wanyu/IteraTeR_full_sent huggingface dataset:
Original: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising individuals in high-risk age groups, healthcare workers, and other groups at increased risk of exposure will receive a highly effective priority vaccine (up to 95\% efficacy for symptomatic disease) .
Revised: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, and other groups at increased risk of exposure will receive a highly effective priority vaccine (up to 95\% efficacy for symptomatic disease) .
Edit intent: clarity
Original: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising individuals in high-risk age groups, healthcare workers, and other groups at increased risk of exposure will receive a highly effective priority vaccine (up to 95\% efficacy for symptomatic disease) .
Revised: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising individuals in high-risk age groups, healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine (up to 95\% efficacy for symptomatic disease) .
Edit intent: meaning-changed
Original: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising individuals in high-risk age groups, healthcare workers, and other groups at increased risk of exposure will receive a highly effective priority vaccine (up to 95\% efficacy for symptomatic disease) .
Revised: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising individuals in high-risk age groups, healthcare workers, and other groups at increased risk of exposure will receive a highly effective priority vaccine .
Edit intent: coherence
Original:  In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43 \% and 52\%, respectively. Because of these combined effects, the severity of epidemics as measured by the peak number of daily new cases decreases by up to two orders of magnitude under plausible mass-vaccination campaign endpoint conditions and lockdown strategies. The study presents a strong argument for a large-scale vaccination campaign, which would significantly reduce the intensity of non-pharmaceutical interventions in Australia and curb future outbreaks.
Revised:  In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43 
Edit intent: coherence
Original:  To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine.
Revised: Background: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine.
Edit intent: coherence
Original:  The study presents a strong argument for a large-scale vaccination campaign, which would significantly reduce the intensity of non-pharmaceutical interventions in Australia and curb future outbreaks.abstract 
Revised:  The study presents a strong argument for a large-scale vaccination campaign, which would significantly reduce the intensity of non-pharmaceutical interventions in Australia and curb future outbreaks.abstract \% and 52\%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. Interpretation: The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression.
Edit intent: meaning-changed
Original:  On the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer that improves the separability between component distributions, thus restricting  interdependence among components.
Revised:  On the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer to improve the separability among component distributions, thus restricting interdependence among components.
Edit intent: fluency
Original:  On the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer that improves the separability between component distributions, thus restricting  interdependence among components.
Revised:  On the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer that improves the separability between component distributions, thus restricting the interdependence among components.
Edit intent: fluency
Original:  We extend this result to SE(3), the group of poses (translation and rotation), showing how to build a family of  mappings that includes the matrix exponential as well as the Cayley transformation.
Revised:  We extend this result to SE(3), the group of poses (translation and rotation), where it is possible to build a family of mappings that includes the matrix exponential as well as the Cayley transformation.
Edit intent: coherence
Original:  We extend this result to SE(3), the group of poses (translation and rotation), showing how to build a family of  mappings that includes the matrix exponential as well as the Cayley transformation.
Revised:  We extend this result to SE(3), the group of poses (translation and rotation), showing how to build a family of vectorial mappings that includes the matrix exponential as well as the Cayley transformation.
Edit intent: meaning-changed
Original:  While our main contribution is the theory, we also demonstrate three different applications of the proposed pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii) pose estimation in a pointcloud alignment problem.
Revised:  We extend what is known for these pose mappings to the 4 x 4 representation common in robotics, and also demonstrate three different applications of the proposed pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii) pose estimation in a pointcloud alignment problem.
Edit intent: meaning-changed
Original:  While our main contribution is the theory, we also demonstrate three different applications of the proposed pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii) pose estimation in a pointcloud alignment problem.
Revised:  While our main contribution is the theory, we also demonstrate three different examples of the proposed pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii) pose estimation in a pointcloud alignment problem.
Edit intent: clarity
Original:  In this paper, we aim at better algorithms under the assumption that all the requests with the same booking time arrive simultaneously. Indeed, we propose a randomized algorithm which can achieve a competitive ratio of \frac{4{k + \lfloor k/3 \rfloor} for all k\geq 2.
Revised:  In this paper, we remove all the holes of unknown CR; namely we prove that the CR is \frac{2k{k + \lfloor k/3 \rfloor} for all k\geq 2.
Edit intent: meaning-changed
Original:  We can take this advantage even more, precisely we can achieve a CR } of 2+R{3} if the number of requests in each stage is at most Rk, where R is a constant and 1 \le R \le 2. Both ratios are tight.
Revised:  We can take this advantage even more, precisely we can achieve a CR } of 2+R{3} if the number of requests in each stage is at most Rk, 1 \le R \le 2. Both ratios are tight.
