wanyu/IteraTeR_full_sent huggingface dataset:
Original:  . Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September.
Revised:  . Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths weekly by mid-September.
Edit intent: clarity
Original:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached .
Revised:  This seems indefensible unless one assumes that (a) there is little chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached .
Edit intent: clarity
Original:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached .
Revised:  This seems indefensible unless one assumes that (a) there is only a small chance of any effective vaccine or improved medical or social interventions for the foreseeable future , (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached .
Edit intent: clarity
Original:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached .
Revised:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) notwithstanding temporary lockdowns, COVID-19 will inevitably continue to propagate until herd immunity is reached .
Edit intent: meaning-changed
Original:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached .
Revised:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will very likely propagate until herd immunity is reached .
Edit intent: clarity
Original:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached .
Revised:  This seems indefensible unless one assumes that (a) there is only a small chance of any future vaccine and no future improvements in treatment or in non-medical interventions , (b) that COVID-19 will inevitably continue to propagate until herd immunity .
Edit intent: clarity
Original:  These are uncertain, but clearly not negligible, and plausibly comparable to or larger than the costs in lives lost .
Revised:  These are uncertain, but clearly not negligible, and plausibly comparable to or larger than the costs in deaths. In summary, policy on tackling COVID-19 cannot be rationally made without estimating probabilities of future medical interventions and long-term illness costs. More work on modelling these uncertainties is urgently needed .
Edit intent: meaning-changed
Original:  We conduct  human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database  and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Revised:  We conduct a human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Edit intent: fluency
Original:  We conduct  human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database  and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Revised:  We conduct human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Edit intent: fluency
Original:  We conduct  human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database  and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Revised:  We conduct human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Edit intent: meaning-changed
Original:  We conduct  human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database  and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Revised:  We conduct human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database and find that our method produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Edit intent: clarity
Original:  We conduct  human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database  and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Revised:  We conduct human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database and find that our model produces paraphrases that better preserve meaning and are more gramatical , for the same level of lexical /syntactic diversity.
Edit intent: clarity
Original:  We conduct  human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database  and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical /syntactic diversity.
Revised:  We conduct human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database and find that our model produces paraphrases that better preserve semantic meaning and grammatically , for the same level of lexical diversity.
Edit intent: coherence
Original: Networks can describe the structure of a wide variety of complex systems by specifying how pairs of nodes interact. This choice of representation is flexible, but not necessarily appropriate when joint interactions between groups of nodes are needed to explain empirical phenomena.
Revised: Networks can describe the structure of a wide variety of complex systems by specifying which pairs of entities in the system are connected. While such pairwise representations are flexible, they are not necessarily appropriate when joint interactions between groups of nodes are needed to explain empirical phenomena.
Edit intent: clarity
Original:  This choice of representation is flexible, but not necessarily appropriate when joint interactions between groups of nodes are needed to explain empirical phenomena. Networks remain the de facto standard, however, as relational datasets often fail to include higher-order interactions  .
Revised:  This choice of representation is flexible, but not necessarily appropriate when the fundamental interactions involve more than two entities at the same time. Pairwise representations nonetheless remain ubiquitous, because higher-order interactions .
Edit intent: meaning-changed
Original:  In our numerical experiments, which are based on a realistic tax-loss harvesting scenario, our method almost always solves the nonconvex problem to optimality, and when in does not, it produces a trade list very close to optimal.
Revised:  In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when in does not, it produces a trade list very close to optimal.
Edit intent: coherence
Original:  In our numerical experiments, which are based on a realistic tax-loss harvesting scenario, our method almost always solves the nonconvex problem to optimality, and when in does not, it produces a trade list very close to optimal.
Revised:  In our numerical experiments, which are based on a realistic tax-loss harvesting scenario, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal.
Edit intent: fluency
Original: We prove that , assuming the Strong Exponential Time Hypothesis , for any \delta>0, a 5/3-\delta approximation of the diameter of an undirected unweighted graph with nvertices needs n ^{ 3/2-o(1) time.
