wanyu/IteraTeR_full_sent huggingface dataset:
Original: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight  as well as a better understanding by practitioners in crisis management  is needed.
Revised: During the COVID-19 pandemic, social media has become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight as well as a better understanding by practitioners in crisis management is needed.
Edit intent: clarity
Original: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight  as well as a better understanding by practitioners in crisis management  is needed.
Revised: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation . To tackle this still ongoing infodemic, scientific oversight as well as a better understanding by practitioners in crisis management is needed.
Edit intent: coherence
Original: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight  as well as a better understanding by practitioners in crisis management  is needed.
Revised: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this infodemic, scientific oversight as well as a better understanding by practitioners in crisis management is needed.
Edit intent: clarity
Original: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight  as well as a better understanding by practitioners in crisis management  is needed.
Revised: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight , as well as a better understanding by practitioners in crisis management is needed.
Edit intent: fluency
Original: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight  as well as a better understanding by practitioners in crisis management  is needed.
Revised: Although a lot of correct and useful information is shared through channels such as Twitter, it has also become a home ground for misinformation on COVID-19 . To tackle this still ongoing infodemic, scientific oversight as well as a better understanding by practitioners in crisis management , is needed.
Edit intent: fluency
Original:  We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights into the COVID-19 infodemic.
Revised:  We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights . We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 infodemic.
Edit intent: meaning-changed
Original:  We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights into the COVID-19 infodemic.
Revised:  We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights into the COVID-19 by over 92 professional URLanisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1 500 tweets relating to 1 274 false and 276 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating (new tweets) or spreading (retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others.
Edit intent: meaning-changed
Original:  Our results enable us to not only give first indications but also to suggest gaps in the current scientific coverage of the topic . Moreover, we propose actions for authorities to counter misinformation and hints for social media users on how to help stop the spread of misinformation.
Revised:  Our results enable us to not only give first indications but also to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities to counter misinformation and hints for social media users on how to help stop the spread of misinformation.
Edit intent: coherence
Original: We define and characterize the set of renegotiation-proof equilibria of coordination games with pre-play communication in which players have private preferences over the feasible coordinated outcomes.
Revised: An equilibrium is communication-proof if it is unaffected by new opportunities to communicate and renegotiate. We characterize the set of renegotiation-proof equilibria of coordination games with pre-play communication in which players have private preferences over the feasible coordinated outcomes.
Edit intent: meaning-changed
Original:  In this paper we report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing ( FigLang2020 ) at ACL 2020.
Revised:  We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing ( FigLang2020 ) at ACL 2020.
Edit intent: coherence
Original:  In this paper we report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing ( FigLang2020 ) at ACL 2020.
Revised:  In this paper we report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing ( FigLang 2020 ) at ACL 2020.
Edit intent: fluency
Original: TextAttack is a library for running adversarial attacksagainst natural language processing (NLP) models .
Revised: While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP .
Edit intent: meaning-changed
Original:  TextAttack builds attacks from four components: a search method, goal function, transformation, and a set of constraints . Researchers can use these components to easily assemble new attacks . Individual components can be isolated and compared for easier ablation studies. TextAttack currently supports attacks on models trained for text classification and entailment across a variety of datasets.
Revised:  TextAttack builds attacks from four components: a search method, goal function, transformation, and a set of constraints , a transformation, and a search method. TextAttack's modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of datasets.
Edit intent: meaning-changed
Original:  TextAttack currently supports attacks on models trained for text classification and entailment across a variety of datasets. Additionally, TextAttack's modular design makes it easily extensible to new NLP tasks, models, and attack strategies. TextAttack code and tutorials are available at URL
Revised:  TextAttack currently supports attacks on models trained for text classification and entailment across a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness. TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at URL
Edit intent: meaning-changed
Original:  After introducing the new moment conditions we explore their implications for identification and estimation of the model parameters that are common to all individuals, and we find that those common model parameters are estimable at root-n rate for many more dynamic panel logit models than was previously known in the literature.
