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. Were other baselines tested to compare with the neural baseline?
Context 1: Introduction
Privacy policies are the documents which disclose the ways in which a company gathers, uses, shares and manages a user's data  As legal documents, they function using the principle of notice and choice BIBREF0, where companies post their policies, and theoretically, users read the policies and decide to use a company's products or services only if they find the conditions outlined in its privacy policy acceptable  Many legal jurisdictions around the world accept this framework, including the United States and the European Union BIBREF1, BIBREF2  However, the legitimacy of this framework depends upon users actually reading and understanding privacy policies to determine whether company practices are acceptable to them BIBREF3  In practice this is seldom the case BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10  This is further complicated by the highly individual and nuanced compromises that users are willing to make with their data BIBREF11, discouraging a `one-size-fits-all' approach to notice of data practices in privacy documents 
With devices constantly monitoring our environment, including our personal space and our bodies, lack of awareness of how our data is being used easily leads to problematic situations where users are outraged by information misuse, but companies insist that users have consented  The discovery of increasingly egregious uses of data by companies, such as the scandals involving Facebook and Cambridge Analytica BIBREF12, have further brought public attention to the privacy concerns of the internet and ubiquitous computing  This makes privacy a well-motivated application domain for NLP researchers, where advances in enabling users to quickly identify the privacy issues most salient to them can potentially have large real-world impact 
[1]https://play google com/store/apps/details?id=com gotokeep keep intl [2]https://play google com/store/apps/details?id=com viber voip [3]A question might not have any supporting evidence for an answer within the privacy policy 
Motivated by this need, we contribute PrivacyQA, a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations  The goal of this effort is to kickstart the development of question-answering methods for this domain, to address the (unrealistic) expectation that a large population should be reading many policies per day  In doing so, we identify several understudied challenges to our ability to answer these questions, with broad implications for systems seeking to serve users' information-seeking intent  By releasing this resource, we hope to provide an impetus to develop systems capable of language understanding in this increasingly important domain 
Related Work
Prior work has aimed to make privacy policies easier to understand  Prescriptive approaches towards communicating privacy information BIBREF21, BIBREF22, BIBREF23 have not been widely adopted by industry  Recently, there have been significant research effort devoted to understanding privacy policies by leveraging NLP techniques BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28, especially by identifying specific data practices within a privacy policy  We adopt a personalized approach to understanding privacy policies, that allows users to query a document and selectively explore content salient to them  Most similar is the PolisisQA corpus BIBREF29, which examines questions users ask corporations on Twitter  Our approach differs in several ways: 1) The PrivacyQA dataset is larger, containing 10x as many questions and answers  2) Answers are formulated by domain experts with legal training  3) PrivacyQA includes diverse question types, including unanswerable and subjective questions 
Our work is also related to reading comprehension in the open domain, which is frequently based upon Wikipedia passages BIBREF16, BIBREF17, BIBREF15, BIBREF30 and news articles BIBREF20, BIBREF31, BIBREF32  Table TABREF4 presents the desirable attributes our dataset shares with past approaches  This work is also tied into research in applying NLP approaches to legal documents BIBREF33, BIBREF34, BIBREF35, BIBREF36, BIBREF37, BIBREF38, BIBREF39  While privacy policies have legal implications, their intended audience consists of the general public rather than individuals with legal expertise  This arrangement is problematic because the entities that write privacy policies often have different goals than the audience  feng2015applying, tan-EtAl:2016:P16-1 examine question answering in the insurance domain, another specialized domain similar to privacy, where the intended audience is the general public 
Data Collection
We describe the data collection methodology used to construct PrivacyQA  With the goal of achieving broad coverage across application types, we collect privacy policies from 35 mobile applications representing a number of different categories in the Google Play Store  One of our goals is to include both policies from well-known applications, which are likely to have carefully-constructed privacy policies, and lesser-known applications with smaller install bases, whose policies might be considerably less sophisticated  Thus, setting 5 million installs as a threshold, we ensure each category includes applications with installs on both sides of this threshold  All policies included in the corpus are in English, and were collected before April 1, 2018, predating many companies' GDPR-focused BIBREF41 updates  We leave it to future studies BIBREF42 to look at the impact of the GDPR (e g , to what extent GDPR requirements contribute to making it possible to provide users with more informative answers, and to what extent their disclosures continue to omit issues that matter to users) 
Data Collection ::: Crowdsourced Question Elicitation
The intended audience for privacy policies consists of the general public  This informs the decision to elicit questions from crowdworkers on the contents of privacy policies  We choose not to show the contents of privacy policies to crowdworkers, a procedure motivated by a desire to avoid inadvertent biases BIBREF43, BIBREF44, BIBREF45, BIBREF46, BIBREF47, and encourage crowdworkers to ask a variety of questions beyond only asking questions based on practices described in the document 
Instead, crowdworkers are presented with public information about a mobile application available on the Google Play Store including its name, description and navigable screenshots  Figure FIGREF9 shows an example of our user interface  Crowdworkers are asked to imagine they have access to a trusted third-party privacy assistant, to whom they can ask any privacy question about a given mobile application  We use the Amazon Mechanical Turk platform and recruit crowdworkers who have been conferred “master” status and are located within the United States of America  Turkers are asked to provide five questions per mobile application, and are paid $2 per assignment, taking ~eight minutes to complete the task 
Data Collection ::: Answer Selection
To identify legally sound answers, we recruit seven experts with legal training to construct answers to Turker questions  Experts identify relevant evidence within the privacy policy, as well as provide meta-annotation on the question's relevance, subjectivity, OPP-115 category BIBREF49, and how likely any privacy policy is to contain the answer to the question asked 
Data Collection ::: Analysis
Table TABREF17 presents aggregate statistics of the PrivacyQA dataset  1750 questions are posed to our imaginary privacy assistant over 35 mobile applications and their associated privacy documents  As an initial step, we formulate the problem of answering user questions as an extractive sentence selection task, ignoring for now background knowledge, statistical data and legal expertise that could otherwise be brought to bear  The dataset is partitioned into a training set featuring 27 mobile applications and 1350 questions, and a test set consisting of 400 questions over 8 policy documents  This ensures that documents in training and test splits are mutually exclusive  Every question is answered by at least one expert  In addition, in order to estimate annotation reliability and provide for better evaluation, every question in the test set is answered by at least two additional experts 
Table TABREF14 describes the distribution over first words of questions posed by crowdworkers  We also observe low redundancy in the questions posed by crowdworkers over each policy, with each policy receiving ~49 94 unique questions despite crowdworkers independently posing questions  Questions are on average 8 4 words long  As declining to answer a question can be a legally sound response but is seldom practically useful, answers to questions where a minority of experts abstain to answer are filtered from the dataset  Privacy policies are ~3000 words long on average  The answers to the question asked by the users typically have ~100 words of evidence in the privacy policy document 
Data Collection ::: Analysis ::: Categories of Questions
Questions are organized under nine categories from the OPP-115 Corpus annotation scheme BIBREF49:
First Party Collection/Use: What, why and how information is collected by the service provider
Third Party Sharing/Collection: What, why and how information shared with or collected by third parties
Data Security: Protection measures for user information
Data Retention: How long user information will be stored
User Choice/Control: Control options available to users
User Access, Edit and Deletion: If/how users can access, edit or delete information
Policy Change: Informing users if policy information has been changed
International and Specific Audiences: Practices pertaining to a specific group of users
