铁总增开申通、顺丰专列" (One trip to earn 100 thousand? China Railway opens new Shentong and Shunfeng special lines) will become UTF8gbsn“中铁总将增开京广两列快递专列" (China Railway opens two special lines for express) from the baseline model, which loses the sensational phrases of UTF8gbsn“一趟挣10万？" (One trip to earn 100 thousand?)   We then train the sensationalism scorer by classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss $L_{\text{sen}}$  Firstly, 1-D convolution is used to extract word features from the input embeddings of a headline  This is followed by a ReLU activation layer and a max-pooling layer along the time dimension  All features from different channels are concatenated together and projected to the sensationalism score by adding another fully connected layer with sigmoid activation  Binary cross entropy is used to compute the loss $L_{\text{sen}}$ 
Sensationalism Scorer ::: Training Details and Dataset
For the CNN model, we choose filter sizes of 1, 3, and 5 respectively  Adam is used to optimize $L_{sen}$ with a learning rate of 0 0001  We set the embedding size as 300 and initialize it from qiu2018revisiting trained on the Weibo corpus with word and character features  We fix the embeddings during training  For dataset collection, we utilize the headlines collected in qin2018automatic, lin2019learning from Tencent News, one of the most popular Chinese news websites, as the positive samples  We follow the same data split as the original paper  As some of the links are not available any more, we get 170,754 training samples and 4,511 validation samples  For the negative training samples collection, we randomly select generated headlines from a pointer generator BIBREF0 model trained on LCSTS dataset BIBREF5 and create a balanced training corpus which includes 351,508 training samples and 9,022 validation samples  To evaluate our trained classifier, we construct a test set by randomly sampling 100 headlines from the test split of LCSTS dataset and the labels are obtained by 11 human annotators  Annotations show that 52% headlines are labeled as positive and 48% headlines as negative by majority voting (The detail on the annotation can be found in Section SECREF26) 
Sensationalism Scorer ::: Results and Discussion
Our classifier achieves 0 65 accuracy and 0 65 averaged F1 score on the test set while a random classifier would only achieve 0 50 accuracy and 0 50 averaged F1 score  This confirms that the predicted sensationalism score can partially capture the sensationalism of headlines  On the other hand, a more natural choice is to take headlines with few comments as negative examples  Thus, we train another baseline classifier on a crawled balanced sensationalism corpus of 84k headlines where the positive headlines have at least 28 comments and the negative headlines have less than 5 comments  However, the results on the test set show that the baseline classifier gets 60% accuracy, which is worse than the proposed classifier (which achieves 65%)  The reason could be that the balanced sensationalism corpus are sampled from different distributions from the test set and it is hard for the trained model to generalize  Therefore, we choose the proposed one as our sensationalism scorer  Therefore, our next challenge is to show that how to leverage this noisy sensationalism reward to generate sensational headlines 
Sensational Headline Generation
Our sensational headline generation model takes an article as input and output a sensational headline  The model consists of a Pointer-Gen headline generator and is trained by ARL  The diagram of ARL can be found in Figure FIGREF6 
We denote the input article as $x=\lbrace x_1,x_2,x_3,\cdots ,x_M\rbrace $, and the corresponding headline as $y^*=\lbrace y_1^*,y_2^*,y_3^*,\cdots ,y_T^*\rbrace $, where $M$ is the number of tokens in an article and $T$ is the number of tokens in a headline 
Sensational Headline Generation ::: Pointer-Gen Headline Generator
We choose Pointer Generator (Pointer-Gen) BIBREF0, a widely used summarization model, as our headline generator for its ability to copy words from the input article  It takes a news article as input and generates a headline  Firstly, the tokens of each article, $\lbrace x_1,x_2,x_3,\cdots ,x_M\rbrace $, are fed into the encoder one-by-one and the encoder generates a sequence of hidden states $h_i$  For each decoding step $t$, the decoder receives the embedding for each token of a headline $y_t$ as input and updates its hidden states $s_t$  An attention mechanism following luong2015effective is used:
where $v$, $W_h$, $W_s$, and $b_{attn}$ are the trainable parameters and $h_t^*$ is the context vector  $s_t$ and $h_t^*$ are then combined to give a probability distribution over the vocabulary through two linear layers:
where $V$, $b$, $V^{^{\prime }}$, and $b^{^{\prime }}$ are trainable parameters  We use a pointer generator network to enable our model to copy rare/unknown words from the input article, giving the following final word probability:
where $x^t$ is the embedding of the input word of the decoder, $w_{h^*}^T$, $w_s^T$, $w_x^T$, and $b_{ptr}$ are trainable parameters, and $\sigma $ is the sigmoid function 
Sensational Headline Generation ::: Training Methods
We first briefly introduce MLE and RL objective functions, and a naive way to mix these two by a hyper-parameter $\lambda $  Then we point out the challenge of training with noisy reward, and propose ARL to address this issue 
Sensational Headline Generation ::: Training Methods ::: MLE and RL
A headline generation model can be trained with MLE, RL or a combination of MLE and RL  MLE training is to minimize the negative log likelihood of the training headlines  We feed $y^*$ into the decoder word by word and maximize the likelihood of $y^*$  The loss function for MLE becomes
For RL training, we choose the REINFORCE algorithm BIBREF6  In the training phase, after encoding an article, a headline $y^s = \lbrace y_1^s, y_2^s, y_3^s, \cdots , y_T^s\rbrace $ is obtained by sampling from $P(w)$ from our generator, and then a reward of sensationalism or ROUGE(RG) is calculated 
We use the baseline reward $\hat{R_t}$ to reduce the variance of the reward, similar to ranzato2015sequence  To elaborate, a linear model is deployed to estimate the baseline reward $\hat{R_t}$ based on $t$-th state $o_t$ for each timestep $t$  The parameters of the linear model are trained by minimizing the mean square loss between $R$ and $\hat{R_t}$:
where $W_r$ and $b_r$ are trainable parameters  To maximize the expected reward, our loss function for RL becomes
A naive way to mix these two objective functions using a hyper-parameter $\lambda $ has been successfully incorporated in the summarization task BIBREF7  It includes the MLE training as a language model to mitigate the readability and quality issues in RL  The mixed loss function is shown as follows:
where $*$ is the reward type  Usually $\lambda $ is large, and paulus2017deep used 0 9984 
Sensational Headline Generation ::: Training Methods ::: Auto-tuned Reinforcement Learning
Applying the naive mixed training method using sensationalism score as the reward is not obvious/trivial in our task  The main reason is that our sensationalism reward is notably more noisy and more fragile than the ROUGE-L reward or abstractive reward used in the summarization task BIBREF7, BIBREF8  A higher ROUGE-L F1 reward in summarization indicates higher overlapping ratio between generation and true summary statistically, but our sensationalism reward is a learned score which is fragile to be fooled with unnatural samples 
To effectively train the model with RL under noisy sensationalism reward, our idea is to balance RL with MLE  However, we argue that the weighted ratio between MLE and RL should be sample-dependent, instead of being fixed for all training samples as in paulus2017deep, kryscinski2018improving  The reason is that, RL and MLE have inconsistent optimization objectives  When the training headline is non-sensational, MLE training will encourage our model to imitate the training headline (thus generating non-sensational headlines), which counteracts the effects of RL training to generate sensational headlines 
The sensationalism score is, therefore, used to give dynamic weight to MLE and RL  Our ARL loss function becomes:
If $\alpha _{\text{sen}}(y^*)$ is high, meaning the training headline is sensational, our loss function encourages our model to imitate the sample more using the MLE training  If $\alpha _{\text{sen}}(y^*)$ is low, our loss function replies on RL training to improve the sensationalism  Note that the weight $\alpha _{\text{sen}}(y^*)$ is different from our sensationalism reward $\alpha _{\text{sen}}(y^s)$ and we call the loss function Auto-tuned Reinforcement Learning, because the ratio between MLE and RL are well “tuned” towards different samples 
Sensational Headline Generation ::: Dataset
We use LCSTS BIBREF5 as our dataset to train the summarization model  The dataset is collected from the Chinese microblogging website Sina Weibo  It contains over 2 million Chinese short texts with corresponding headlines given by the author of each text  The dataset is split into 2,400,591 samples for training, 10,666 samples for validation and 725 samples for testing  We tokenize each sentence with Jieba and a vocabulary size of 50000 is saved 
Sensational Headline Generation ::: Baselines and Our Models
We experiment and compare with the following models 
Pointer-Gen is the baseline model trained by optimizing $L_\text{MLE}$ in Equation DISPLAY_FORM13 
Pointer-Gen+Pos is the baseline model by training Pointer-Gen only on positive examples whose sensationalism score is larger than 0 5
Pointer-Gen+Same-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0 1
Pointer-Gen+Pos-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0 5
Pointer-Gen+RL-ROUGE is the baseline model trained by optimizing $L_\text{RL-ROUGE}$ in Equation DISPLAY_FORM17, with ROUGE-L BIBREF9 as the reward 
Pointer-Gen+RL-SEN is the baseline model trained by optimizing $L_\text{RL-SEN}$ in Equation DISPLAY_FORM17, with $\alpha _\text{sen}$ as the reward 
Pointer-Gen+ARL-SEN is our model trained by optimizing $L_\text{ARL-SEN}$ in Equation DISPLAY_FORM19, with $\alpha _\text{sen}$ as the reward 
Test set is the headlines from the test set 
Note that we didn't compare to Pointer-Gen+ARL-ROUGE as it is actually Pointer-GEN  Recall that $\alpha _{\text{sen}}(y^*)$ in Equation DISPLAY_FORM19 measures how good (based on reward function) is $y^*$  Then the loss function for Pointer-Gen+ARL-ROUGE will be
We also tried text style transfer baseline BIBREF10, but the generated headlines were very poor (many unknown words and irrelevant) 
Sensational Headline Generation ::: Training Details
MLE training: An Adam optimizer is used with the learning rate of 0 0001 to optimize $L_{\text{MLE}}$  The batch size is set as 128 and a one-layer, bi-directional Long Short-Term Memory (bi-LSTM) model with 512 hidden sizes and a 350 embedding size is utilized  Gradients with the l2 norm larger than 2 0 are clipped  We stop training when the ROUGE-L f-score stops increasing 
Hybrid training: An Adam optimizer with a learning rate of 0 0001 is used to optimize $L_{\text{RL-*}}$ (Equation DISPLAY_FORM17) and $L_\text{{ARL-SEN}}$ (Equation DISPLAY_FORM19)  When training Pointer-Gen+RL-ROUGE, the best $\lambda $ is chosen based on the ROUGE-L score on the validation set  In our experiment, $\lambda $ is set as 0 95  An Adam optimizer with a learning rate of 0 001 is used to optimize $L_b$  When training Pointer-Gen+ARL-SEN, we don't use the full LCSTS dataset, but only headlines with a sensationalism score larger than 0 1 as we observe that Pointer-Gen+ARL-SEN will generate a few unnatural phrases when using full dataset  We believe the reason is the high ratio of RL during training  Figure FIGREF23 shows that the probability density near 0 is very high, meaning that in each batch, many of the samples will have a very low sensationalism score  On expectation, each sample will receive 0 239 MLE training and 0 761 RL training  This leads to RL dominanting the loss  Thus, we propose to filter samples with a minimum sensationalism score with 0 1 and it works very well  For Pointer-Gen+RL-SEN, we also set the minimum sensationalism score as 0 1, and $\lambda $ is set as 0 5 to remove unnatural phrases, making a fair comparison to Pointer-Gen+ARL-SEN 
We stop training Pointer-Gen+Same-FT, Pointer-Gen+Pos-FT, Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN, when $\alpha _\text{sen}$ stops increasing on the validation set  Beam-search with a beam size of 5 is adopted for decoding in all models 
Sensational Headline Generation ::: Evaluation Metrics
We briefly describe the evaluation metrics below 
ROUGE: ROUGE is a commonly used evaluation metric for summarization  It measures the N-gram overlap between generated and training headlines  We use it to evaluate the relevance of generated headlines  The widely used pyrouge toolkit is used to calculate ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L) 
Human evaluation: We randomly sample 50 articles from the test set and send the generated headlines from all models and corresponding headlines in the test set to human annotators  We evaluate the sensationalism and fluency of the headlines by setting up two independent human annotation tasks  We ask 10 annotators to label each headline for each task  For the sensationalism annotation, each annotator is asked one question, “Is the headline sensational?”, and he/she has to choose either `yes' or `no'  The annotators were not told which system the headline is from  The process of distributing samples and recruiting annotators is managed by Crowdflower  After annotation, we define the sensationalism score as the proportion of annotations on all generated headlines from one model labeled as `yes'  For the fluency annotation, we repeat the same procedure as for the sensationalism annotation, except that we ask each annotator the question “Is the headline fluent?” We define the fluency score as the proportion of annotations on all headlines from one specific model labeled as `yes'  We put human annotation instructions in the supplemental material 
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Results
We first compare all four models, Pointer-Gen, Pointer-Gen-RL+ROUGE, Pointer-Gen-RL-SEN, and Pointer-Gen-ARL-SEN, to existing models with ROUGE in Table TABREF25 to establish that our model produces relevant headlines and we leave the sensationalism for human evaluation  Note that we only compare our models to commonly used strong summarization baselines, to validate that our implementation achieves comparable performance to existing work  In our implementation, Pointer-Gen achieves a 34 51 RG-1 score, 22 21 RG-2 score, and 31 68 RG-L score, which is similar to the results of gu2016incorporating  Pointer-Gen+ARL-SEN, although optimized for the sensationalism reward, achieves similar performance to our Pointer-Gen baseline, which means that Pointer-Gen+ARL-SEN still keeps its summarization ability  An example of headlines generated from different models in Table TABREF29 shows that Pointer-Gen and Pointer-Gen+RL-ROUGE learns to summarize the main point of the article: “The Nikon D600 camera is reported to have black spots when taking photos”  Pointer-Gen+RL-SEN makes the headline more sensational by blaming Nikon for attributing the damage to the smog  Pointer-Gen+ARL-SEN generates the most sensational headline by exaggerating the result “Getting a serious trouble!” to maximize user's attention 
We then compare different models using the sensationalism score in Table TABREF30  The Pointer-Gen baseline model achieves a 42 6% sensationalism score, which is the minimum that a typical summarization model achieves  By filtering out low-sensational headlines, Pointer-Gen+Same-FT and Pointer-Gen+Pos-FT achieves higher sensationalism scores, which implies the effectiveness of our sensationalism scorer  Our Pointer-Gen+ARL-SEN model achieves the best performance of 60 8%  This is an absolute improvement of 18 2% over the Pointer-Gen baseline  The Chi-square test on the results confirms that Pointer-Gen+ARL-SEN is statistically significantly more sensational than all the other baseline models, with the largest p-value less than 0 01  Also, we find that the test set headlines achieves 57 8% sensationalism score, much larger than Pointer-Gen baseline, which also supports our intuition that generated headlines will be less sensational than the original one  On the other hand, we found that Pointer-Gen+Pos is much worse than other baselines  The reason is that training on sensational samples alone discards around 80% of the whole training set that is also helpful for maintaining relevance and a good language model  It shows the necessity of using RL 