Edit intent: clarity
Original:  We can take this advantage even more, precisely we can achieve a CR } of 2+R{3} if the number of requests in each stage is at most Rk, where R is a constant and 1 \le R \le 2. Both ratios are tight. Our algorithm can also accommodate the original kS2L-F without changing its basic structure .
Revised:  We can take this advantage even more, precisely we can achieve a CR } of 2+R{3} if the number of requests in each stage is at most Rk, where R is a constant and 1 \le R \le 2, where we do not have to know the value of R in advance. Finally we demonstrate that randomization also helps to get (slightly) better CR's .
Edit intent: meaning-changed
Original:  In spite of only two locations, the analysis does not seem easy and the (tight) competitive ratio (CR) is only known to be 2.0 for k=2 and 1.5 for a restricted  k, i.e., a multiple of three.
Revised:  In spite of only two locations, the analysis does not seem easy and the (tight) competitive ratio (CR) is only known to be 2.0 for k=2 and 1.5 for a restricted value of k, i.e., a multiple of three.
Edit intent: clarity
Original:  We can take this advantage even more , precisely we can achieve a CR of 2+R{3} if the number of requests in each stage is at most Rk, 1 %DIFDELCMD < \le %%% R%DIFDELCMD < \le %%%  2, where we do not have to know the value of R in advance.
Revised:  We can take this advantage even further , precisely we can achieve a CR of 2+R{3} if the number of requests in each stage is at most Rk, 1 %DIFDELCMD < \le %%% R%DIFDELCMD < \le %%% 2, where we do not have to know the value of R in advance.
Edit intent: clarity
Original:  We can take this advantage even more , precisely we can achieve a CR of 2+R{3} if the number of requests in each stage is at most Rk, 1 %DIFDELCMD < \le %%% R%DIFDELCMD < \le %%%  2, where we do not have to know the value of R in advance.
Revised:  We can take this advantage even more , precisely we can achieve a CR of 2+R{3} if the number of requests in each stage is at most Rk, 1 %DIFDELCMD < \le %%% %DIFDELCMD < \le %%% 2, where we do not have to know the value of R in advance.
Edit intent: coherence
Original:  We can take this advantage even more , precisely we can achieve a CR of 2+R{3} if the number of requests in each stage is at most Rk, 1 %DIFDELCMD < \le %%% R%DIFDELCMD < \le %%%  2, where we do not have to know the value of R in advance.
Revised:  We can take this advantage even more , precisely we can achieve a CR of 2+R{3} if the number of requests in each stage is at most Rk, 1 %DIFDELCMD < \le %%% R%DIFDELCMD < \le %%% \leq R \leq 2, where we do not have to know the value of R in advance.
Edit intent: meaning-changed
Original:  We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Revised:  REGEX is built upon a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Edit intent: meaning-changed
Original:  We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Revised:  We implemented a masked answer extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Edit intent: fluency
Original:  We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Revised:  We implemented a masked answer entity extraction task with an interactive learning environment containing an answer entity REcognizer, a question Generator, and an answer EXtractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Edit intent: meaning-changed
Original:  We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Revised:  We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Edit intent: clarity
Original:  We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts.
Revised:  We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, the generator generates a question around the entity, and the extractor is trained to extract the masked entity with the generated question and raw texts.
Edit intent: meaning-changed
Original:  To further improve the performances of the question answering model, we propose a reinforcement learning method that rewards generated questions that improves the extraction learning. Experimental results showed that our model outperforms the state-of-the-art  pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Revised:  To further improve the performances of the question answering model, we propose a reinforcement learning technique to reward generating high-quality questions and to improve the answer extraction model's performance. Experiment results show that REGEX outperforms the state-of-the-art pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Edit intent: clarity
Original:  Experimental results showed that our model outperforms the state-of-the-art  pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Revised:  Experimental results showed that our model outperforms the state-of-the-art (SOTA) pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Edit intent: meaning-changed
Original:  Experimental results showed that our model outperforms the state-of-the-art  pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Revised:  Experimental results showed that our model outperforms the state-of-the-art pretrained language models and zero-shot approaches on standard question-answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Edit intent: clarity
Original:  Experimental results showed that our model outperforms the state-of-the-art  pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Revised:  Experimental results showed that our model outperforms the state-of-the-art pretrained language models on standard question answering benchmarks, and yields the new SOTA performance under the zero-shot learning setting.
Edit intent: meaning-changed
Original:  Experimental results showed that our model outperforms the state-of-the-art  pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Revised:  Experimental results showed that our model outperforms the state-of-the-art pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot setting.