Other: General text, contact information or practices not covered by other categories 
For each question, domain experts indicate one or more relevant OPP-115 categories  We mark a category as relevant to a question if it is identified as such by at least two annotators  If no such category exists, the category is marked as `Other' if atleast one annotator has identified the `Other' category to be relevant  If neither of these conditions is satisfied, we label the question as having no agreement  The distribution of questions in the corpus across OPP-115 categories is as shown in Table TABREF16  First party and third party related questions are the largest categories, forming nearly 66 4% of all questions asked to the privacy assistant 
Data Collection ::: Analysis ::: Answer Validation
When do experts disagree? We would like to analyze the reasons for potential disagreement on the annotation task, to ensure disagreements arise due to valid differences in opinion rather than lack of adequate specification in annotation guidelines  It is important to note that the annotators are experts rather than crowdworkers  Accordingly, their judgements can be considered valid, legally-informed opinions even when their perspectives differ  For the sake of this question we randomly sample 100 instances in the test data and analyze them for likely reasons for disagreements  We consider a disagreement to have occurred when more than one expert does not agree with the majority consensus  By disagreement we mean there is no overlap between the text identified as relevant by one expert and another 
We find that the annotators agree on the answer for 74% of the questions, even if the supporting evidence they identify is not identical i e full overlap  They disagree on the remaining 26%  Sources of apparent disagreement correspond to situations when different experts: have differing interpretations of question intent (11%) (for example, when a user asks 'who can contact me through the app', the questions admits multiple interpretations, including seeking information about the features of the app, asking about first party collection/use of data or asking about third party collection/use of data), identify different sources of evidence for questions that ask if a practice is performed or not (4%), have differing interpretations of policy content (3%), identify a partial answer to a question in the privacy policy (2%) (for example, when the user asks `who is allowed to use the app' a majority of our annotators decline to answer, but the remaining annotators highlight partial evidence in the privacy policy which states that children under the age of 13 are not allowed to use the app), and other legitimate sources of disagreement (6%) which include personal subjective views of the annotators (for example, when the user asks `is my DNA information used in any way other than what is specified', some experts consider the boilerplate text of the privacy policy which states that it abides to practices described in the policy document as sufficient evidence to answer this question, whereas others do not) 
Experimental Setup
We evaluate the ability of machine learning methods to identify relevant evidence for questions in the privacy domain  We establish baselines for the subtask of deciding on the answerability (§SECREF33) of a question, as well as the overall task of identifying evidence for questions from policies (§SECREF37)  We describe aspects of the question that can render it unanswerable within the privacy domain (§SECREF41) 
Experimental Setup ::: Answerability Identification Baselines
We define answerability identification as a binary classification task, evaluating model ability to predict if a question can be answered, given a question in isolation  This can serve as a prior for downstream question-answering  We describe three baselines on the answerability task, and find they considerably improve performance over a majority-class baseline 
SVM: We define 3 sets of features to characterize each question  The first is a simple bag-of-words set of features over the question (SVM-BOW), the second is bag-of-words features of the question as well as length of the question in words (SVM-BOW + LEN), and lastly we extract bag-of-words features, length of the question in words as well as part-of-speech tags for the question (SVM-BOW + LEN + POS)  This results in vectors of 200, 201 and 228 dimensions respectively, which are provided to an SVM with a linear kernel 
CNN: We utilize a CNN neural encoder for answerability prediction  We use GloVe word embeddings BIBREF50, and a filter size of 5 with 64 filters to encode questions 
BERT: BERT BIBREF51 is a bidirectional transformer-based language-model BIBREF52  We fine-tune BERT-base on our binary answerability identification task with a learning rate of 2e-5 for 3 epochs, with a maximum sequence length of 128 
Experimental Setup ::: Privacy Question Answering
Our goal is to identify evidence within a privacy policy for questions asked by a user  This is framed as an answer sentence selection task, where models identify a set of evidence sentences from all candidate sentences in each policy 
Experimental Setup ::: Privacy Question Answering ::: Evaluation Metric
Our evaluation metric for answer-sentence selection is sentence-level F1, implemented similar to BIBREF30, BIBREF16  Precision and recall are implemented by measuring the overlap between predicted sentences and sets of gold-reference sentences  We report the average of the maximum F1 from each n$-$1 subset, in relation to the heldout reference 
Experimental Setup ::: Privacy Question Answering ::: Baselines
We describe baselines on this task, including a human performance baseline 
No-Answer Baseline (NA) : Most of the questions we receive are difficult to answer in a legally-sound way on the basis of information present in the privacy policy  We establish a simple baseline to quantify the effect of identifying every question as unanswerable 
Word Count Baseline : To quantify the effect of using simple lexical matching to answer the questions, we retrieve the top candidate policy sentences for each question using a word count baseline BIBREF53, which counts the number of question words that also appear in a sentence  We include the top 2, 3 and 5 candidates as baselines 
BERT: We implement two BERT-based baselines BIBREF51 for evidence identification  First, we train BERT on each query-policy sentence pair as a binary classification task to identify if the sentence is evidence for the question or not (Bert)  We also experiment with a two-stage classifier, where we separately train the model on questions only to predict answerability  At inference time, if the answerable classifier predicts the question is answerable, the evidence identification classifier produces a set of candidate sentences (Bert + Unanswerable) 
Human Performance: We pick each reference answer provided by an annotator, and compute the F1 with respect to the remaining references, as described in section 4 2 1  Each reference answer is treated as the prediction, and the remaining n-1 answers are treated as the gold reference  The average of the maximum F1 across all reference answers is computed as the human baseline 
Results and Discussion
The results of the answerability baselines are presented in Table TABREF31, and on answer sentence selection in Table TABREF32  We observe that bert exhibits the best performance on a binary answerability identification task  However, most baselines considerably exceed the performance of a majority-class baseline  This suggests considerable information in the question, indicating it's possible answerability within this domain 
Table TABREF32 describes the performance of our baselines on the answer sentence selection task  The No-answer (NA) baseline performs at 28 F1, providing a lower bound on performance at this task  We observe that our best-performing baseline, Bert + Unanswerable achieves an F1 of 39 8  This suggest that bert is capable of making some progress towards answering questions in this difficult domain, while still leaving considerable headroom for improvement to reach human performance  Bert + Unanswerable performance suggests that incorporating information about answerability can help in this difficult domain  We examine this challenging phenomena of unanswerability further in Section  
Results and Discussion ::: Error Analysis
Disagreements are analyzed based on the OPP-115 categories of each question (Table TABREF34)  We compare our best performing BERT variant against the NA model and human performance  We observe significant room for improvement across all categories of questions but especially for first party, third party and data retention categories 
We analyze the performance of our strongest BERT variant, to identify classes of errors and directions for future improvement (Table 8)  We observe that a majority of answerability mistakes made by the BERT model are questions which are in fact answerable, but are identified as unanswerable by BERT  We observe that BERT makes 124 such mistakes on the test set  We collect expert judgments on relevance, subjectivity , silence and information about how likely the question is to be answered from the privacy policy from our experts  We find that most of these mistakes are relevant questions  However many of them were identified as subjective by the annotators, and at least one annotator marked 19 of these questions as having no answer within the privacy policy  However, only 6 of these questions were unexpected or do not usually have an answer in privacy policies  These findings suggest that a more nuanced understanding of answerability might help improve model performance in his challenging domain 
Results and Discussion ::: What makes Questions Unanswerable?