Revised:  After introducing the moment conditions obtained in this way, we explore their implications for identification and estimation of the model parameters that are common to all individuals, and we find that those common model parameters are estimable at root-n rate for many more dynamic panel logit models than was previously known in the literature.
Edit intent: clarity
Original:  After introducing the new moment conditions we explore their implications for identification and estimation of the model parameters that are common to all individuals, and we find that those common model parameters are estimable at root-n rate for many more dynamic panel logit models than was previously known in the literature.
Revised:  After introducing the new moment conditions we explore their implications for identification and estimation of the model parameters that are common to all individuals, and we find that those common model parameters are estimable at root-n rate for many more dynamic panel logit models than has been appreciated by the existing literature. In the case where the model contains one lagged variable, the moment conditions in Kitazawa (2013, 2016) are transformations of a subset of ours.
Edit intent: meaning-changed
Original:  The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets).
Revised:  The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets).
Edit intent: meaning-changed
Original:  The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets).
Revised:  The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets ( ~32M tweets).
Edit intent: meaning-changed
Original:  The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets).
Revised:  The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( 169M tweets).
Edit intent: meaning-changed
Original:  The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets (  169M tweets).
Revised:  The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets ( ~ 169M tweets).
Edit intent: fluency
Original:  We also develop and release a powerful model (acc = 94%DIFDELCMD < \\%%% %DIF < ) for teasing apart messages related to the pandemic from those that are not.
Revised:  We also develop and release two powerful models, one for identifying whether or not a tweet is related to the pandemic (best F1 = 94%DIFDELCMD < \\%%% %DIF < ) for teasing apart messages related to the pandemic from those that are not.
Edit intent: meaning-changed
Original:  Our data and models are publicly available for research.abstract 
Revised:  Our data and models are publicly available for research.abstract 97\%) and another for detecting misinformation about COVID-19 (best F1=92\%). A human annotation study reveals the utility of our models on a subset of Mega-COV. Our data and models can be useful for studying a wide host of phenomena related to the pandemic. Mega-COV and our models are publicly available.
Edit intent: meaning-changed
Original: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in  susceptibilities across animal species.
Revised: The devastating impact of the COVID-19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in susceptibilities across animal species.
Edit intent: fluency
Original: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in  susceptibilities across animal species.
Revised: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARS CoV 2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in susceptibilities across animal species.
Edit intent: clarity
Original: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in  susceptibilities across animal species.
Revised: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions about viral origin, mechanisms of zoonotic transfer to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in susceptibilities across animal species.
Edit intent: clarity
Original: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in  susceptibilities across animal species.
Revised: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion or commercial animals can act as reservoirs for infection, and the reasons for the large variations in susceptibilities across animal species.
Edit intent: clarity
Original: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in  susceptibilities across animal species.
Revised: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and why there are large variations in susceptibilities across animal species.
Edit intent: clarity
Original: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in  susceptibilities across animal species.
Revised: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 ( SARSCoV2 ) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in SARS-CoV-2 susceptibilities across animal species.
Edit intent: meaning-changed
Original:  In silico modeling methods provide the opportunity to rapidly generate information on newly emerged pathogens to aid countermeasure development and also to predict potential future behaviors. We used a structural homology modeling approach to characterize the SARSCoV2 spike protein and predict its binding strength to the human ACE2 receptor.
Revised:  In silico modeling methods provide the opportunity to rapidly generate information on newly emerged pathogens to aid countermeasure development and predict future behaviours. Here we report an in silico structural homology modelling, protein-protein docking, and molecular dynamics simulation study of the key infection initiating interaction between the spike protein of SARS-Cov-2 and its target, angiotensin converting enzyme 2 ( ACE2 receptor.