Edit intent: clarity
Original:  Although top-k sampling and nucleus sampling outperform beam search based decoding methods , they only focus on truncating the  "tail" of the distribution  and do not address the "head" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops.
Revised:  Traditional methods only focus on truncating the "tail" of the distribution and do not address the "head" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops.
Edit intent: clarity
Original:  Although top-k sampling and nucleus sampling outperform beam search based decoding methods , they only focus on truncating the  "tail" of the distribution  and do not address the "head" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops.
Revised:  Although top-k sampling and nucleus sampling outperform beam search based decoding methods , they only focus on truncating the unreliable "tail" of the distribution and do not address the "head" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops.
Edit intent: meaning-changed
Original:  Although top-k sampling and nucleus sampling outperform beam search based decoding methods , they only focus on truncating the  "tail" of the distribution  and do not address the "head" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops.
Revised:  Although top-k sampling and nucleus sampling outperform beam search based decoding methods , they only focus on truncating the "tail" of the distribution , and do not address the "head" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops.
Edit intent: fluency
Original:  We support our results regarding immigration rates through two related studies from the literature .
Revised:  We particularly highlight the incidence of the semi-log SAR for low immigration rates from a source pool, which has been alluded to in empirical studies. The two SAR forms might show good fits to data over a large span of areas but the extinction rates could be significantly higher for smaller areas when the semi-log relationship holds. This calls for a critical evaluation of extinction scenarios based on SARs so as to not underestimate the possible effects of habitat loss .
Edit intent: coherence
Original:  The simulations show that heparin polyanionic chains can bind at long, mostly positively charged patches on the spike, preventing the binding of host cell heparan sulphate proteoglycans to the spike. Heparin can mask both the S1/S2 basic motif, thereby inhibiting furin cleavage and the formation of the prefusion conformation, and the basic residues of the receptor binding domain (RBD), thus acting on the hinge region responsible for the motion of the RBD between inactive closed and active open conformations of the spike.
Revised:  The simulations show that heparin polyanionic chains can bind at long, mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 basic motif, thereby inhibiting furin cleavage and the formation of the prefusion conformation, and the basic residues of the receptor binding domain (RBD), thus acting on the hinge region responsible for the motion of the RBD between inactive closed and active open conformations of the spike.
Edit intent: coherence
Original:  Heparin can mask both the S1/S2 basic motif, thereby inhibiting furin cleavage and the formation of the prefusion conformation, and the basic residues of the receptor binding domain (RBD), thus acting on the hinge region responsible for the motion of the RBD between inactive closed and active open conformations of the spike.
Revised:  Heparin can mask both the S1/S2 site, which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the formation of the prefusion conformation, and the basic residues of the receptor binding domain (RBD), thus acting on the hinge region responsible for the motion of the RBD between inactive closed and active open conformations of the spike.
Edit intent: meaning-changed
Original:  Heparin can mask both the S1/S2 basic motif, thereby inhibiting furin cleavage and the formation of the prefusion conformation, and the basic residues of the receptor binding domain (RBD), thus acting on the hinge region responsible for the motion of the RBD between inactive closed and active open conformations of the spike.
Revised:  Heparin can mask both the S1/S2 basic motif, thereby inhibiting furin cleavage and the subsequent formation of its post-fusion conformation. Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin. The simulations show that heparin can act on the hinge region responsible for the motion of the RBD between inactive closed and active open conformations of the spike.
Edit intent: clarity
Original:  Heparin is therefore able to inhibit host cell attachment directly and by two allosteric mechanisms .
Revised:  Heparin is therefore able to inhibit SARS-CoV-2 infection by allosterically hindering binding to the host cell receptor, by directly competing with binding to the host heparan sulfate proteoglycan co-receptors, and by preventing spike cleavage by furin .
Edit intent: meaning-changed
Original:  This paper proposes new learning algorithms in two-player zero-sum  games where the number of pure strategies is huge or even infinite . Specifically, we combine no-regret analysis from online learning with double oracle methods from game theory.
Revised:  This paper proposes new learning algorithms for solving two-player zero-sum games where the number of pure strategies is huge or even infinite . Specifically, we combine no-regret analysis from online learning with double oracle methods from game theory.
Edit intent: clarity
Original:  This paper proposes new learning algorithms in two-player zero-sum  games where the number of pure strategies is huge or even infinite . Specifically, we combine no-regret analysis from online learning with double oracle methods from game theory.
Revised:  This paper proposes new learning algorithms in two-player zero-sum normal-form games where the number of pure strategies is huge or even infinite . Specifically, we combine no-regret analysis from online learning with double oracle methods from game theory.
Edit intent: meaning-changed