We further ask legal experts to identify potential causes of unanswerability of questions  This analysis has considerable implications  While past work BIBREF17 has treated unanswerable questions as homogeneous, a question answering system might wish to have different treatments for different categories of `unanswerable' questions  The following factors were identified to play a role in unanswerability:
Incomprehensibility: If a question is incomprehensible to the extent that its meaning is not intelligible 
Relevance: Is this question in the scope of what could be answered by reading the privacy policy 
Ill-formedness: Is this question ambiguous or vague  An ambiguous statement will typically contain expressions that can refer to multiple potential explanations, whereas a vague statement carries a concept with an unclear or soft definition 
Silence: Other policies answer this type of question but this one does not 
Atypicality: The question is of a nature such that it is unlikely for any policy policy to have an answer to the question 
Our experts attempt to identify the different `unanswerable' factors for all 573 such questions in the corpus  4 18% of the questions were identified as being incomprehensible (for example, `any difficulties to occupy the privacy assistant')  Amongst the comprehendable questions, 50% were identified as likely to have an answer within the privacy policy, 33 1% were identified as being privacy-related questions but not within the scope of a privacy policy (e g , 'has Viber had any privacy breaches in the past?') and 16 9% of questions were identified as completely out-of-scope (e g , `'will the app consume much space?')  In the questions identified as relevant, 32% were ill-formed questions that were phrased by the user in a manner considered vague or ambiguous  Of the questions that were both relevant as well as `well-formed', 95 7% of the questions were not answered by the policy in question but it was reasonable to expect that a privacy policy would contain an answer  The remaining 4 3% were described as reasonable questions, but of a nature generally not discussed in privacy policies  This suggests that the answerability of questions over privacy policies is a complex issue, and future systems should consider each of these factors when serving user's information seeking intent 
We examine a large-scale dataset of “natural” unanswerable questions BIBREF54 based on real user search engine queries to identify if similar unanswerability factors exist  It is important to note that these questions have previously been filtered, according to a criteria for bad questions defined as “(questions that are) ambiguous, incomprehensible, dependent on clear false presuppositions, opinion-seeking, or not clearly a request for factual information ” Annotators made the decision based on the content of the question without viewing the equivalent Wikipedia page  We randomly sample 100 questions from the development set which were identified as unanswerable, and find that 20% of the questions are not questions (e g , “all I want for christmas is you mariah carey tour”)  12% of questions are unlikely to ever contain an answer on Wikipedia, corresponding closely to our atypicality category  3% of questions are unlikely to have an answer anywhere (e g , `what guides Santa home after he has delivered presents?')  7% of questions are incomplete or open-ended (e g , `the south west wind blows across nigeria between')  3% of questions have an unresolvable coreference (e g , `how do i get to Warsaw Missouri from here')  4% of questions are vague, and a further 7% have unknown sources of error  2% still contain false presuppositions (e g , `what is the only fruit that does not have seeds?') and the remaining 42% do not have an answer within the document  This reinforces our belief that though they have been understudied in past work, any question answering system interacting with real users should expect to receive such unanticipated and unanswerable questions 
Conclusion
We present PrivacyQA, the first significant corpus of privacy policy questions and more than 3500 expert annotations of relevant answers  The goal of this work is to promote question-answering research in the specialized privacy domain, where it can have large real-world impact  Strong neural baselines on PrivacyQA achieve a performance of only 39 8 F1 on this corpus, indicating considerable room for future research  Further, we shed light on several important considerations that affect the answerability of questions  We hope this contribution leads to multidisciplinary efforts to precisely understand user intent and reconcile it with information in policy documents, from both the privacy and NLP communities 
Acknowledgements
This research was supported in part by grants from the National Science Foundation Secure and Trustworthy Computing program (CNS-1330596, CNS-1330214, CNS-15-13957, CNS-1801316, CNS-1914486, CNS-1914444) and a DARPA Brandeis grant on Personalized Privacy Assistants (FA8750-15-2-0277)  The US Government is authorized to reproduce and distribute reprints for Governmental purposes not withstanding any copyright notation  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the NSF, DARPA, or the US Government  The authors would like to extend their gratitude to Elias Wright, Gian Mascioli, Kiara Pillay, Harrison Kay, Eliel Talo, Alexander Fagella and N  Cameron Russell for providing their valuable expertise and insight to this effort  The authors are also grateful to Eduard Hovy, Lorrie Cranor, Florian Schaub, Joel Reidenberg, Aditya Potukuchi and Igor Shalyminov for helpful discussions related to this work, and to the three anonymous reviewers of this draft for their constructive feedback  Finally, the authors would like to thank all crowdworkers who consented to participate in this study 
Context 2: Introduction
The irony is a kind of figurative language, which is widely used on social media BIBREF0   The irony is defined as a clash between the intended meaning of a sentence and its literal meaning BIBREF1   As an important aspect of language, irony plays an essential role in sentiment analysis BIBREF2 , BIBREF0 and opinion mining BIBREF3 , BIBREF4  
Although some previous studies focus on irony detection, little attention is paid to irony generation  As ironies can strengthen sentiments and express stronger emotions, we mainly focus on generating ironic sentences  Given a non-ironic sentence, we implement a neural network to transfer it to an ironic sentence and constrain the sentiment polarity of the two sentences to be the same  For example, the input is “I hate it when my plans get ruined" which is negative in sentiment polarity and the output should be ironic and negative in sentiment as well, such as “I like it when my plans get ruined"  The speaker uses “like" to be ironic and express his or her negative sentiment  At the same time, our model can preserve contents which are irrelevant to sentiment polarity and irony  According to the categories mentioned in BIBREF5 , irony can be classified into 3 classes: verbal irony by means of a polarity contrast, the sentences containing expression whose polarity is inverted between the intended and the literal evaluation; other types of verbal irony, the sentences that show no polarity contrast between the literal and intended meaning but are still ironic; and situational irony, the sentences that describe situations that fail to meet some expectations  As ironies in the latter two categories are obscure and hard to understand, we decide to only focus on ironies in the first category in this work  For example, our work can be specifically described as: given a sentence “I hate to be ignored", we train our model to generate an ironic sentence such as “I love to be ignored"  Although there is “love" in the generated sentence, the speaker still expresses his or her negative sentiment by irony  We also make some explorations in the transformation from ironic sentences to non-ironic sentences at the end of our work  Because of the lack of previous work and baselines on irony generation, we implement our model based on style transfer  Our work will not only provide the first large-scale irony dataset but also make our model as a benchmark for the irony generation 
Recently, unsupervised style transfer becomes a very popular topic  Many state-of-the-art studies try to solve the task with sequence-to-sequence (seq2seq) framework  There are three main ways to build up models  The first is to learn a latent style-independent content representation and generate sentences with the content representation and another style BIBREF6 , BIBREF7   The second is to directly transfer sentences from one style to another under the control of classifiers and reinforcement learning BIBREF8   The third is to remove style attribute words from the input sentence and combine the remaining content with new style attribute words BIBREF9 , BIBREF10   The first method usually obtains better performances via adversarial training with discriminators  The style-independent content representation, nevertheless, is not easily obtained BIBREF11 , which results in poor performances  The second method is suitable for complex styles which are difficult to model and describe  The model can learn the deep semantic features by itself but sometimes the model is sensitive to parameters and hard to train  The third method succeeds to preserve content but cannot work for some complex styles such as democratic and republican  Sentences with those styles usually do not have specific style attribute words  Unfortunately, due to the lack of large irony dataset and difficulties of modeling ironies, there has been little work trying to generate ironies based on seq2seq framework as far as we know  Inspired by methods for style transfer, we decide to implement a specifically designed model based on unsupervised style transfer to explore irony generation 