Revised: We prove several tight results on the fine-grained complexity of approximating the diameter of a graph. First, we prove that, for any \varepsilon>0 , assuming the Strong Exponential Time Hypothesis , for any \delta>0, a 5/3-\delta approximation of the diameter of an undirected unweighted graph with nvertices needs n ^{ 3/2-o(1) time.
Edit intent: meaning-changed
Original: We prove that , assuming the Strong Exponential Time Hypothesis , for any \delta>0, a 5/3-\delta approximation of the diameter of an undirected unweighted graph with nvertices needs n ^{ 3/2-o(1) time.
Revised: We prove that , assuming the Strong Exponential Time Hypothesis (SETH), there are no near-linear time 2-\varepsilon-approximation algorithms for the Diameter of a sparse directed graph, even in unweighted graphs. This result shows that a simple near-linear time 2-approximation algorithm for Diameter is optimal under SETH, answering a question from a survey of Rubinstein and Vassilevska-Williams (SIGACT '19) for the case of directed graphs. In the same survey, Rubinstein and Vassilevska-Williams also asked if it is possible to show that there are no 2-\varepsilon approximation algorithms for Diameter in a directed graph in O(n ^{ 3/2-o(1) time.
Edit intent: meaning-changed
Original: We prove that , assuming the Strong Exponential Time Hypothesis , for any \delta>0, a 5/3-\delta approximation of the diameter of an undirected unweighted graph with nvertices needs n ^{ 3/2-o(1) time.
Revised: We prove that , assuming the Strong Exponential Time Hypothesis , for any \delta>0, a 5/3-\delta approximation of the diameter of an undirected unweighted graph with nvertices needs n ^{ time.
Edit intent: meaning-changed
Original: We prove that , assuming the Strong Exponential Time Hypothesis , for any \delta>0, a 5/3-\delta approximation of the diameter of an undirected unweighted graph with nvertices needs n ^{ 3/2-o(1) time. This result improves on lower bounds of Backurs, Roditty, Segal, Vassilevska-Williams, and Wein ) for the Diameter of a sparse directed unweighted graph can be ruled out by a deterministic SETH-based reduction for every \delta\in(0,1) and essentially every \varepsilon\in(0,1), assuming NSETH.
Revised: We prove that , assuming the Strong Exponential Time Hypothesis , for any \delta>0, a 5/3-\delta approximation of the diameter of an undirected unweighted graph with nvertices needs n ^{ 3/2-o(1) 1.499 ) for the Diameter of a sparse directed unweighted graph can be ruled out by a deterministic SETH-based reduction for every \delta\in(0,1) and essentially every \varepsilon\in(0,1), assuming NSETH.
Edit intent: meaning-changed
Original:  This result improves on lower bounds of Backurs, Roditty, Segal, Vassilevska-Williams, and Wein ) for the Diameter of a sparse directed unweighted graph can be ruled out by a deterministic SETH-based reduction for every \delta\in(0,1) and essentially every \varepsilon\in(0,1), assuming NSETH.
Revised:  This result improves on lower bounds of Backurs, Roditty, Segal, Vassilevska-Williams, and Wein ) time. We show that, assuming a hypothesis called NSETH, one cannot use a deterministic SETH-based reduction to rule out the existence of such algorithms. Furthermore, assuming another hypothesis called Non-Uniform NSETH (NUNSETH), one cannot use a even a randomized SETH-based reduction to rule out the existence of such algorithms. Extending the techniques in these two results, we characterize whether a 2-\varepsilon approximation algorithm running in time O(n^{1+\delta) for the Diameter of a sparse directed unweighted graph can be ruled out by a deterministic SETH-based reduction for every \delta\in(0,1) and essentially every \varepsilon\in(0,1), assuming NSETH.
Edit intent: meaning-changed
Original:  This settles the SETH-hardness of approximating the diameter of sparse directed unweighted graphs for deterministic reductions, up to NSETH , and for randomized reductions, up to NUNSETH.
Revised:  This settles the SETH-hardness of approximating the diameter of sparse directed unweighted graphs for deterministic reductions, up to NSETH . We make the same characterization for randomized SETH-based reductions, assuming another hypothesis called NUNSETH.
Edit intent: meaning-changed
Original:  When benchmarked on MSR corpus under closed test condition without additional data, our method achieves 97.6\% F1, which is on a par with the state of the art  .