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In addition, both Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN, which use the sensationalism score as the reward, obtain statistically better results than Pointer-Gen+RL-ROUGE and Pointer-Gen, with a p-value less than 0 05 by a Chi-square test  This result shows the effectiveness of RL to generate more sensational headlines  The reason is that even though our noisy classifier could also learn to classify domains, the generator during RL training is not allowed to increase the reward by shifting domains but encouraged to generate more sensational headlines, due to the consistency constraint on the domains of the headline and the article  Furthermore, Poiner-Gen+ARL-SEN gets better performance than Pointer-Gen+RL-SEN, which confirms the superiority of the ARL loss function  We also visualize in Figure FIGREF31 a comparison between Pointer-Gen+ARL-SEN and Pointer-Gen+RL-SEN according to how sensational the test set headlines are  The blue bars denote the smaller scores between the two models  For example, if the blue bar is 0 6, it means that the worse model between Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN achieves 0 6  And the color of orange/black further indicates the better model and its score  We find that Pointer-Gen+ARL-SEN outperforms Pointer-Gen+RL-SEN for most cases  The improvement is higher when the test set headlines are not sensational (the sensationalism score is less than 0 5), which may be attributed to the higher ratio of RL training on non-sensational headlines 
Apart from the sensationalism evaluation, we measure the fluency of the headlines generated from different models  Fluency scores in Table TABREF30 show that Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN achieve comparable fluency performance to Pointer-Gen and Pointer-Gen+RL-ROUGE  Test set headlines achieve the best performance among all models, but the difference is not statistically significant  Also, we observe that fine-tuning on sensational headlines will hurt the performance, both in sensationalism and fluency 
After manually checking the outputs, we observe that our model is able to generate sensational headlines using diverse sensationalization strategies  These strategies include, but are not limited to, creating a curiosity gap, asking questions, highlighting numbers, being emotional and emphasizing the user  Examples can be found in Table TABREF32 
Related Work
Our work is related to summarization tasks  An encoder-decoder model was first applied to two sentence-level abstractive summarization tasks on the DUC-2004 and Gigaword datasets BIBREF12  This model was later extended by selective encoding BIBREF13, a coarse to fine approach BIBREF14, minimum risk training BIBREF1, and topic-aware models BIBREF15  As long summaries were recognized as important, the CNN/Daily Mail dataset was used in nallapati2016abstractive  Graph-based attention BIBREF16, pointer-generator with coverage loss BIBREF0 are further developed to improve the generated summaries  celikyilmaz2018deep proposed deep communicating agents for representing a long document for abstractive summarization  In addition, many papers BIBREF17, BIBREF18, BIBREF19 use extractive methods to directly select sentences from articles  However, none of these work considered the sensationalism of generated outputs 
RL is also gaining popularity as it can directly optimize non-differentiable metrics BIBREF20, BIBREF21, BIBREF22  paulus2017deep proposed an intra-decoder model and combined RL and MLE to deal with summaries with bad qualities  RL has also been explored with generative adversarial networks (GANs) BIBREF23  liu2017generative applied GANs on summarization task and achieved better performance  niu2018polite tackles the problem of polite generation with politeness reward  Our work is different in that we propose a novel function to balance RL and MLE 
Our task is also related to text style transfer  Implicit methods BIBREF10, BIBREF24, BIBREF4 transfer the styles by separating sentence representations into content and style, for example using back-translationBIBREF4  However, these methods cannot guarantee the content consistency between the original sentence and transferred output BIBREF25  Explicit methods BIBREF26, BIBREF25 transfer the style by directly identifying style related keywords and modifying them  However, sensationalism is not always restricted to keywords, but the full sentence  By leveraging small human labeled English dataset, clickbait detection has been well investigated BIBREF2, BIBREF27, BIBREF3  However, these human labeled dataset are not available for other languages, such as Chinese 
Modeling sensationalism is also related to modeling emotion  Emotion has been well investigated in both word levelBIBREF28, BIBREF29 and sentence levelBIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34  It has also been considered an important factor in engaging interactive systemsBIBREF35, BIBREF36, BIBREF37  Although we observe that sensational headlines contain emotion, it is still not clear which emotion and how emotions will influence the sensationalism 
Conclusion and Future Work
In this paper, we propose a model that generates sensational headlines without labeled data using Reinforcement Learning  Firstly, we propose a distant supervision strategy to train the sensationalism scorer  As a result, we achieve 65% accuracy between the predicted sensationalism score and human evaluation  To effectively leverage this noisy sensationalism score as the reward for RL, we propose a novel loss function, ARL, to automatically balance RL with MLE  Human evaluation confirms the effectiveness of both our sensationalism scorer and ARL to generate more sensational headlines  Future work can be improving the sensationalism scorer and investigating the applications of dynamic balancing methods between RL and MLE in textGANBIBREF23  Our work also raises the ethical questions about generating sensational headlines, which can be further explored 
Acknowledgments
Thanks to ITS/319/16FP of Innovation Technology Commission, HKUST 16248016 of Hong Kong Research Grants Council for funding  In addition, we thank Zhaojiang Lin for helpful discussion and Yan Xu, Zihan Liu for the data collection 
Context 2: Introduction
How humans process language has become increasingly relevant in natural language processing since physiological data during language understanding is more accessible and recorded with less effort  In this work, we focus on eye-tracking and electroencephalography (EEG) recordings to capture the reading process  On one hand, eye movement data provides millisecond-accurate records about where humans look when they are reading, and is highly correlated with the cognitive load associated with different stages of text processing  On the other hand, EEG records electrical brain activity across the scalp and is a direct measure of physiological processes, including language processing  The combination of both measurement methods enables us to study the language understanding process in a more natural setting, where participants read full sentences at a time, in their own speed  Eye-tracking then permits us to define exact word boundaries in the timeline of a subject reading a sentence, allowing the extraction of brain activity signals for each word 
Human cognitive language processing data is immensely useful for NLP: Not only can it be leveraged to improve NLP applications (e g  barrett2016weakly for part-of-speech tagging or klerke2016improving for sentence compression), but also to evaluate state-of-the-art machine learning systems  For example, hollenstein2019cognival evaluate word embeddings, or schwartz2019inducing fine-tune language models with brain-relevant bias 
Additionally, the availability of labelled data plays a crucial role in all supervised machine learning applications  Physiological data can be used to understand and improve the labelling process (e g  tokunaga2017eye), and, for instance, to build cost models for active learning scenarios BIBREF0  Is it possible to replace this expensive manual work with models trained on physiological activity data recorded from humans while reading? That is to say, can we find and extract relevant aspects of text understanding and annotation directly from the source, i e  eye-tracking and brain activity signals during reading?