In this paper, in order to address the lack of irony data, we first crawl over 2M tweets from twitter to build a dataset with 262,755 ironic and 112,330 non-ironic tweets  Then, due to the lack of parallel data, we propose a novel model to transfer non-ironic sentences to ironic sentences in an unsupervised way  As ironic style is hard to model and describe, we implement our model with the control of classifiers and reinforcement learning  Different from other studies in style transfer, the transformation from non-ironic to ironic sentences has to preserve sentiment polarity as mentioned above  Therefore, we not only design an irony reward to control the irony accuracy and implement denoising auto-encoder and back-translation to control content preservation but also design a sentiment reward to control sentiment preservation 
Experimental results demonstrate that our model achieves a high irony accuracy with well-preserved sentiment and content  The contributions of our work are as follows:
Related Work
Style Transfer: As irony is a complicated style and hard to model with some specific style attribute words, we mainly focus on studies without editing style attribute words 
Some studies are trying to disentangle style representation from content representation  In BIBREF12 , authors leverage adversarial networks to learn separate content representations and style representations  In BIBREF13 and BIBREF6 , researchers combine variational auto-encoders (VAEs) with style discriminators 
However, some recent studies BIBREF11 reveal that the disentanglement of content and style representations may not be achieved in practice  Therefore, some other research studies BIBREF9 , BIBREF10 strive to separate content and style by removing stylistic words  Nonetheless, many non-ironic sentences do not have specific stylistic words and as a result, we find it difficult to transfer non-ironic sentences to ironic sentences through this way in practice 
Besides, some other research studies do not disentangle style from content but directly learn representations of sentences  In BIBREF8 , authors propose a dual reinforcement learning framework without separating content and style representations  In BIBREF7 , researchers utilize a machine translation model to learn a sentence representation preserving the meaning of the sentence but reducing stylistic properties  In this method, the quality of generated sentences relies on the performance of classifiers to a large extent  Meanwhile, such models are usually sensitive to parameters and difficult to train  In contrast, we combine a pre-training process with reinforcement learning to build up a stable language model and design special rewards for our task 
Irony Detection: With the development of social media, irony detection becomes a more important task  Methods for irony detection can be mainly divided into two categories: methods based on feature engineering and methods based on neural networks 
As for methods based on feature engineering, In BIBREF1 , authors investigate pragmatic phenomena and various irony markers  In BIBREF14 , researchers leverage a combination of sentiment, distributional semantic and text surface features  Those models rely on hand-crafted features and are hard to implement 
When it comes to methods based on neural networks, long short-term memory (LSTM) BIBREF15 network is widely used and is very efficient for irony detection  In BIBREF16 , a tweet is divided into two segments and a subtract layer is implemented to calculate the difference between two segments in order to determine whether the tweet is ironic  In BIBREF17 , authors utilize a recurrent neural network with Bi-LSTM and self-attention without hand-crafted features  In BIBREF18 , researchers propose a system based on a densely connected LSTM network 
Our Dataset
In this section, we describe how we build our dataset with tweets  First, we crawl over 2M tweets from twitter using GetOldTweets-python  We crawl English tweets from 04/09/2012 to /12/18/2018  We first remove all re-tweets and use langdetect to remove all non-English sentences  Then, we remove hashtags attached at the end of the tweets because they are usually not parts of sentences and will confuse our language model  After that, we utilize Ekphrasis to process tweets  We remove URLs and restore remaining hashtags, elongated words, repeated words, and all-capitalized words  To simplify our dataset, We replace all “ INLINEFORM0 money INLINEFORM1 " and “ INLINEFORM2 time INLINEFORM3 " tokens with “ INLINEFORM4 number INLINEFORM5 " token when using Ekphrasis  And we delete sentences whose lengths are less than 10 or greater than 40  In order to restore abbreviations, we download an abbreviation dictionary from webopedia and restore abbreviations to normal words or phrases according to the dictionary  Finally, we remove sentences which have more than two rare words (appearing less than three times) in order to constrain the size of vocabulary  Finally, we get 662,530 sentences after pre-processing 
As neural networks are proved effective in irony detection, we decide to implement a neural classifier in order to classify the sentences into ironic and non-ironic sentences  However, the only high-quality irony dataset we can obtain is the dataset of Semeval-2018 Task 3 and the dataset is pretty small, which will cause overfitting to complex models  Therefore, we just implement a simple one-layer RNN with LSTM cell to classify pre-processed sentences into ironic sentences and non-ironic sentences because LSTM networks are widely used in irony detection  We train the model with the dataset of Semeval-2018 Task 3  After classification, we get 262,755 ironic sentences and 399,775 non-ironic sentences  According to our observation, not all non-ironic sentences are suitable to be transferred into ironic sentences  For example, “just hanging out   watching   is it monday yet" is hard to transfer because it does not have an explicit sentiment polarity  So we remove all interrogative sentences from the non-ironic sentences and only obtain the sentences which have words expressing strong sentiments  We evaluate the sentiment polarity of each word with TextBlob and we view those words with sentiment scores greater than 0 5 or less than -0 5 as words expressing strong sentiments  Finally, we build our irony dataset with 262,755 ironic sentences and 102,330 non-ironic sentences 
[t] Irony Generation Algorithm
INLINEFORM0 pre-train with auto-encoder Pre-train INLINEFORM1 , INLINEFORM2 with INLINEFORM3 using MLE based on Eq  EQREF16 Pre-train INLINEFORM4 , INLINEFORM5 with INLINEFORM6 using MLE based on Eq  EQREF17 INLINEFORM7 pre-train with back-translation Pre-train INLINEFORM8 , INLINEFORM9 , INLINEFORM10 , INLINEFORM11 with INLINEFORM12 using MLE based on Eq  EQREF19 Pre-train INLINEFORM13 , INLINEFORM14 , INLINEFORM15 , INLINEFORM16 with INLINEFORM17 using MLE based on Eq  EQREF20
INLINEFORM0 train with RL each epoch e = 1, 2,    , INLINEFORM1 INLINEFORM2 train non-irony2irony with RL INLINEFORM3 in N INLINEFORM4 update INLINEFORM5 , INLINEFORM6 , using INLINEFORM7 based on Eq  EQREF29 INLINEFORM8 back-translation INLINEFORM9 INLINEFORM10 INLINEFORM11 update INLINEFORM12 , INLINEFORM13 , INLINEFORM14 , INLINEFORM15 using MLE based on Eq  EQREF19 INLINEFORM16 train irony2non-irony with RL INLINEFORM17 in I INLINEFORM18 update INLINEFORM19 , INLINEFORM20 , using INLINEFORM21 similar to Eq  EQREF29 INLINEFORM22 back-translation INLINEFORM23 INLINEFORM24 INLINEFORM25 update INLINEFORM26 , INLINEFORM27 , INLINEFORM28 , INLINEFORM29 using MLE based on Eq  EQREF20
Our Method
Given two non-parallel corpora: non-ironic corpus N={ INLINEFORM0 , INLINEFORM1 ,    , INLINEFORM2 } and ironic corpus I={ INLINEFORM3 , INLINEFORM4 ,    , INLINEFORM5 }, the goal of our irony generation model is to generate an ironic sentence from a non-ironic sentence while preserving the content and sentiment polarity of the source input sentence  We implement an encoder-decoder framework where two encoders are utilized to encode ironic sentences and non-ironic sentences respectively and two decoders are utilized to decode ironic sentences and non-ironic sentences from latent representations respectively  In order to enforce a shared latent space, we share two layers on both the encoder side and the decoder side  Our model architecture is illustrated in Figure FIGREF13   We denote irony encoder as INLINEFORM6 , irony decoder as INLINEFORM7 and non-irony encoder as INLINEFORM8 , non-irony decoder as INLINEFORM9   Their parameters are INLINEFORM10 , INLINEFORM11 , INLINEFORM12 and INLINEFORM13  
Our irony generation algorithm is shown in Algorithm SECREF3   We first pre-train our model using denoising auto-encoder and back-translation to build up language models for both styles (section SECREF14 )  Then we implement reinforcement learning to train the model to transfer sentences from one style to another (section SECREF21 )  Meanwhile, to achieve content preservation, we utilize back-translation for one time in every INLINEFORM0 time steps 
