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
Performance appraisal (PA) is an important HR process, particularly for modern organizations that crucially depend on the skills and expertise of their workforce  The PA process enables an organization to periodically measure and evaluate every employee's performance  It also provides a mechanism to link the goals established by the organization to its each employee's day-to-day activities and performance  Design and analysis of PA processes is a lively area of research within the HR community BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3  
The PA process in any modern organization is nowadays implemented and tracked through an IT system (the PA system) that records the interactions that happen in various steps  Availability of this data in a computer-readable database opens up opportunities to analyze it using automated statistical, data-mining and text-mining techniques, to generate novel and actionable insights / patterns and to help in improving the quality and effectiveness of the PA process BIBREF4 , BIBREF5 , BIBREF6   Automated analysis of large-scale PA data is now facilitated by technological and algorithmic advances, and is becoming essential for large organizations containing thousands of geographically distributed employees handling a wide variety of roles and tasks 
A typical PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers  In most PA processes, the communication includes the following steps: (i) in self-appraisal, an employee records his/her achievements, activities, tasks handled etc ; (ii) in supervisor assessment, the supervisor provides the criticism, evaluation and suggestions for improvement of performance etc ; and (iii) in peer feedback (aka INLINEFORM0 view), the peers of the employee provide their feedback  There are several business questions that managers are interested in  Examples:
In this paper, we develop text mining techniques that can automatically produce answers to these questions  Since the intended users are HR executives, ideally, the techniques should work with minimum training data and experimentation with parameter setting  These techniques have been implemented and are being used in a PA system in a large multi-national IT company 
The rest of the paper is organized as follows  Section SECREF2 summarizes related work  Section SECREF3 summarizes the PA dataset used in this paper  Section SECREF4 applies sentence classification algorithms to automatically discover three important classes of sentences in the PA corpus viz , sentences that discuss strengths, weaknesses of employees and contain suggestions for improving her performance  Section SECREF5 considers the problem of mapping the actual targets mentioned in strengths, weaknesses and suggestions to a fixed set of attributes  In Section SECREF6 , we discuss how the feedback from peers for a particular employee can be summarized  In Section SECREF7 we draw conclusions and identify some further work 
Related Work
We first review some work related to sentence classification  Semantically classifying sentences (based on the sentence's purpose) is a much harder task, and is gaining increasing attention from linguists and NLP researchers  McKnight and Srinivasan BIBREF7 and Yamamoto and Takagi BIBREF8 used SVM to classify sentences in biomedical abstracts into classes such as INTRODUCTION, BACKGROUND, PURPOSE, METHOD, RESULT, CONCLUSION  Cohen et al  BIBREF9 applied SVM and other techniques to learn classifiers for sentences in emails into classes, which are speech acts defined by a verb-noun pair, with verbs such as request, propose, amend, commit, deliver and nouns such as meeting, document, committee; see also BIBREF10   Khoo et al  BIBREF11 uses various classifiers to classify sentences in emails into classes such as APOLOGY, INSTRUCTION, QUESTION, REQUEST, SALUTATION, STATEMENT, SUGGESTION, THANKING etc  Qadir and Riloff BIBREF12 proposes several filters and classifiers to classify sentences on message boards (community QA systems) into 4 speech acts: COMMISSIVE (speaker commits to a future action), DIRECTIVE (speaker expects listener to take some action), EXPRESSIVE (speaker expresses his or her psychological state to the listener), REPRESENTATIVE (represents the speaker's belief of something)  Hachey and Grover BIBREF13 used SVM and maximum entropy classifiers to classify sentences in legal documents into classes such as FACT, PROCEEDINGS, BACKGROUND, FRAMING, DISPOSAL; see also BIBREF14   Deshpande et al  BIBREF15 proposes unsupervised linguistic patterns to classify sentences into classes SUGGESTION, COMPLAINT 
There is much work on a closely related problem viz , classifying sentences in dialogues through dialogue-specific categories called dialogue acts BIBREF16 , which we will not review here  Just as one example, Cotterill BIBREF17 classifies questions in emails into the dialogue acts of YES_NO_QUESTION, WH_QUESTION, ACTION_REQUEST, RHETORICAL, MULTIPLE_CHOICE etc 