Motivated by these questions and our previously released dataset, ZuCo 1 0 BIBREF1, we developed this new corpus, where we specifically aim to collect recordings during natural reading as well as during annotation 
We provide the first dataset of simultaneous eye movement and brain activity recordings to analyze and compare normal reading to task-specific reading during annotation  The Zurich Cognitive Language Processing Corpus (ZuCo) 2 0, including raw and preprocessed eye-tracking and electroencephalography (EEG) data of 18 subjects, as well as the recording and preprocessing scripts, is publicly available at https://osf io/2urht/  It contains physiological data of each subject reading 739 English sentences from Wikipedia (see example in Figure FIGREF1)  We want to highlight the re-use potential of this data  In addition to the psycholinguistic motivation, this corpus is especially tailored for training and evaluating machine learning algorithms for NLP purposes  We conduct a detailed technical validation of the data as proof of the quality of the recordings 
Related Work
Some eye-tracking corpora of natural reading (e g  the Dundee BIBREF2, Provo BIBREF3 and GECO corpus BIBREF4), and a few EEG corpora (for example, the UCL corpus BIBREF5) are available  It has been shown that this type of cognitive processing data is useful for improving and evaluating NLP methods (e g  barrett2018sequence,hollenstein2019cognival, hale2018finding)  However, before the Zurich Cognitive Language Processing Corpus (ZuCo 1 0), there was no available data for simultaneous eye-tracking and EEG recordings of natural reading  dimigen2011coregistration studied the linguistic effects of eye movements and EEG co-registration in natural reading and showed that they accurately represent lexical processing  Moreover, the simultaneous recordings are crucial to extract word-level brain activity signals 
While the above mentioned studies analyze and leverage natural reading, some NLP work has used eye-tracking during annotation (but, as of yet, not EEG data)  mishra2016predicting and joshi2014measuring recorded eye-tracking during binary sentiment annotation (positive/negative)  This data was used to determine the annotation complexity of the text passages based on eye movement metrics and for sarcasm detection BIBREF6  Moreover, eye-tracking has been used to analyze the word sense annotation process in Hindi BIBREF7, named entity annotation in Japanese BIBREF8, and to leverage annotator gaze behaviour for coreference resolution BIBREF9  Finally, tomanek2010cognitive used eye-tracking data during entity annotation to build a cost model for active learning  However, until now there is no available data or research that analyzes the differences in the human processing of normal reading versus annotation 
Related Work ::: ZuCo1 0
In previous work, we recorded a first dataset of simultaneous eye-tracking and EEG during natural reading BIBREF1  ZuCo 1 0 consists of three reading tasks, two of which contain very similar reading material and experiments as presented in the current work  However, the main difference and reason for recording ZuCo 2 0, consists in the experiment procedure  For ZuCo 1 0 the normal reading and task-specific reading paradigms were recorded in different sessions on different days  Therefore, the recorded data is not appropriate as a means of comparison between natural reading and annotation, since the differences in the brain activity data might result mostly from the different sessions due to the sensitivity of EEG  This, and extending the dataset with more sentences and more subjects, were the main factors for recording the current corpus  We purposefully maintained an overlap of some sentences between both datasets to allow additional analyses (details are described in Section SECREF7) 
Corpus Construction
In this section we describe the contents and experimental design of the ZuCo 2 0 corpus 
Corpus Construction ::: Participants
We recorded data from 19 participants and discarded the data of one of them due to technical difficulties with the eye-tracking calibration  Hence, we share the data of 18 participants  All participants are healthy adults (mean age = 34 (SD=8 3), 10 females)  Their native language is English, originating from Australia, Canada, UK, USA or South Africa  Two participants are left-handed and three participants wear glasses for reading  Details on subject demographics can be found in Table TABREF4  All participants gave written consent for their participation and the re-use of the data prior to the start of the experiments  The study was approved by the Ethics Commission of the University of Zurich 
Corpus Construction ::: Reading materials
During the recording session, the participants read 739 sentences that were selected from the Wikipedia corpus provided by culotta2006integrating  This corpus was chosen because it provides annotations of semantic relations  We included seven of the originally defined relation types: political_affiliation, education, founder, wife/husband, job_title, nationality, and employer  The sentences were chosen in the same length range as ZuCo 1 0, and with similar Flesch reading ease scores  The dataset statistics are shown in Table TABREF2 
Of the 739 sentences, the participants read 349 sentences in a normal reading paradigm, and 390 sentences in a task-specific reading paradigm, in which they had to determine whether a certain relation type occurred in the sentence or not  Table TABREF3 shows the distribution of the different relation types in the sentences of the task-specific annotation paradigm 
Purposefully, there are 63 duplicates between the normal reading and the task-specific sentences (8% of all sentences)  The intention of these duplicate sentences is to provide a set of sentences read twice by all participants with a different task in mind  Hence, this enables the comparison of eye-tracking and brain activity data when reading normally and when annotating specific relations (see examples in Section SECREF4) 
Furthermore, there is also an overlap in the sentences between ZuCo 1 0 and ZuCo 2 0  100 normal reading and 85 task-specific sentences recorded for this dataset were already recorded in ZuCo 1 0  This allows for comparisons between the different recording procedures (i e  session-specific effects) and between more participants (subject-specific effects) 
Corpus Construction ::: Experimental design
As mentioned above, we recorded two different reading tasks for the ZuCo 2 0 dataset  During both tasks the participants were able to read in their own speed, using a control pad to move to the next sentence and to answer the control questions, which allowed for natural reading  Since each subject reads at their own personal pace, the reading speed between varies between subjects  Table TABREF4 shows the average reading speed for each task, i e  the average number of seconds a subject spends per sentence before switching to the next one 
All 739 sentences were recorded in a single session for each participant  The duration of the recording sessions was between 100 and 180 minutes, depending on the time required to set up and calibrate the devices, and the personal reading speed of the participants 
We recorded 14 blocks of approx  50 sentences, alternating between tasks: 50 sentences of normal reading, followed by 50 sentences of task-specific reading  The order of blocks and sentences within blocks was identical for all subjects  Each sentence block was preceded by a practice round of three sentences 
Corpus Construction ::: Experimental design ::: Normal reading (NR)
In the first task, participants were instructed to read the sentences naturally, without any specific task other than comprehension  Participants were told to read the sentences normally without any special instructions  Figure FIGREF8 (left) shows an example sentence as it was depicted on the screen during recording  As shown in Figure FIGREF8 (middle), the control condition for this task consisted of single-choice questions about the content of the previous sentence  12% of randomly selected sentences were followed by such a comprehension question with three answer options on a separate screen 
Corpus Construction ::: Experimental design ::: Task-specific reading (TSR)
In the second task, the participants were instructed to search for a specific relation in each sentence they read  Instead of comprehension questions, the participants had to decide for each sentence whether it contains the relation or not, i e  they were actively annotating each sentence  Figure FIGREF8 (right) shows an example screen for this task  17% of the sentences did not include the relation type and were used as control conditions  All sentences within one block involved the same relation type
Context 3: Introduction
Accurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines  If the language of a piece of text is known then the appropriate downstream models like parts of speech taggers and language models can be applied as required 
LID is further also an important step in harvesting scarce language resources  Harvested data can be used to bootstrap more accurate LID models and in doing so continually improve the quality of the harvested data  Availability of data is still one of the big roadblocks for applying data driven approaches like supervised machine learning in developing countries 
Having 11 official languages of South Africa has lead to initiatives (discussed in the next section) that have had positive effect on the availability of language resources for research  However, many of the South African languages are still under resourced from the point of view of building data driven models for machine comprehension and process automation 
Table TABREF2 shows the percentages of first language speakers for each of the official languages of South Africa  These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso)  The Nguni languages are similar to each other and harder to distinguish  The same is true of the Sotho languages 
This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long  The algorithm is evaluated against recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks 