Pretraining
In order to build up our language model and preserve the content, we apply the auto-encoder model  To prevent the model from simply copying the input sentence, we randomly add some noises in the input sentence
Context 3: Introduction
Deep Neural Networks (DNN) have been widely employed in industry for solving various Natural Language Processing (NLP) tasks, such as text classification, sequence labeling, question answering, etc  However, when engineers apply DNN models to address specific NLP tasks, they often face the following challenges 
The above challenges often hinder the productivity of engineers, and result in less optimal solutions to their given tasks  This motivates us to develop an NLP toolkit for DNN models, which facilitates engineers to develop DNN approaches  Before designing this NLP toolkit, we conducted a survey among engineers and identified a spectrum of three typical personas 
To satisfy the requirements of all the above three personas, the NLP toolkit has to be generic enough to cover as many tasks as possible  At the same time, it also needs to be flexible enough to allow alternative network architectures as well as customized modules  Therefore, we analyzed the NLP jobs submitted to a commercial centralized GPU cluster  Table TABREF11 showed that about 87 5% NLP related jobs belong to a few common tasks, including sentence classification, text matching, sequence labeling, MRC, etc  It further suggested that more than 90% of the networks were composed of several common components, such as embedding, CNN/RNN, Transformer and so on 
Based on the above observations, we developed NeuronBlocks, a DNN toolkit for NLP tasks  The basic idea is to provide two layers of support to the engineers  The upper layer targets common NLP tasks  For each task, the toolkit contains several end-to-end network templates, which can be immediately instantiated with simple configuration  The bottom layer consists of a suite of reusable and standard components, which can be adopted as building blocks to construct networks with complex architecture  By following the interface guidelines, users can also contribute to this gallery of components with their own modules 
The technical contributions of NeuronBlocks are summarized into the following three aspects 
Related Work
There are several general-purpose deep learning frameworks, such as TensorFlow, PyTorch and Keras, which have gained popularity in NLP community  These frameworks offer huge flexibility in DNN model design and support various NLP tasks  However, building models under these frameworks requires a large overhead of mastering these framework details  Therefore, higher level abstraction to hide the framework details is favored by many engineers 
There are also several popular deep learning toolkits in NLP, including OpenNMT BIBREF0 , AllenNLP BIBREF1 etc  OpenNMT is an open-source toolkit mainly targeting neural machine translation or other natural language generation tasks  AllenNLP provides several pre-built models for NLP tasks, such as semantic role labeling, machine comprehension, textual entailment, etc  Although these toolkits reduce the development cost, they are limited to certain tasks, and thus not flexible enough to support new network architectures or new components 
Design
The Neuronblocks is built on PyTorch  The overall framework is illustrated in Figure FIGREF16   It consists of two layers: the Block Zoo and the Model Zoo  In Block Zoo, the most commonly used components of deep neural networks are categorized into several groups according to their functions  Within each category, several alternative components are encapsulated into standard and reusable blocks with a consistent interface  These blocks serve as basic and exchangeable units to construct complex network architectures for different NLP tasks  In Model Zoo, the most popular NLP tasks are identified  For each task, several end-to-end network templates are provided in the form of JSON configuration files  Users can simply browse these configurations and choose one or more to instantiate  The whole task can be completed without any coding efforts 
Block Zoo
We recognize the following major functional categories of neural network components  Each category covers as many commonly used modules as possible  The Block Zoo is an open framework, and more modules can be added in the future 
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Embedding Layer: Word/character embedding and extra handcrafted feature embedding such as pos-tagging are supported 
Neural Network Layers: Block zoo provides common layers like RNN, CNN, QRNN BIBREF2 , Transformer BIBREF3 , Highway network, Encoder Decoder architecture, etc  Furthermore, attention mechanisms are widely used in neural networks  Thus we also support multiple attention layers, such as Linear/Bi-linear Attention, Full Attention BIBREF4 , Bidirectional attention flow BIBREF5 , etc  Meanwhile, regularization layers such as Dropout, Layer Norm, Batch Norm, etc are also supported for improving generalization ability 
Loss Function: Besides of the loss functions built in PyTorch, we offer more options such as Focal Loss BIBREF6  
Metrics: For classification task, AUC, Accuracy, Precision/Recall, F1 metrics are supported  For sequence labeling task, F1/Accuracy are supported  For knowledge distillation task, MSE/RMSE are supported  For MRC task, ExactMatch/F1 are supported 
Model Zoo
In NeuronBlocks, we identify four types of most popular NLP tasks  For each task, we provide various end-to-end network templates 
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Text Classification and Matching  Tasks such as domain/intent classification, question answer matching are supported 
Sequence Labeling  Predict each token in a sequence into predefined types  Common tasks include NER, POS tagging, Slot tagging, etc 
Knowledge Distillation BIBREF7   Teacher-Student based knowledge distillation is one common approach for model compression  NeuronBlocks provides knowledge distillation template to improve the inference speed of heavy DNN models like BERT/GPT 
Extractive Machine Reading Comprehension  Given a pair of question and passage, predict the start and end positions of the answer spans in the passage 
User Interface
NeuronBlocks provides convenient user interface for users to build, train, and test DNN models  The details are described in the following 
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I/O interface  This part defines model input/output, such as training data, pre-trained models/embeddings, model saving path, etc 
Model Architecture interface  This is the key part of the configuration file, which defines the whole model architecture  Figure FIGREF19 shows an example of how to specify a model architecture using the blocks in NeuronBlocks  To be more specific, it consists of a list of layers/blocks to construct the architecture, where the blocks are supplied in the gallery of Block Zoo 
Training Parameters interface  In this part, the model optimizer as well as all other training hyper parameters are indicated 
Workflow
Figure FIGREF34 shows the workflow of building DNN models in NeuronBlocks  Users only need to write a JSON configuration file  They can either instantiate an existing template from Model Zoo, or construct a new architecture based on the blocks from Block Zoo  This configuration file is shared across training, test, and prediction  For model hyper-parameter tuning or architecture modification, users just need to change the JSON configuration file  Advanced users can also contribute novel customized blocks into Block Zoo, as long as they follow the same interface guidelines with the existing blocks  These new blocks can be further shared across all users for model architecture design  Moreover, NeuronBlocks has flexible platform support, such as GPU/CPU, GPU management platforms like PAI 
Experiments
To verify the performance of NeuronBlocks, we conducted extensive experiments for common NLP tasks on public data sets including CoNLL-2003 BIBREF14 , GLUE benchmark BIBREF13 , and WikiQA corpus BIBREF15   The experimental results showed that the models built with NeuronBlocks can achieve reliable and competitive results on various tasks, with productivity greatly improved 
Sequence Labeling
For sequence labeling task, we evaluated NeuronBlocks on CoNLL-2003 BIBREF14 English NER dataset, following most works on the same task  This dataset includes four types of named entities, namely, PERSON, LOCATION, ORGANIZATION, and MISC  We adopted the BIOES tagging scheme instead of IOB, as many previous works indicated meaningful improvement with BIOES scheme BIBREF16 , BIBREF17   Table TABREF28 shows the results on CoNLL-2003 Englist testb dataset, with 12 different combinations of network layers/blocks, such as word/character embedding, CNN/LSTM and CRF  The results suggest that the flexible combination of layers/blocks in NeuronBlocks can easily reproduce the performance of original models, with comparative or slightly better performance 
GLUE Benchmark
The General Language Understanding Evaluation (GLUE) benchmark BIBREF13 is a collection of natural language understanding tasks  We experimented on the GLUE benchmark tasks using BiLSTM and Attention based models  As shown in Table TABREF29 , the models built by NeuronBlocks can achieve competitive or even better results on GLUE tasks with minimal coding efforts 