We could not find much work related to mining of performance appraisals data  Pawar et al  BIBREF18 uses kernel-based classification to classify sentences in both performance appraisal text and product reviews into classes SUGGESTION, APPRECIATION, COMPLAINT  Apte et al  BIBREF6 provides two algorithms for matching the descriptions of goals or tasks assigned to employees to a standard template of model goals  One algorithm is based on the co-training framework and uses goal descriptions and self-appraisal comments as two separate perspectives  The second approach uses semantic similarity under a weak supervision framework  Ramrakhiyani et al  BIBREF5 proposes label propagation algorithms to discover aspects in supervisor assessments in performance appraisals, where an aspect is modelled as a verb-noun pair (e g  conduct training, improve coding) 
Dataset
In this paper, we used the supervisor assessment and peer feedback text produced during the performance appraisal of 4528 employees in a large multi-national IT company  The corpus of supervisor assessment has 26972 sentences  The summary statistics about the number of words in a sentence is: min:4 max:217 average:15 5 STDEV:9 2 Q1:9 Q2:14 Q3:19 
Sentence Classification
The PA corpus contains several classes of sentences that are of interest  In this paper, we focus on three important classes of sentences viz , sentences that discuss strengths (class STRENGTH), weaknesses of employees (class WEAKNESS) and suggestions for improving her performance (class SUGGESTION)  The strengths or weaknesses are mostly about the performance in work carried out, but sometimes they can be about the working style or other personal qualities  The classes WEAKNESS and SUGGESTION are somewhat overlapping; e g , a suggestion may address a perceived weakness  Following are two example sentences in each class 
STRENGTH:
WEAKNESS:
SUGGESTION:
Several linguistic aspects of these classes of sentences are apparent  The subject is implicit in many sentences  The strengths are often mentioned as either noun phrases (NP) with positive adjectives (Excellent technology leadership) or positive nouns (engineering strength) or through verbs with positive polarity (dedicated) or as verb phrases containing positive adjectives (delivers innovative solutions)  Similarly for weaknesses, where negation is more frequently used (presentations are not his forte), or alternatively, the polarities of verbs (avoid) or adjectives (poor) tend to be negative  However, sometimes the form of both the strengths and weaknesses is the same, typically a stand-alone sentiment-neutral NP, making it difficult to distinguish between them; e g , adherence to timing or timely closure  Suggestions often have an imperative mood and contain secondary verbs such as need to, should, has to  Suggestions are sometimes expressed using comparatives (better process compliance)  We built a simple set of patterns for each of the 3 classes on the POS-tagged form of the sentences  We use each set of these patterns as an unsupervised sentence classifier for that class  If a particular sentence matched with patterns for multiple classes, then we have simple tie-breaking rules for picking the final class  The pattern for the STRENGTH class looks for the presence of positive words / phrases like takes ownership, excellent, hard working, commitment, etc  Similarly, the pattern for the WEAKNESS class looks for the presence of negative words / phrases like lacking, diffident, slow learner, less focused, etc  The SUGGESTION pattern not only looks for keywords like should, needs to but also for POS based pattern like “a verb in the base form (VB) in the beginning of a sentence” 
We randomly selected 2000 sentences from the supervisor assessment corpus and manually tagged them (dataset D1)  This labelled dataset contained 705, 103, 822 and 370 sentences having the class labels STRENGTH, WEAKNESS, SUGGESTION or OTHER respectively  We trained several multi-class classifiers on this dataset  Table TABREF10 shows the results of 5-fold cross-validation experiments on dataset D1  For the first 5 classifiers, we used their implementation from the SciKit Learn library in Python (scikit-learn org)  The features used for these classifiers were simply the sentence words along with their frequencies  For the last 2 classifiers (in Table TABREF10 ), we used our own implementation  The overall accuracy for a classifier is defined as INLINEFORM0 , where the denominator is 2000 for dataset D1  Note that the pattern-based approach is unsupervised i e , it did not use any training data  Hence, the results shown for it are for the entire dataset and not based on cross-validation 
Comparison with Sentiment Analyzer
We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS
Context 3: Introduction
The cognitive processes involved in human language comprehension are complex and only partially identified  According to the dual-stream model of speech comprehension BIBREF1 , sound waves are first converted to phoneme-like features and further processed by a ventral stream that maps those features onto words and semantic structures, and a dorsal stream that (among other things) supports audio-short term memory  The mapping of words onto meaning is thought to be subserved by widely distributed regions of the brain that specialize in particular modalities — for example visual aspects of the word banana reside in the occipital lobe of the brain and are activated when the word banana is heard BIBREF2 — and the different representation modalities are thought to be integrated into a single coherent latent representation in the anterior temporal lobe BIBREF3   While this part of meaning representation in human language comprehension is somewhat understood, much less is known about how the meanings of words are integrated together to form the meaning of sentences and discourses  One tool researchers use to study the integration of meaning across words is electroencephelography (EEG), which measures the electrical activity of large numbers of neurons acting in concert  EEG has the temporal resolution necessary to study the processes involved in meaning integration, and certain stereotyped electrical responses to word presentations, known as event-related potentials (ERPs), have been identified with some of the processes thought to contribute to comprehension 
In this work, we consider six ERP components that have been associated in the cognitive neuroscience and psycholinguistics literature with language processing and which we analyze in the data from BIBREF0 (see Figure FIGREF1 for spatial and temporal definitions of these ERP components)  Three of these — the N400, EPNP, and PNP responses — are primarily considered markers for semantic processing, while the other three — the P600, ELAN, and LAN responses — are primarily considered markers for syntactic processing  However, the neat division of the ERP responses into either semantic or syntactic categories is controversial  The N400 response has been very well studied (for an overview see BIBREF4 ) and it is well established that it is associated with semantic complexity, but the features of language that trigger the other ERP responses we consider here are poorly understood  We propose to use a neural network pretrained as a language model to probe what features of language drive these ERP responses, and in turn to probe what features of language mediate the cognitive processes that underlie human language comprehension, and especially the integration of meaning across words 
Background
While a full discussion of each ERP component and the features of language thought to trigger each are beyond the scope of this document (for reviews see e g  BIBREF0 , BIBREF2 , BIBREF4 , BIBREF5 , and BIBREF6 ), we introduce some basic features of ERP components to help in the discussion later  ERP components are electrical potential responses measured with respect to a baseline that are triggered by an event (in our case the presentation of a new word to a participant in an experiment)  The name of each ERP component reflects whether the potential is positive or negative relative to the baseline  The N400 is so-named because it is Negative relative to a baseline (the baseline is typically recorded just before a word is presented at an electrode that is not affected by the ERP response) and because it peaks in magnitude at about 400ms after a word is presented to a participant in an experiment  The P600 is Positive relative to a baseline and peaks around 600ms after a word is presented to a participant (though its overall duration is much longer and less specific in time than the N400)  The post-N400 positivity is so-named because it is part of a biphasic response; it is a positivity that occurs after the negativity associated with the N400  The early post-N400 positivity (EPNP) is also part of a biphasic response, but the positivity has an eariler onset than the standard PNP  Finally, the LAN and ELAN are the left-anterior negativity and early left-anterior negativity respectively  These are named for their timing, spatial distribution on the scalp, and direction of difference from the baseline  It is important to note that ERP components can potentially cancel and mask each other, and that it is difficult to precisely localize the neural activity that causes the changes in electrical potential at the electrodes where those changes are measured 
Related Work
This work is most closely related to the paper from which we get the ERP data: BIBREF0 

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. What evaluation metrics are looked at for classification tasks?