Section SECREF2 reviews existing works on the topic and summarises the remaining research problems  Section SECREF3 of the paper discusses the proposed algorithm and Section SECREF4 presents comparative results 
Related Works
The focus of this section is on recently published datasets and LID research applicable to the South African context  An in depth survey of algorithms, features, datasets, shared tasks and evaluation methods may be found in BIBREF0 
The datasets for the DSL 2015 & DSL 2017 shared tasks BIBREF1 are often used in LID benchmarks and also available on Kaggle   The DSL datasets, like other LID datasets, consists of text sentences labelled by language  The 2017 dataset, for example, contains 14 languages over 6 language groups with 18000 training samples and 1000 testing samples per language 
The recently published JW300 parallel corpus BIBREF2 covers over 300 languages with around 100 thousand parallel sentences per language pair on average  In South Africa, a multilingual corpus of academic texts produced by university students with different mother tongues is being developed BIBREF3  The WiLI-2018 benchmark dataset BIBREF4 for monolingual written natural language identification includes around 1000 paragraphs of 235 languages  A possibly useful link can also be made BIBREF5 between Native Language Identification (NLI) (determining the native language of the author of a text) and Language Variety Identification (LVI) (classification of different varieties of a single language) which opens up more datasets  The Leipzig Corpora Collection BIBREF6, the Universal Declaration of Human Rights and Tatoeba are also often used sources of data 
The NCHLT text corpora BIBREF7 is likely a good starting point for a shared LID task dataset for the South African languages BIBREF8  The NCHLT text corpora contains enough data to have 3500 training samples and 600 testing samples of 300+ character sentences per language  Researchers have recently started applying existing algorithms for tasks like neural machine translation in earnest to such South African language datasets BIBREF9 
Existing NLP datasets, models and services BIBREF10 are available for South African languages  These include an LID algorithm BIBREF11 that uses a character level n-gram language model  Multiple papers have shown that 'shallow' naive Bayes classifiers BIBREF12, BIBREF8, BIBREF13, BIBREF14, SVMs BIBREF15 and similar models work very well for doing LID  The DSL 2017 paper BIBREF1, for example, gives an overview of the solutions of all of the teams that competed on the shared task and the winning approach BIBREF16 used an SVM with character n-gram, parts of speech tag features and some other engineered features  The winning approach for DSL 2015 used an ensemble naive Bayes classifier  The fasttext classifier BIBREF17 is perhaps one of the best known efficient 'shallow' text classifiers that have been used for LID  
Multiple papers have proposed hierarchical stacked classifiers (including lexicons) that would for example first classify a piece of text by language group and then by exact language BIBREF18, BIBREF19, BIBREF8, BIBREF0  Some work has also been done on classifying surnames between Tshivenda, Xitsonga and Sepedi BIBREF20  Additionally, data augmentation BIBREF21 and adversarial training BIBREF22 approaches are potentially very useful to reduce the requirement for data 
Researchers have investigated deeper LID models like bidirectional recurrent neural networks BIBREF23 or ensembles of recurrent neural networks BIBREF24  The latter is reported to achieve 95 12% in the DSL 2015 shared task  In these models text features can include character and word n-grams as well as informative character and word-level features learnt BIBREF25 from the training data  The neural methods seem to work well in tasks where more training data is available 
In summary, LID of short texts, informal styles and similar languages remains a difficult problem which is actively being researched  Increased confusion can in general be expected between shorter pieces of text and languages that are more closely related  Shallow methods still seem to work well compared to deeper models for LID  Other remaining research opportunities seem to be data harvesting, building standardised datasets and creating shared tasks for South Africa and Africa  Support for language codes that include more languages seems to be growing and discoverability of research is improving with more survey papers coming out  Paywalls also seem to no longer be a problem; the references used in this paper was either openly published or available as preprint papers 
Methodology
The proposed LID algorithm builds on the work in BIBREF8 and BIBREF26  We apply a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier 
The naive Bayesian classifier is trained to predict the specific language label of a piece of text, but used to first classify text as belonging to either the Nguni family, the Sotho family, English, Afrikaans, Xitsonga or Tshivenda  The scikit-learn multinomial naive Bayes classifier is used for the implementation with an alpha smoothing value of 0 01 and hashed text features 
The lexicon based classifier is then used to predict the specific language within a language group  For the South African languages this is done for the Nguni and Sotho groups  If the lexicon prediction of the specific language has high confidence then its result is used as the final label else the naive Bayesian classifier's specific language prediction is used as the final result  The lexicon is built over all the data and therefore includes the vocabulary from both the training and testing sets 
The lexicon based classifier is designed to trade higher precision for lower recall  The proposed implementation is considered confident if the number of words from the winning language is at least one more than the number of words considered to be from the language scored in second place 
The stacked classifier is tested against three public LID implementations BIBREF17, BIBREF23, BIBREF8  The LID implementation described in BIBREF17 is available on GitHub and is trained and tested according to a post on the fasttext blog  Character (5-6)-gram features with 16 dimensional vectors worked the best  The implementation discussed in BIBREF23 is available from https://github com/tomkocmi/LanideNN  Following the instructions for an OSX pip install of an old r0 8 release of TensorFlow, the LanideNN code could be executed in Python 3 7 4  Settings were left at their defaults and a learning rate of 0 001 was used followed by a refinement with learning rate of 0 0001  Only one code modification was applied to return the results from a method that previously just printed to screen  The LID algorithm described in BIBREF8 is also available on GitHub 
The stacked classifier is also tested against the results reported for four other algorithms BIBREF16, BIBREF26, BIBREF24, BIBREF15  All the comparisons are done using the NCHLT BIBREF7, DSL 2015 BIBREF19 and DSL 2017 BIBREF1 datasets discussed in Section SECREF2 
Results and Analysis
The average classification accuracy results are summarised in Table TABREF9  The accuracies reported are for classifying a piece of text by its specific language label  Classifying text only by language group or family is a much easier task as reported in BIBREF8 
Different variations of the proposed classifier were evaluated  A single NB classifier (NB), a stack of two NB classifiers (NB+NB), a stack of a NB classifier and lexicon (NB+Lex) and a lexicon (Lex) by itself  A lexicon with a 50% training token dropout is also listed to show the impact of the lexicon support on the accuracy 
From the results it seems that the DSL 2017 task might be harder than the DSL 2015 and NCHLT tasks  Also, the results for the implementation discussed in BIBREF23 might seem low, but the results reported in that paper is generated on longer pieces of text so lower scores on the shorter pieces of text derived from the NCHLT corpora is expected 
The accuracy of the proposed algorithm seems to be dependent on the support of the lexicon  Without a good lexicon a non-stacked naive Bayesian classifier might even perform better 
The execution performance of some of the LID implementations are shown in Table TABREF10  Results were generated on an early 2015 13-inch Retina MacBook Pro with a 2 9 GHz CPU (Turbo Boosted to 3 4 GHz) and 8GB RAM  The C++ implementation in BIBREF17 is the fastest  The implementation in BIBREF8 makes use of un-hashed feature representations which causes it to be slower than the proposed sklearn implementation  The execution performance of BIBREF23 might improve by a factor of five to ten when executed on a GPU 
Conclusion
LID of short texts, informal styles and similar languages remains a difficult problem which is actively being researched  The proposed algorithm was evaluated on three existing datasets and compared to the implementations of three public LID implementations as well as to reported results of four other algorithms  It performed well relative to the other methods beating their results  However, the performance is dependent on the support of the lexicon 
We would like to investigate the value of a lexicon in a production system and how to possibly maintain it using self-supervised learning  We are investigating the application of deeper language models some of which have been used in more recent DSL shared tasks  We would also like to investigate data augmentation strategies to reduce the amount of training data that is required 
Further research opportunities include data harvesting, building standardised datasets and shared tasks for South Africa as well as the rest of Africa  In general, the support for language codes that include more languages seems to be growing, discoverability of research is improving and paywalls seem to no longer be a big problem in getting access to published research 

ground_truth: Pointer-Gen, Pointer-Gen+Pos, Pointer-Gen+Same-FT, Pointer-Gen+Pos-FT, Pointer-Gen+RL-ROUGE, Pointer-Gen+RL-SEN

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. Did they experiment with this new dataset?