Knowledge Distillation
We evaluated Knowledge Distillation task in NeuronBlocks on a dataset collected from one commercial search engine  We refer to this dataset as Domain Classification Dataset  Each sample in this dataset consists of two parts, i e , a question and a binary label indicating whether the question belongs to a specific domain  Table TABREF36 shows the results, where Area Under Curve (AUC) metric is used as the performance evaluation criteria and Queries per Second (QPS) is used to measure inference speed  By knowledge distillation training approach, the student model by NeuronBlocks managed to get 23-27 times inference speedup with only small performance regression compared with BERTbase fine-tuned classifier 
WikiQA
The WikiQA corpus BIBREF15 is a publicly available dataset for open-domain question answering  This dataset contains 3,047 questions from Bing query logs, each associated with some candidate answer sentences from Wikipedia  We conducted experiments on WikiQA dataset using CNN, BiLSTM, and Attention based models  The results are shown in Table TABREF41   The models built in NeuronBlocks achieved competitive or even better results with simple model configurations 
Conclusion and Future Work
In this paper, we introduce NeuronBlocks, a DNN toolkit for NLP tasks built on PyTorch  NeuronBlocks targets three types of engineers, and provides a two-layer solution to satisfy the requirements from all three types of users  To be more specific, the Model Zoo consists of various templates for the most common NLP tasks, while the Block Zoo supplies a gallery of alternative layers/modules for the networks  Such design achieves a balance between generality and flexibility  Extensive experiments have verified the effectiveness of this approach  NeuronBlocks has been widely used in a product team of a commercial search engine, and significantly improved the productivity for developing NLP DNN approaches 
As an open-source toolkit, we will further extend it in various directions  The following names a few examples 

ground_truth: SVM, No-Answer Baseline (NA) , Word Count Baseline, Human Performance

No-Answer Baseline (NA), Word Count Baseline, Human Performance

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 experiments are conducted?
Context 2:  Specifically, for every word in the input sentence, there is 10% chance that we delete it, 10 % chance that we duplicate it, 10% chance that we swap it with the next word, or it remains unchanged  We first encode the input sentence INLINEFORM0 or INLINEFORM1 with respective encoder INLINEFORM2 or INLINEFORM3 to obtain its latent representation INLINEFORM4 or INLINEFORM5 and reconstruct the input sentence with the latent representation and respective decoder  So we can get the reconstruction loss for auto-encoder INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1
In addition to denoising auto-encoder, we implement back-translation BIBREF19 to generate a pseudo-parallel corpus  Suppose our model takes non-ironic sentence INLINEFORM0 as input  We first encode INLINEFORM1 with INLINEFORM2 to obtain its latent representation INLINEFORM3 and decode the latent representation with INLINEFORM4 to get a transferred sentence INLINEFORM5   Then we encode INLINEFORM6 with INLINEFORM7 and decode its latent representation with INLINEFORM8 to reconstruct the original input sentence INLINEFORM9   Therefore, our reconstruction loss for back-translation INLINEFORM10 : DISPLAYFORM0
And if our model takes ironic sentence INLINEFORM0 as input, we can get the reconstruction loss for back-translation as: DISPLAYFORM0
Reinforcement Learning
Since the gold transferred result of input is unavailable, we cannot evaluate the quality of the generated sentence directly  Therefore, we implement reinforcement learning and elaborately design two rewards to describe the irony accuracy and sentiment preservation, respectively 
A pre-trained binary irony classifier based on CNN BIBREF20 is used to evaluate how ironic a sentence is  We denote the parameter of the classifier as INLINEFORM0 and it is fixed during the training process 
In order to facilitate the transformation, we design the irony reward as the difference between the irony score of the input sentence and that of the output sentence  Formally, when we input a non-ironic sentence INLINEFORM0 and transfer it to an ironic sentence INLINEFORM1 , our irony reward is defined as: DISPLAYFORM0
where INLINEFORM0 denotes ironic style and INLINEFORM1 is the probability of that a sentence INLINEFORM2 is ironic 
To preserve the sentiment polarity of the input sentence, we also need to use classifiers to evaluate the sentiment polarity of the sentences  However, the sentiment analysis of ironic sentences and non-ironic sentences are different  In the case of figurative languages such as irony, sarcasm or metaphor, the sentiment polarity of the literal meaning may differ significantly from that of the intended figurative meaning BIBREF0   As we aim to train our model to transfer sentences from non-ironic to ironic, using only one classifier is not enough  As a result, we implement two pre-trained sentiment classifiers for non-ironic sentences and ironic sentences respectively  We denote the parameter of the sentiment classifier for ironic sentences as INLINEFORM0 and that of the sentiment classifier for non-ironic sentences as INLINEFORM1  
A challenge, when we implement two classifiers to evaluate the sentiment polarity, is that the two classifiers trained with different datasets may have different distributions of scores  That means we cannot directly calculate the sentiment reward with scores applied by two classifiers  To alleviate this problem and standardize the prediction results of two classifiers, we set a threshold for each classifier and subtract the respective threshold from scores applied by the classifier to obtain the comparative sentiment polarity score  We get the optimal threshold by maximizing the ability of the classifier according to the distribution of our training data 
We denote the threshold of ironic sentiment classifier as INLINEFORM0 and the threshold of non-ironic sentiment classifier as INLINEFORM1   The standardized sentiment score is defined as INLINEFORM2 and INLINEFORM3 where INLINEFORM4 denotes the positive sentiment polarity and INLINEFORM5 is the probability of that a sentence is positive in sentiment polarity 
As mentioned above, the input sentence and the generated sentence should express the same sentiment  For example, if we input a non-ironic sentence “I hate to be ignored" which is negative in sentiment polarity, the generated ironic sentence should be also negative, such as “I love to be ignored"  To achieve sentiment preservation, we design the sentiment reward as that one minus the absolute value of the difference between the standardized sentiment score of the input sentence and that of the generated sentence  Formally, when we input a non-ironic sentence INLINEFORM0 and transfer it to an ironic sentence INLINEFORM1 , our sentiment reward is defined as: DISPLAYFORM0
To encourage our model to focus on both the irony accuracy and the sentiment preservation, we apply the harmonic mean of irony reward and sentiment reward: DISPLAYFORM0
Policy Gradient
The policy gradient algorithm BIBREF21 is a simple but widely-used algorithm in reinforcement learning  It is used to maximize the expected reward INLINEFORM0   The objective function to minimize is defined as: DISPLAYFORM0
where INLINEFORM0 , INLINEFORM1 is the reward of INLINEFORM2 and INLINEFORM3 is the input size 
Training Details
INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 in our model are Transformers BIBREF22 with 4 layers and 2 shared layers  The word embeddings of 128 dimensions are learned during the training process  Our maximum sentence length is set as 40  The optimizer is Adam BIBREF23 and the learning rate is INLINEFORM4   The batch size is 32 and harmonic weight INLINEFORM5 in Eq 9 is 0 5  We set the interval INLINEFORM6 as 200  The model is pre-trained for 6 epochs and trained for 15 epochs for reinforcement learning 
Irony Classifier: We implement a CNN classifier trained with our irony dataset  All the CNN classifiers we utilize in this paper use the same parameters as BIBREF20  
Sentiment Classifier for Irony: We first implement a one-layer LSTM network to classify ironic sentences in our dataset into positive and negative ironies  The LSTM network is trained with the dataset of Semeval 2015 Task 11 BIBREF0 which is used for the sentiment analysis of figurative language in twitter  Then, we use the positive ironies and negative ironies to train the CNN sentiment classifier for irony 
Sentiment Classifier for Non-irony: Similar to the training process of the sentiment classifier for irony, we first implement a one-layer LSTM network trained with the dataset for the sentiment analysis of common twitters to classify the non-ironies into positive and negative non-ironies  Then we use the positive and negative non-ironies to train the sentiment classifier for non-irony 
Baselines
We compare our model with the following state-of-art generative models:
BackTrans BIBREF7 : In BIBREF7 , authors propose a model using machine translation in order to preserve the meaning of the sentence while reducing stylistic properties 
Unpaired BIBREF10 : In BIBREF10 , researchers implement a method to remove emotional words and add desired sentiment controlled by reinforcement learning 