Context 2:  We used an implementation of sentiment analyzer from TextBlob to get a polarity score for each sentence  Table TABREF13 shows the distribution of positive, negative and neutral sentiments across the 3 class labels STRENGTH, WEAKNESS and SUGGESTION  It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem 
Discovering Clusters within Sentence Classes
After identifying sentences in each class, we can now answer question (1) in Section SECREF1   From 12742 sentences predicted to have label STRENGTH, we extract nouns that indicate the actual strength, and cluster them using a simple clustering algorithm which uses the cosine similarity between word embeddings of these nouns  We repeat this for the 9160 sentences with predicted label WEAKNESS or SUGGESTION as a single class  Tables TABREF15 and TABREF16 show a few representative clusters in strengths and in weaknesses, respectively  We also explored clustering 12742 STRENGTH sentences directly using CLUTO BIBREF19 and Carrot2 Lingo BIBREF20 clustering algorithms  Carrot2 Lingo discovered 167 clusters and also assigned labels to these clusters  We then generated 167 clusters using CLUTO as well  CLUTO does not generate cluster labels automatically, hence we used 5 most frequent words within the cluster as its labels  Table TABREF19 shows the largest 5 clusters by both the algorithms  It was observed that the clusters created by CLUTO were more meaningful and informative as compared to those by Carrot2 Lingo  Also, it was observed that there is some correspondence between noun clusters and sentence clusters  E g  the nouns cluster motivation expertise knowledge talent skill (Table TABREF15 ) corresponds to the CLUTO sentence cluster skill customer management knowledge team (Table TABREF19 )  But overall, users found the nouns clusters to be more meaningful than the sentence clusters 
PA along Attributes
In many organizations, PA is done from a predefined set of perspectives, which we call attributes  Each attribute covers one specific aspect of the work done by the employees  This has the advantage that we can easily compare the performance of any two employees (or groups of employees) along any given attribute  We can correlate various performance attributes and find dependencies among them  We can also cluster employees in the workforce using their supervisor ratings for each attribute to discover interesting insights into the workforce  The HR managers in the organization considered in this paper have defined 15 attributes (Table TABREF20 )  Each attribute is essentially a work item or work category described at an abstract level  For example, FUNCTIONAL_EXCELLENCE covers any tasks, goals or activities related to the software engineering life-cycle (e g , requirements analysis, design, coding, testing etc ) as well as technologies such as databases, web services and GUI 
In the example in Section SECREF4 , the first sentence (which has class STRENGTH) can be mapped to two attributes: FUNCTIONAL_EXCELLENCE and BUILDING_EFFECTIVE_TEAMS  Similarly, the third sentence (which has class WEAKNESS) can be mapped to the attribute INTERPERSONAL_EFFECTIVENESS and so forth  Thus, in order to answer the second question in Section SECREF1 , we need to map each sentence in each of the 3 classes to zero, one, two or more attributes, which is a multi-class multi-label classification problem 
We manually tagged the same 2000 sentences in Dataset D1 with attributes, where each sentence may get 0, 1, 2, etc  up to 15 class labels (this is dataset D2)  This labelled dataset contained 749, 206, 289, 207, 91, 223, 191, 144, 103, 80, 82, 42, 29, 15, 24 sentences having the class labels listed in Table TABREF20 in the same order  The number of sentences having 0, 1, 2, or more than 2 attributes are: 321, 1070, 470 and 139 respectively  We trained several multi-class multi-label classifiers on this dataset  Table TABREF21 shows the results of 5-fold cross-validation experiments on dataset D2 
Precision, Recall and F-measure for this multi-label classification are computed using a strategy similar to the one described in BIBREF21   Let INLINEFORM0 be the set of predicted labels and INLINEFORM1 be the set of actual labels for the INLINEFORM2 instance  Precision and recall for this instance are computed as follows: INLINEFORM3
It can be observed that INLINEFORM0 would be undefined if INLINEFORM1 is empty and similarly INLINEFORM2 would be undefined when INLINEFORM3 is empty  Hence, overall precision and recall are computed by averaging over all the instances except where they are undefined  Instance-level F-measure can not be computed for instances where either precision or recall are undefined  Therefore, overall F-measure is computed using the overall precision and recall 
Summarization of Peer Feedback using ILP
The PA system includes a set of peer feedback comments for each employee  To answer the third question in Section SECREF1 , we need to create a summary of all the peer feedback comments about a given employee  As an example, following are the feedback comments from 5 peers of an employee 
The individual sentences in the comments written by each peer are first identified and then POS tags are assigned to each sentence  We hypothesize that a good summary of these multiple comments can be constructed by identifying a set of important text fragments or phrases  Initially, a set of candidate phrases is extracted from these comments and a subset of these candidate phrases is chosen as the final summary, using Integer Linear Programming (ILP)  The details of the ILP formulation are shown in Table TABREF36   As an example, following is the summary generated for the above 5 peer comments 
humble nature, effective communication, technical expertise, always supportive, vast knowledge
Following rules are used to identify candidate phrases:
Various parameters are used to evaluate a candidate phrase for its importance  A candidate phrase is more important:
A complete list of parameters is described in detail in Table TABREF36  
There is a trivial constraint INLINEFORM0 which makes sure that only INLINEFORM1 out of INLINEFORM2 candidate phrases are chosen  A suitable value of INLINEFORM3 is used for each employee depending on number of candidate phrases identified across all peers (see Algorithm SECREF6 )  Another set of constraints ( INLINEFORM4 to INLINEFORM5 ) make sure that at least one phrase is selected for each of the leadership attributes  The constraint INLINEFORM6 makes sure that multiple phrases sharing the same headword are not chosen at a time  Also, single word candidate phrases are chosen only if they are adjectives or nouns with lexical category noun attribute  This is imposed by the constraint INLINEFORM7   It is important to note that all the constraints except INLINEFORM8 are soft constraints, i e  there may be feasible solutions which do not satisfy some of these constraints  But each constraint which is not satisfied, results in a penalty through the use of slack variables  These constraints are described in detail in Table TABREF36  