Edit intent: meaning-changed
Original:  We used a structural homology modeling approach to characterize the SARSCoV2 spike protein and predict its binding strength to the human ACE2 receptor. We then explored the possible transmission path by which SARSCoV2 might have crossed to humans by constructing models of ACE2 receptors of relevant species, and calculating the binding energy of SARSCoV2 spike protein to each.
Revised:  We used a structural homology modeling approach to characterize the SARSCoV2 spike protein and predict its binding strength to the human ACE2 ) from multiple species. Human ACE2 receptors of relevant species, and calculating the binding energy of SARSCoV2 spike protein to each.
Edit intent: clarity
Original:  We then explored the possible transmission path by which SARSCoV2 might have crossed to humans by constructing models of ACE2 receptors of relevant species, and calculating the binding energy of SARSCoV2 spike protein to each. Notably, SARSCoV2 spike protein had the highest overall binding energy for human ACE2, greater than all the other tested species including bat, the postulated source of the virus.
Revised:  We then explored the possible transmission path by which SARSCoV2 might have crossed to humans by constructing models of ACE2 has the strongest binding interaction, significantly greater than for any species proposed as source of the virus.
Edit intent: clarity
Original:  This indicates that SARSCoV2 is a highly adapted human pathogen. Of the species studied, the next highest binding affinity after human was pangolin , which is most likely explained by a process of convergent evolution. Binding of SARSCoV2 for dog and cat ACE2 was similar to affinity for bat ACE2, all being lower than for humanACE2, and is consistent with only occasional observations of infections of these domestic animals.
Revised:  Binding to pangolin ACE2 was similar to affinity for bat ACE2, all being lower than for humanACE2, and is consistent with only occasional observations of infections of these domestic animals.
Edit intent: coherence
Original:  Binding of SARSCoV2 for dog and cat ACE2 was similar to affinity for bat ACE2, all being lower than for humanACE2, and is consistent with only occasional observations of infections of these domestic animals. Overall, the data indicates that SARSCoV2 is uniquely adapted to infect humans, raising questions as to whether it arose in nature by a rare chance event or whether its origins lie elsewhere .
Revised:  Binding of SARSCoV2 for dog and cat ACE2 was the second strongest, possibly due to the SARS-CoV-2 spike receptor binding domain (RBD) being identical to pangolin CoV spike RDB. Except for snake, pangolin and bat for which permissiveness has not been tested, all those species in the upper half of the affinity range (human, monkey, hamster, dog, ferret) have been shown to be at least moderately permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and permissiveness. Our data indicates that SARSCoV2 is uniquely adapted to infect humans, raising questions as to whether it arose in nature by a rare chance event or whether its origins lie elsewhere .
Edit intent: meaning-changed
Original:  Here, we present a formal definition of a digital social contracts as a transition system that specifies a system of automata, one automaton per party, which communicate asynchronously via crypto-speech acts, where the output of each automaton is the input of all the other automata .
Revised:  Here, we present a formal definition of a digital social contract as agents that communicate asynchronously via crypto-speech acts, where the output of each automaton is the input of all the other automata .
Edit intent: clarity
Original:  Here, we present a formal definition of a digital social contracts as a transition system that specifies a system of automata, one automaton per party, which communicate asynchronously via crypto-speech acts, where the output of each automaton is the input of all the other automata .
Revised:  Here, we present a formal definition of a digital social contracts as a transition system that specifies a system of automata, one automaton per party, which communicate asynchronously via crypto-speech acts, where the output of each agent is the input of all the other automata .
Edit intent: clarity
Original:  Here, we present a formal definition of a digital social contracts as a transition system that specifies a system of automata, one automaton per party, which communicate asynchronously via crypto-speech acts, where the output of each automaton is the input of all the other automata .
Revised:  Here, we present a formal definition of a digital social contracts as a transition system that specifies a system of automata, one automaton per party, which communicate asynchronously via crypto-speech acts, where the output of each automaton is the input of all the other agents .
Edit intent: clarity