CrossAlign BIBREF6 : In BIBREF6 , authors leverage refined alignment of latent representations to perform style transfer and a cross-aligned auto-encoder is implemented 
CPTG BIBREF24 : An interpolated reconstruction loss is introduced in BIBREF24 and a discriminator is implemented to control attributes in this work 
DualRL BIBREF8 : In BIBREF8 , researchers use two reinforcement rewards simultaneously to control style accuracy and content preservation 
Evaluation Metrics
In order to evaluate sentiment preservation, we use the absolute value of the difference between the standardized sentiment score of the input sentence and that of the generated sentence  We call the value as sentiment delta (senti delta)  Besides, we report the sentiment accuracy (Senti ACC) which measures whether the output sentence has the same sentiment polarity as the input sentence based on our standardized sentiment classifiers  The BLEU score BIBREF25 between the input sentences and the output sentences is calculated to evaluate the content preservation performance  In order to evaluate the overall performance of different models, we also report the geometric mean (G2) and harmonic mean (H2) of the sentiment accuracy and the BLEU score  As for the irony accuracy, we only report it in human evaluation results because it is more accurate for the human to evaluate the quality of irony as it is very complicated 
We first sample 50 non-ironic input sentences and their corresponding output sentences of different models  Then, we ask four annotators who are proficient in English to evaluate the qualities of the generated sentences of different models  They are required to rank the output sentences of our model and baselines from the best to the worst in terms of irony accuracy (Irony), Sentiment preservation (Senti) and content preservation (Content)  The best output is ranked with 1 and the worst output is ranked with 6  That means that the smaller our human evaluation value is, the better the corresponding model is 
Results and Discussions
Table TABREF35 shows the automatic evaluation results of the models in the transformation from non-ironic sentences to ironic sentences  From the results, our model obtains the best result in sentiment delta  The DualRL model achieves the highest result in other metrics, but most of its outputs are the almost same as the input sentences  So it is reasonable that DualRL system outperforms ours in these metrics but it actually does not transfer the non-ironic sentences to ironic sentences at all  From this perspective, we cannot view DualRL as an effective model for irony generation  In contrast, our model gets results close to those of DualRL and obtains a balance between irony accuracy, sentiment preservation, and content preservation if we also consider the irony accuracy discussed below 
And from human evaluation results shown in Table TABREF36 , our model gets the best average rank in irony accuracy  And as mentioned above, the DualRL model usually does not change the input sentence and outputs the same sentence  Therefore, it is reasonable that it obtains the best rank in sentiment and content preservation and ours is the second  However, it still demonstrates that our model, instead of changing nothing, transfers the style of the input sentence with content and sentiment preservation at the same time 
Case Study
In the section, we present some example outputs of different models  Table TABREF37 shows the results of the transformation from non-ironic sentences to ironic sentences  We can observe that: (1) The BackTrans system, the Unpaired system, the CrossAlign system and the CPTG system tends to generate sentences which are towards irony but do not preserve content  (2) The DualRL system preserves content and sentiment very well but even does not change the input sentence  (3) Our model considers both aspects and achieves a better balance among irony accuracy, sentiment and content preservation 
Error Analysis
Although our model outperforms other style transfer baselines according to automatic and human evaluation results, there are still some failure cases because irony generation is still a very challenging task  We would like to share the issues we meet during our experiments and our solutions to some of them in this section 
No Change: As mentioned above, many style transfer models, such as DualRL, tend to make few changes to the input sentence and output the same sentence  Actually, this is a common issue for unsupervised style transfer systems and we also meet it during our experiments  The main reason for the issue is that rewards for content preservation are too prominent and rewards for style accuracy cannot work well  In contrast, in order to guarantee the readability and fluency of the output sentence, we also cannot emphasize too much on rewards for style accuracy because it may cause some other issues such as word repetition mentioned below  A method to solve the problem is tuning hyperparameters and this is also the method we implement in this work  As for content preservation, maybe MLE methods such as back-translation are not enough because they tend to force models to generate specific words  In the future, we should further design some more suitable methods to control content preservation for models without disentangling style and content representations, such as DualRL and ours 
Word Repetition: During our experiments, we observe that some of the outputs prefer to repeat the same word as shown in Table TABREF38   This is because reinforcement learning rewards encourage the model to generate words which can get high scores from classifiers and even back-translation cannot stop it  Our solution is that we can lower the probability of decoding a word in decoders if the word has been generated in the previous time steps during testing  We also try to implement this method during training time but obtain worse performances because it may limit the effects of training  Some previous studies utilize language models to control the fluency of the output sentence and we also try this method  Nonetheless, pre-training a language model with tweets and using it to generate rewards is difficult because tweets are more casual and have more noise  Rewards from that kind of language model are usually not accurate and may confuse the model  In the future, we should come up with better methods to model language fluency with the consideration of irony accuracy, sentiment and content preservation, especially for tweets 
Improper Words: As ironic style is hard for our model to learn, it may generate some improper words which make the sentence strange  As the example shown in the Table TABREF38 , the sentiment word in the input sentence is “wonderful" and the model should change it into a negative word such as “sad" to make the output sentence ironic  However, the model changes “friday" and “fifa" which are not related to ironic styles  We have not found a very effective method to address this issue and maybe we should further explore stronger models to learn ironic styles better 
Additional Experiments
In this section, we describe some additional experiments on the transformation from ironic sentences to non-ironic sentences  Sometimes ironies are hard to understand and may cause misunderstanding, for which our task also explores the transformation from ironic sentences to non-ironic sentences 
As shown in Table TABREF46 , we also conduct automatic evaluations and the conclusions are similar to those of the transformation from non-ironic sentences to ironic sentences  As for human evaluation results in Table TABREF47 , our model still can achieve the second-best results in sentiment and content preservation  Nevertheless, DualRL system and ours get poor performances in irony accuracy  The reason may be that the other four baselines tend to generate common and even not fluent sentences which are irrelevant to the input sentences and are hard to be identified as ironies  So annotators usually mark these output sentences as non-ironic sentences, which causes these models to obtain better performances than DualRL and ours but much poorer results in sentiment and content preservation  Some examples are shown in Table TABREF52  
Conclusion and Future Work
In this paper, we first systematically define irony generation based on style transfer  Because of the lack of irony data, we make use of twitter and build a large-scale dataset  In order to control irony accuracy, sentiment preservation and content preservation at the same time, we also design a combination of rewards for reinforcement learning and incorporate reinforcement learning with a pre-training process  Experimental results demonstrate that our model outperforms other generative models and our rewards are effective  Although our model design is effective, there are still many errors and we systematically analyze them  In the future, we are interested in exploring these directions and our work may extend to other kinds of ironies which are more difficult to model 

ground_truth: Irony Classifier, Sentiment Classifier for Irony, Sentiment Classifier for Non-irony, transformation from ironic sentences to non-ironic sentences

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

Please answer the questions based on the text provided below. How do the authors evidence the claim that many engineers find it a big overhead to choose from multiple frameworks, models and optimization techniques?