Context 2:  The blocks started with a practice round, which described the relation and was followed by three sample sentences, so that the participants would be familiar with the respective relation type 
Corpus Construction ::: Linguistic assessment
As a linguistic assessment, the vocabulary and language proficiency of the participants was tested with the LexTALE test (Lexical Test for Advanced Learners of English, lemhofer2012introducing)  This is an unspeeded lexical decision task designed for intermediate to highly proficient language users  The average LexTALE score over all participants was 88 54%  Moreover, we also report the scores the participants achieved with their answers to the reading comprehension control questions and their relation annotations  The detailed scores for all participants are also presented in Table TABREF4 
Corpus Construction ::: Data acquisition
Data acquisition took place in a sound-attenuated and dark experiment room  Participants were seated at a distance of 68cm from a 24-inch monitor with a resolution of 800x600 pixels  A stable head position was ensured via a chin rest  Participants were instructed to stay as still as possible during the tasks to avoid motor EEG artifacts  Participants were also offered snacks and water during the breaks and were encouraged to rest  All sentences were presented at the same position on the screen and could span multiple lines  The sentences were presented in black on a light grey background with font size 20-point Arial, resulting in a letter height of 0 8 mm  The experiment was programmed in MATLAB 2016b BIBREF10, using PsychToolbox BIBREF11  Participants completed the tasks sitting alone in the room, while two research assistants were monitoring their progress in the adjoining room  All recording scripts including detailed participant instructions are available alongside the data 
Corpus Construction ::: Data acquisition ::: Eye-tracking acquisition
Eye position and pupil size were recorded with an infrared video-based eye tracker (EyeLink 1000 Plus, SR Research) at a sampling rate of 500 Hz  The eye tracker was calibrated with a 9-point grid at the beginning of the session and re-validated before each block of sentences 
Corpus Construction ::: Data acquisition ::: EEG acquisition
High-density EEG data were recorded at a sampling rate of 500 Hz with a bandpass of 0 1 to 100 Hz, using a 128-channel EEG Geodesic Hydrocel system (Electrical Geodesics)  The recording reference was set at electrode Cz  The head circumference of each participant was measured to select an appropriately sized EEG net  To ensure good contact, the impedance of each electrode was checked prior to recording, and was kept below 40 kOhm  Electrode impedance levels were checked after every third block of 50 sentences (approx  every 30 mins) and reduced if necessary 
Corpus Construction ::: Preprocessing and feature extraction ::: Eye-tracking
The eye-tracking data consists of (x,y) gaze location entries for all individual fixations (Figure FIGREF1b)  Coordinates were given in pixels with respect to the monitor coordinates (the upper left corner of the screen was (0,0) and down/right was positive)  We provide this raw data as well as various engineered eye-tracking features  For this feature extraction only fixations within the boundaries of each displayed word were extracted  Data points distinctly not associated with reading (minimum distance of 50 pixels to the text) were excluded  Additionally, fixations shorter than 100 ms were excluded from the analyses, because these are unlikely to reflect fixations relevant for reading BIBREF12  On the basis of the GECO and ZuCo 1 0 corpora, we extracted the following features: (i) gaze duration (GD), the sum of all fixations on the current word in the first-pass reading before the eye moves out of the word; (ii) total reading time (TRT), the sum of all fixation durations on the current word, including regressions; (iii) first fixation duration (FFD), the duration of the first fixation on the prevailing word; (iv) single fixation duration (SFD), the duration of the first and only fixation on the current word; and (v) go-past time (GPT), the sum of all fixations prior to progressing to the right of the current word, including regressions to previous words that originated from the current word  For each of these eye-tracking features we additionally computed the pupil size  Furthermore, we extracted the number of fixations and mean pupil size for each word and sentence 
Corpus Construction ::: Preprocessing and feature extraction ::: EEG
The EEG data shared in this project are available as raw data, but also preprocessed with Automagic (version 1 4 6, pedroni2019automagic), a tool for automatic EEG data cleaning and validation  105 EEG channels (i e  electrodes) were used from the scalp recordings  9 EOG channels were used for artifact removal and additional 14 channels lying mainly on the neck and face were discarded before data analysis  Bad channels were identified and interpolated  We used the Multiple Artifact Rejection Algorithm (MARA), a supervised machine learning algorithm that evaluates ICA components, for automatic artifact rejection  MARA has been trained on manual component classifications, and thus captures a wide range of artifacts  MARA is especially effective at detecting and removing eye and muscle artifact components  The effect of this preprocessing can be seen in Figure FIGREF1d 
After preprocessing, we synchronized the EEG and eye-tracking data to enable EEG analyses time-locked to the onsets of fixations  To compute oscillatory power measures, we band-pass filtered the continuous EEG signals across an entire reading task for five different frequency bands resulting in a time-series for each frequency band  The independent frequency bands were determined as follows: theta$_1$ (4–6 Hz), theta$_2$ (6 5–8 Hz), alpha$_1$ (8 5–10 Hz), alpha$_2$ (10 5–13 Hz), beta$_1$ (13 5–18 Hz), beta$_2$ (18 5–30 Hz), gamma$_1$ (30 5–40 Hz), and gamma$_2$ (40–49 5 Hz)  We then applied a Hilbert transformation to each of these time-series  We specifically chose the Hilbert transformation to maintain the temporal information of the amplitude of the frequency bands, to enable the power of the different frequencies for time segments defined through the fixations from the eye-tracking recording  Thus, for each eye-tracking feature we computed the corresponding EEG feature in each frequency band  Furthermore, we extracted sentence-level EEG features by calculating the power in each frequency band, and additionally, the difference of the power spectra between frontal left and right homologue electrodes pairs  For each eye-tracking based EEG feature, all channels were subject to an artifact rejection criterion of $90\mu V$ to exclude trials with transient noise 
Data Validation
The aim of the technical validation of the data is to guarantee good recording quality and to replicate findings of previous studies investigating co-registration of EEG and eye movement data during natural reading tasks (e g  dimigen2011coregistration)  We also compare the results to ZuCo 1 0 BIBREF1, which allows a more direct comparison due to the analogous recording procedure 
Data Validation ::: Eye-tracking
We validated the recorded eye-tracking data by analyzing the fixations made by all subjects through their reading speed and omission rate on sentence level  The omission rate is defined as the percentage of words that is not fixated in a sentence  Figure FIGREF10 (middle) shows the mean reading speed over all subjects, measured in seconds per sentence and Figure FIGREF10 (right) shows the mean omission rates aggregated over all subjects for each task  Clearly, the participants made less fixations during the task-specific reading, which lead to faster reading speed 