The objective function maximizes the total importance score of the selected candidate phrases  At the same time, it also minimizes the sum of all slack variables so that the minimum number of constraints are broken 
INLINEFORM0 : No  of candidate phrases INLINEFORM1 : No  of phrases to select as part of summary
INLINEFORM0 INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 INLINEFORM5 INLINEFORM6 INLINEFORM7 INLINEFORM8
INLINEFORM0 and INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 INLINEFORM5 INLINEFORM6
INLINEFORM0 (For determining number of phrases to select to include in summary)
Evaluation of auto-generated summaries
We considered a dataset of 100 employees, where for each employee multiple peer comments were recorded  Also, for each employee, a manual summary was generated by an HR personnel  The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score  For comparing performance of our ILP-based summarization algorithm, we explored a few summarization algorithms provided by the Sumy package  A common parameter which is required by all these algorithms is number of sentences keep in the final summary  ILP-based summarization requires a similar parameter K, which is automatically decided based on number of total candidate phrases  Assuming a sentence is equivalent to roughly 3 phrases, for Sumy algorithms, we set number of sentences parameter to the ceiling of K/3  Table TABREF51 shows average and standard deviation of ROUGE unigram f1 scores for each algorithm, over the 100 summaries  The performance of ILP-based summarization is comparable with the other algorithms, as the two sample t-test does not show statistically significant difference  Also, human evaluators preferred phrase-based summary generated by our approach to the other sentence-based summaries 
Conclusions and Further Work
In this paper, we presented an analysis of the text generated in Performance Appraisal (PA) process in a large multi-national IT company  We performed sentence classification to identify strengths, weaknesses and suggestions for improvements found in the supervisor assessments and then used clustering to discover broad categories among them  As this is non-topical classification, we found that SVM with ADWS kernel BIBREF18 produced the best results  We also used multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance  Logistic Regression classifier was observed to produce the best results for this topical classification  Finally, we proposed an ILP-based summarization technique to produce a summary of peer feedback comments for a given employee and compared it with manual summaries 
The PA process also generates much structured data, such as supervisor ratings  It is an interesting problem to compare and combine the insights from discovered from structured data and unstructured text  Also, we are planning to automatically discover any additional performance attributes to the list of 15 attributes currently used by HR 
Context 3:  In that work, the authors relate the surprisal of a word, i e  the (negative log) probability of the word appearing in its context, to each of the ERP signals we consider here  The authors do not directly train a model to predict ERPs  Instead, models of the probability distribution of each word in context are used to compute a surprisal for each word, which is input into a mixed effects regression along with word frequency, word length, word position in the sentence, and sentence position in the experiment  The effect of the surprisal is assessed using a likelihood-ratio test  In BIBREF7 , the authors take an approach similar to BIBREF0   The authors compare the explanatory power of surprisal (as computed by an LSTM or a Recurrent Neural Network Grammar (RNNG) language model) to a measure of syntactic complexity they call “distance" that counts the number of parser actions in the RNNG language model  The authors find that surprisal (as predicted by the RNNG) and distance are both significant factors in a mixed effects regression which predicts the P600, while the surprisal as computed by an LSTM is not  Unlike BIBREF0 and BIBREF7 , we do not use a linking function (e g  surprisal) to relate a language model to ERPs  We thus lose the interpretability provided by the linking function, but we are able to predict a significant proportion of the variance for all of the ERP components, where prior work could not  We interpret our results through characterization of the ERPs in terms of how they relate to each other and to eye-tracking data rather than through a linking function  The authors in BIBREF8 also use a recurrent neural network to predict neural activity directly  In that work the authors predict magnetoencephalography (MEG) activity, a close cousin to EEG, recorded while participants read a chapter of Harry Potter and the Sorcerer’s Stone BIBREF9   Their approach to characterization of processing at each MEG sensor location is to determine whether it is best predicted by the context vector of the recurrent network (prior to the current word being processed), the embedding of the current word, or the probability of the current word given the context  In future work we also intend to add these types of studies to the ERP predictions 
Discussion
In this work we find that all six of the ERP components from BIBREF0 can be predicted above chance by a model which has been pretrained using a language modeling objective and then directly trained to predict the components  This is in contrast to prior work which has successfully linked language models to the N400 BIBREF0 and P600 BIBREF7 but not the other ERP components  We also note that contrary to BIBREF7 , we find that an LSTM does contain information that can be used to predict EEG data, and in particular that it can predict the P600  We speculate that the analysis used in BIBREF7 did not find reliable effects because the language models were related to the EEG data through functions chosen a priori (the surprisal, and the `distance' metric)  These functions, though interpretable, might be interpretable at the cost of losing much of the information in the representations learned by the network 
In addition, we show through our multitask learning analysis that information is shared between ERP components, and between ERP components and behavioral data

ground_truth: Precision, Recall, F-measure, accuracy

Precision, Recall and F-measure

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 datasets are used?