Context 4:  To gain a better understanding of the significance of those differences, we use the Kolmogorov-Smirnov test with the null hypothesis that both distributions are equal 
Results
The sample collected consisted on 1 785 855 tweets published by 848 196 different users  Within our sample, we identified 1327 tweets that went viral (retweeted more than 1000 times by the 8th of November 2016) produced by 643 users  Such small subset of viral tweets were retweeted on 290 841 occasions in the observed time-window 
The 1327 `viral' tweets were manually annotated as containing fake news or not  The annotation was carried out by a single person in order to obtain a consistent annotation throughout the dataset  Out of those 1327 tweets, we identified 136 as potentially containing fake news (according to the categories previously described), and the rest were classified as `non containing fake news'  Note that the categorization is far from being perfect given the ambiguity of fake news themselves and human judgement involved in the process of categorization  Because of this, we do not claim that this dataset can be considered a ground truth 
The following results detail characteristics of these tweets along the previously mentioned dimensions  Table TABREF23 reports the actual differences (together with their associated p-values) of the distributions of viral tweets containing fake news and viral tweets not containing them for every variable considered 
Exposure
Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently  As such, Twitter users were exposed to fake news related to the election for a shorter period of time 
However, in terms of retweets, Figure FIGREF25 shows no apparent difference between containing fake news or not containing them  That is confirmed by the Kolmogorov-Smirnoff test, which does not discard the hypothesis that the associated distributions are equal 
In relation to the number of favourites, users that generated at least a viral tweet containing fake news appear to have, on average, less favourites than users that do not generate them  Figure FIGREF26 shows the distribution of favourites  Despite the apparent visual differences, the difference are not statistically significant 
Finally, the number of hashtags used in viral fake news appears to be larger than those in other viral tweets  Figure FIGREF27 shows the density distribution of the number of hashtags used  However, once again, we were not able to find any statistical difference between the average number of hashtags in a viral tweet and the average number of hashtags in viral fake news 
Characterization
We found that 82 users within our sample were spreading fake news (i e  they produced at least one tweet which was labelled as fake news)  Out of those, 34 had verified accounts, and the rest were unverified  From the 48 unverified accounts, 6 have been suspended by Twitter at the date of writing, 3 tried to imitate legitimate accounts of others, and 4 accounts have been already deleted  Figure FIGREF28 shows the proportion of verified accounts to unverified accounts for viral tweets (containing fake news vs  not containing fake news)  From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts 
Turning to friends, accounts distributing fake news appear to have, on average, the same number of friends than those distributing tweets with no fake news  However, the density distribution of friends from the accounts (Figure FIGREF29 ) shows that there is indeed a statistically significant difference in their distributions 
If we take into consideration the number of followers, accounts generating viral tweets with fake news do have a very different distribution on this dimension, compared to those accounts generating viral tweets with no fake news (see Figure FIGREF30 )  In fact, such differences are statistically significant 
A useful representation for friends and followers is the ratio between friends/followers  Figures FIGREF31 and FIGREF32 show this representation  Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers  The distribution of those accounts not generating fake news is more evenly distributed 
With respect to the number of mentions, Figure FIGREF33 shows that viral tweets labelled as containing fake news appear to use mentions to other users less frequently than viral tweets not containing fake news  In other words, tweets containing fake news mostly contain 1 mention, whereas other tweets tend to have two)  Such differences are statistically significant 
The analysis (Figure FIGREF34 ) of the presence of media in the tweets in our dataset shows that tweets labelled as not containing fake news appear to present more media elements than those labelled as fake news  However, the difference is not statistically significant 
On the other hand, Figure FIGREF35 shows that viral tweets containing fake news appear to include more URLs to other sites than viral tweets that do not contain fake news  In fact, the difference between the two distributions is statistically significant (assuming INLINEFORM0 ) 
Polarization
Finally, manual inspection of the text field of those viral tweets labelled as containing fake news shows that 117 of such tweets expressed support for Donald Trump, while only 8 supported Hillary Clinton  The remaining tweets contained fake news related to other topics, not expressing support for any of the candidates 
Discussion
As a summary, and constrained by our existing dataset, we made the following observations regarding differences between viral tweets labelled as containing fake news and viral tweets labelled as not containing them:
These findings (related to our initial hypothesis in Table TABREF44 ) clearly suggest that there are specific pieces of meta-data about tweets that may allow the identification of fake news  One such parameter is the time of exposure  Viral tweets containing fake news are shorter-lived than those containing other type of content  This notion seems to resonate with our findings showing that a number of accounts spreading fake news have already been deleted or suspended by Twitter by the time of writing  If one considers that researchers using different data have found similar results BIBREF9 , it appears that the lifetime of accounts, together with the age of the questioned viral content could be useful to identify fake news  In the light of this finding, accounts newly created should probably put under higher scrutiny than older ones  This in fact, would be a nice a-priori bias for a Bayesian classifier 
Accounts spreading fake news appear to have a larger proportion of friends/followers (i e  they have, on average, the same number of friends but a smaller number of followers) than those spreading viral content only  Together with the fact that, on average, tweets containing fake news have more URLs than those spreading viral content, it is possible to hypothesize that, both, the ratio of friends/followers of the account producing a viral tweet and number of URLs contained in such a tweet could be useful to single-out fake news in Twitter  Not only that, but our finding related to the number of URLs is in line with intuitions behind the incentives to create fake news commonly found in the literature BIBREF9 (in particular that of obtaining revenue through click-through advertising) 
Finally, it is interesting to notice that the content of viral fake news was highly polarized  This finding is also in line with those of Alcott et al  BIBREF9   This feature suggests that textual sentiment analysis of the content of tweets (as most researchers do), together with the above mentioned parameters from meta-data, may prove useful for identifying fake news 
Conclusions
With the election of Donald Trump as President of the United States, the concept of fake news has become a broadly-known phenomenon that is getting tremendous attention from governments and media companies  We have presented a preliminary study on the meta-data of a publicly available dataset of tweets that became viral during the day of the 2016 US presidential election  Our aim is to advance the understanding of which features might be characteristic of viral tweets containing fake news in comparison with viral tweets without fake news 
We believe that the only way to automatically identify those deceitful tweets (i e  containing fake news) is by actually understanding and modelling them  Only then, the automation of the processes of tagging and blocking these tweets can be successfully performed  In the same way that spam was fought, we anticipate fake news will suffer a similar evolution, with social platforms implementing tools to deal with them  With most works so far focusing on the actual content of the tweets, ours is a novel attempt from a different, but also complementary, angle 
Within the used dataset, we found there are differences around exposure, characteristics of accounts spreading fake news and the tone of the content  Those findings suggest that it is indeed possible to model and automatically detect fake news  We plan to replicate and validate our experiments in an extended sample of tweets (until 4 months after the US election), and tests the predictive power of the features we found relevant within our sample 
Author Disclosure Statement
No competing financial interest exist 

ground_truth: By conducting a survey among engineers

Unanswerable