Moreover, we corroborated these sentence-level metrics by visualizing the skipping proportion on word level (Figure FIGREF13)  The skipping proportion is the average rate of words being skipped (i e  not being fixated) in a sentence  As expected, this also increases in the task-specific reading 
Although the reading material is from the same source and of the same length range (see Figure FIGREF10 (left)), in the first task (NR) passive reading was recorded, while in the second task (TSR) the subjects had to annotate a specific relation type in each sentence  Thus, the task-specific annotation reading lead to shorter passes because the goal was merely to recognize a relation in the text, but not necessarily to process the every word in each sentence  This distinct reading behavior is shown in Figure FIGREF15, where fixations occur until the end of the sentence during normal reading, while during task-specific reading the fixations stop after the decisive words to detect a given relation type  Finally, we also analyzed the average reading times for each of the extracted eye-tracking features  The means and distributions for both tasks are shown in Figure FIGREF21  These results are in line with the recorded data in ZuCo 1 0, as well as with the features extracted in the GECO corpus BIBREF4 
Data Validation ::: EEG
As a first validation step, we extracted fixation-related potentials (FRPs), where the EEG signal during all fixations of one task are averaged  Figure FIGREF24 shows the time-series of the resulting FRPs for two electrodes (PO8 and Cz), as well as topographies of the voltage distributions across the scalp at selected points in time  The five components (for which the scalp topographies are plotted) are highly similar in the time-course of the chosen electrodes to dimigen2011coregistration as well as to ZuCo 1 0 
Moreover, these previous studies were able to show an effect of fixation duration on the resulting FRPs  To show this dependency we followed two approaches  First, for each reading task, all single-trial FRPs were ordered by fixation duration and a vertical sliding time-window was used to smooth the data BIBREF13  Figure FIGREF25 (bottom) shows the resulting plots  In line with this previous work, a first positivation can be identified at 100 ms post-fixation onset  A second positive peak is located dependent on the duration of the fixation, which can be explained by the time-jittered succeeding fixation  The second approach is based on henderson2013co in which single trial EEG segments are clustered by the duration of the current fixation  As shown in Figure FIGREF25 (top), we chose four clusters and averaged the data within each cluster to four distinct FRPs, depending on the fixation duration  Again, the same positivation peaks become apparent  Both findings are consistent with the previous work mentioned and with our findings from ZuCo 1 0 
Conclusion
We presented a new, freely available corpus of eye movement and electrical brain activity recordings during natural reading as well as during annotation  This is the first dataset that allows for the comparison between these two reading paradigms  We described the materials and experiment design in detail and conducted an extensive validation to ensure the quality of the recorded data  Since this corpus is tailored to cognitively-inspired NLP, the applications and re-use potentials of this data are extensive  The provided word-level and sentence-level eye-tracking and EEG features can be used to improve and evaluate NLP and machine learning methods, for instance, to evaluate linguistic phenomena in neural models via psycholinguistic data  In addition, because the sentences contains semantic relation labels and the annotations of all participants, it can also be widely used for relation extraction and classification  Finally, the two carefully constructed reading paradigms allow for the comparison between normal reading and reading during annotation, which can be relevant to improve the manual labelling process as well as the quality of the annotations for supervised machine learning 

ground_truth: No

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. Which languages are similar to each other?
Context 4:  Since IWSLT participators use different data pre-processing methods, we reproduce several competitive baselines based on the ESPnet BIBREF12 for a fair comparison 
Experiments ::: Dataset ::: Speech translation data:
The organizer provides a speech translation corpus extracting from the TED talk (ST-TED), which consists of raw English wave files, English transcriptions, and aligned German translations  The corpus contains 272 hours of English speech with 171k segments  We split 2k segments from the corpus as dev set and tst2010, tst2013, tst2014, tst2015 are used as test sets 
Speech recognition data: Aside from ST-TED, TED-LIUM2 corpus BIBREF13 is provided as speech recognition data, which contains 207 hours of English speech and 93k transcript sentences 
Text translation data: We use transcription and translation pairs in the ST-TED corpus and WIT3 as in-domain MT data, which contains 130k and 200k sentence pairs respectively  WMT2018 is used as out-of-domain training data which consists of 41M sentence pairs 
Data preprocessing: For speech data, the utterances are segmented into multiple frames with a 25 ms window size and a 10 ms step size  Then we extract 80-channel log-Mel filter bank and 3-dimensional pitch features using Kaldi BIBREF14, resulting in 83-dimensional input features  We normalize them by the mean and the standard deviation on the whole training set  The utterances with more than 3000 frames are discarded  The transcripts in ST-TED are in true-case with punctuation while in TED-LIUM2, transcripts are in lower-case and unpunctuated  Thus, we lowercase all the sentences and remove the punctuation to keep consistent  To increase the amount of training data, we perform speed perturbation on the raw signals with speed factors 0 9 and 1 1  For the text translation data, sentences longer than 80 words or shorter than 10 words are removed  Besides, we discard pairs whose length ratio between source and target sentence is smaller than 0 5 or larger than 2 0  Word tokenization is performed using the Moses scripts and both English and German words are in lower-case 
We use two different sets of vocabulary for our experiments  For the subword experiments, both English and German vocabularies are generated using sentencepiece BIBREF15 with a fixed size of 5k tokens  BIBREF9 inaguma2018speech show that increasing the vocabulary size is not helpful for ST task  For the character experiments, both English and German sentences are represented in the character level 
For evaluation, we segment each audio with the LIUM SpkDiarization tool BIBREF16 and then perform MWER segmentation with RWTH toolkit BIBREF17  We use lowercase BLEU as evaluation metric 
Experiments ::: Baseline Models and Implementation
We compare our method with following baselines 
Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder  It is trained from scratch on the ST-TED corpus 
Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained  The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus  The MT model has a text encoder and decoder with the same architecture of which in TCEN  It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data 
Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting  In the first two settings, we train the model with $\alpha _{st}=0 75$ while $\alpha _{asr}=0 25$ or $\alpha _{mt}=0 25$  For many-to-many setting, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$   For MT task, we use only in-domain data 
Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models  Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation  Their model solves the subnet waste issue by concatenating an ST decoder to an ASR encoder-decoder model  Notably, their ST decoder can consume representations from the speech encoder as well as the ASR decoder  For a fair comparison, the speech encoder and the ASR decoder are initialized from the pre-trained ASR model  The Triangle model is fine-tuned under their multi-task manner 