Context 3:  Although these relationships must be viewed with caution until they can be verified across multiple datasets and with more variation in neural network architectures, here we consider some potential reasons for our findings  The broad point we wish to make is that by better understanding which ERP components share information with each other and with behavioral data through the type of analysis we present here (multitask learning) or other means, we can better understand what drives each ERP component and in turn the processes involved in human language comprehension 
Conclusion
We have shown that ERP components can be predicted from neural networks pretrained as language models and fine-tuned to directly predict those components  To the best of our knowledge, prior work has not successfully used statistical models to predict all of these components  Furthermore, we have shown that multitask learning benefits the prediction of ERP components and can suggest how components relate to each other  At present, these joint-training benefit relationships are only suggestive, but if these relationships ultimately lead to insights about what drives each ERP component, then the components become more useful tools for studying human language comprehension  By using multitask learning as a method of characterization, we have found some expected relationships (LAN+P600 and ELAN+P600) and several more surprising relationships  We believe that this is exactly the kind of finding that makes multitask learning an interesting exploratory technique in this area  Additionally, we have shown that information can be shared between heterogeneous types of data (eye-tracking, self-paced reading, and ERP components) in the domain of human language processing prediction, and in particular between behavioral and neural data  Given the small datasets associated with human language processing, using heterogeneous data is a potentially major advantage of a multitask approach  In future work, we will further explore what information is encoded into the model representations when neural and behavioral data are used to train neural networks, and how these representations differ from the representations in a model trained on language alone 
Acknowledgments
We thank our reviewers for their valuable feedback  This work is supported in part by National Institutes of Health grant number U01NS098969 
Appendix
Here we present a visualization (Figure FIGREF21 ) of the results presented in Table TABREF9 of the main paper, and a visualization (Figure FIGREF22 ) of a more complete set of results from which the information in Table TABREF16 of the main paper is drawn  We also show supplemental results for variants of our primary analysis on multitask learning with eye-tracking, self-paced reading time and ERP data  In the variants we modify the input representation to our decoder network to see whether the relationships between the behavioral data and neural activity appear to be consistent with different choices of encoder architectures  Additional (and more varied) choices or architectures are left to future work  The results in Table TABREF23 reflect using only the forward-encoder (rather than the bi-LSTM) in the encoder network, while the results in Table TABREF24 reflect using only the word embeddings (i e  bypassing the LSTM entirely)  While the results are clearly worse for each of these choices of architecture than for using a bi-LSTM encoder, the relationships between the behavioral data and the ERP signals is qualitatively similar  Finally, TABREF25 shows the Pearson correlation coefficient between different measures  We note that the patterns of correlation are different than the patterns of which measures benefit from joint training with each other 

ground_truth: Answer with content missing: (Whole Method and Results sections) The primary dataset we use is the ERP data collected and computed by Frank et al. (2015), and we also use behavioral data (eye-tracking data and self-paced reading times) from Frank et al. (2013) which were collected on the same set of 205 sentences.
Select:
- ERP data collected and computed by Frank et al. (2015)
- behavioral data (eye-tracking data and self-paced reading times) from Frank et al. (2013)

the ERP data: BIBREF0