All our baselines as well as TCEN are implemented based on ESPnet BIBREF12, the RNN size is set as $d=1024$ for all models  We use a dropout of 0 3 for embeddings and encoders, and train using Adadelta with initial learning rate of 1 0 for a maximum of 10 epochs 
For training of TCEN, we set $\alpha _{asr}=0 2$ and $\alpha _{mt}=0 8$ in the pre-training stage, since the MT dataset is much larger than ASR dataset  For fine-tune, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$, same as the `many-to-many' baseline 
For testing, we select the model with the best accuracy on speech translation task on dev set  At inference time, we use a beam size of 10, and the beam scores include length normalization with a weight of 0 2 
Experiments ::: Experimental Results
Table TABREF29 shows the results on four test sets as well as the average performance  Our method significantly outperforms the strong `many-to-many+pretrain' baseline by 3 6 and 2 2 BLEU scores respectively, indicating the proposed method is very effective that substantially improves the translation quality  Besides, both pre-training and multi-task learning can improve translation quality, and the pre-training settings (2nd-4th rows) are more effective compared to multi-task settings (5th-8th rows)  We observe a performance degradation in the `triangle+pretrain' baseline  Compared to our method, where the decoder receives higher-level syntactic and semantic linguistic knowledge extracted from text encoder, their ASR decoder can only provide lower word-level linguistic information  Besides, since their model lacks text encoder and the architecture of ST decoder is different from MT decoder, their model cannot utilize the large-scale MT data in all the training stages  Interestingly, we find that the char-level models outperform the subword-level models in all settings, especially in vanilla baseline  A similar phenomenon is observed by BIBREF6 berard2018end  A possible explanation is that learning the alignments between speech frames and subword units in another language is notoriously difficult  Our method can bring more gains in the subword setting since our model is good at learning the text-to-text alignment and the subword-level alignment is more helpful to the translation quality 
Experiments ::: Discussion ::: Ablation Study
To better understand the contribution of each component, we perform an ablation study on subword-level experiments  The results are shown in Table TABREF37  In `-MT noise' setting, we do not add noise to source sentences for MT  In `-weight sharing' setting, we use different parameters in CTC classification layer and source embedding layer  These two experiments prove that both weight sharing and using noisy MT input benefit to the final translation quality  Performance degrades more in `-weight sharing', indicating the semantic consistency contributes more to our model  In the `-pretrain' experiment, we remove the pre-training stage and directly update the model on three tasks, leading to a dramatic decrease on BLEU score, indicating the pre-training is an indispensable step for end-to-end ST 
Experiments ::: Discussion ::: Learning Curve
It is interesting to investigate why our method is superior to baselines  We find that TCEN achieves a higher final result owing to a better start-point in fine-tuning  Figure FIGREF39 provides learning curves of subword accuracy on validation set  The x-axis denotes the fine-tuning training steps  The vanilla model starts at a low accuracy, because its networks are not pre-trained on the ASR and MT data  The trends of our model and `many-to-many+pretrain' are similar, but our model outperforms it about five points in the whole fine-tuning process  It indicates that the gain comes from bridging the gap between pre-training and fine-tuning rather than a better fine-tuning process 
Experiments ::: Discussion ::: Compared with a Cascaded System
Table TABREF29 compares our model with end-to-end baselines  Here, we compare our model with cascaded systems  We build a cascaded system by combining the ASR model and MT model used in pre-training baseline  Word error rate (WER) of the ASR system and BLEU score of the MT system are reported in the supplementary material  In addition to a simple combination of the ASR and MT systems, we also re-segment the ASR outputs before feeding to the MT system, denoted as cascaded+re-seg  Specifically, we train a seq2seq model BIBREF19 on the MT dataset, where the source side is a no punctuation sentence and the target side is a natural sentence  After that, we use the seq2seq model to add sentence boundaries and punctuation on ASR outputs  Experimental results are shown in Table TABREF41  Our end-to-end model outperforms the simple cascaded model over 2 BLEU scores, and it achieves a comparable performance with the cascaded model combining with a sentence re-segment model 
Related Work
Early works conduct speech translation in a pipeline manner BIBREF2, BIBREF20, where the ASR output lattices are fed into an MT system to generate target sentences  HMM BIBREF21, DenseNet BIBREF22, TDNN BIBREF23 are commonly used ASR systems, while RNN with attention BIBREF19 and Transformer BIBREF10 are top choices for MT  To enhance the robustness of the NMT model towards ASR errors, BIBREF24 DBLP:conf/eacl/TsvetkovMD14 and BIBREF25 DBLP:conf/asru/ChenHHL17 propose to simulate the noise in training and inference 
To avoid error propagation and high latency issues, recent works propose translating the acoustic speech into text in target language without yielding the source transcription BIBREF4  Since ST data is scarce, pre-training BIBREF7, multi-task learning BIBREF4, BIBREF6, curriculum learning BIBREF26, attention-passing BIBREF27, and knowledge distillation BIBREF28, BIBREF29 strategies have been explored to utilize ASR data and MT data  Specifically, BIBREF5 DBLP:conf/interspeech/WeissCJWC17 show improvements of performance by training the ST model jointly with the ASR and the MT model  BIBREF6 berard2018end observe faster convergence and better results due to pre-training and multi-task learning on a larger dataset  BIBREF7 DBLP:conf/naacl/BansalKLLG19 show that pre-training a speech encoder on one language can improve ST quality on a different source language  All of them follow the traditional multi-task training strategies  BIBREF26 DBLP:journals/corr/abs-1802-06003 propose to use curriculum learning to improve ST performance on syntactically distant language pairs  To effectively leverage transcriptions in ST data, BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 augment the multi-task model where the target decoder receives information from the source decoder and they show improvements on low-resource speech translation  Their model just consumes ASR and ST data, in contrast, our work sufficiently utilizes the large-scale MT data to capture the rich semantic knowledge  BIBREF30 DBLP:conf/icassp/JiaJMWCCALW19 use pre-trained MT and text-to-speech (TTS) synthesis models to convert weakly supervised data into ST pairs and demonstrate that an end-to-end MT model can be trained using only synthesised data 
Conclusion
This paper has investigated the end-to-end method for ST  It has discussed why there is a huge gap between pre-training and fine-tuning in previous methods  To alleviate these issues, we have proposed a method, which is capable of reusing every sub-net and keeping the role of sub-net consistent between pre-training and fine-tuning  Empirical studies have demonstrated that our model significantly outperforms baselines 

ground_truth: Nguni languages (zul, xho, nbl, ssw), Sotho languages (nso, sot, tsn)

The Nguni languages are similar to each other, The same is true of the Sotho languages