Revised:  Our method is generic for word segmentation, without the need for feature engineering or model implementation. In the closed test with constrained data, our method achieves 97.6\% F1, which is on a par with the state of the art .
Edit intent: clarity
Original:  When benchmarked on MSR corpus under closed test condition without additional data, our method achieves 97.6\% F1, which is on a par with the state of the art  .
Revised:  When benchmarked on MSR corpus under closed test condition without additional data, our method achieves 97.6\% F1, which is on a par with the state of the art on the MSR dataset and is comparable to other methods on the PKU dataset .
Edit intent: meaning-changed
Original: The joint bidiagonalization process of a large matrix pair A,L ^2+\lambda^2\|Lx\|_{2}^2\right\} or the essentially equivalent one \min\|Lx\|_{2} \ s.t. \ x\inS = \{x|\ \|Ax-b\|_{2}\leq \eta\|e\|_{2}\}, where e is a Gaussian white noise, L is a regularization matrix and \eta>1 slightly.
Revised: The joint bidiagonalization process of a matrix pair \{ A,L ^2+\lambda^2\|Lx\|_{2}^2\right\} or the essentially equivalent one \min\|Lx\|_{2} \ s.t. \ x\inS = \{x|\ \|Ax-b\|_{2}\leq \eta\|e\|_{2}\}, where e is a Gaussian white noise, L is a regularization matrix and \eta>1 slightly.
Edit intent: clarity
Original: The joint bidiagonalization process of a large matrix pair A,L ^2+\lambda^2\|Lx\|_{2}^2\right\} or the essentially equivalent one \min\|Lx\|_{2} \ s.t. \ x\inS = \{x|\ \|Ax-b\|_{2}\leq \eta\|e\|_{2}\}, where e is a Gaussian white noise, L is a regularization matrix and \eta>1 slightly.
Revised: The joint bidiagonalization process of a large matrix pair A,L \} can be used to develop iterative regularization algorithms for large scale ill-posed problems in general-form Tikhonov regularization \min_x\left\{\|Ax-b\|_{2^2+\lambda^2\|Lx\|_{2}^2\right\} or the essentially equivalent one \min\|Lx\|_{2} \ s.t. \ x\inS = \{x|\ \|Ax-b\|_{2}\leq \eta\|e\|_{2}\}, where e is a Gaussian white noise, L is a regularization matrix and \eta>1 slightly.
Edit intent: meaning-changed
Original: The joint bidiagonalization process of a large matrix pair A,L ^2+\lambda^2\|Lx\|_{2}^2\right\} or the essentially equivalent one \min\|Lx\|_{2} \ s.t. \ x\inS = \{x|\ \|Ax-b\|_{2}\leq \eta\|e\|_{2}\}, where e is a Gaussian white noise, L is a regularization matrix and \eta>1 slightly.
Revised: The joint bidiagonalization process of a large matrix pair A,L ^2+\lambda^2\|Lx\|_{2}^2\right\} or the essentially equivalent one \min\|Lx\|_{2} \ \mbox{{\rm s.t. \ x\inS = \{x|\ \|Ax-b\|_{2}\leq \eta\|e\|_{2}\}, where e is a Gaussian white noise, L is a regularization matrix and \eta>1 slightly.
Edit intent: meaning-changed
Original:  The results show that  the solution accuracy of the inner least squares problems can be relaxed considerably while it will not reduce the accuracy of regularized solutions, thus the overall efficiency of the algorithms can be improved substantially .
Revised:  The results show that for ill-posed problems with not too small noise levels, the solution accuracy of the inner least squares problems can be relaxed considerably while it will not reduce the accuracy of regularized solutions, thus the overall efficiency of the algorithms can be improved substantially .
Edit intent: meaning-changed
Original:  The results show that  the solution accuracy of the inner least squares problems can be relaxed considerably while it will not reduce the accuracy of regularized solutions, thus the overall efficiency of the algorithms can be improved substantially . We also discuss some details for implementing the joint bidiagonalization based regularization algorithms efficiently and stably .
Revised:  The results show that the solution accuracy of the inner least squares problems can be relaxed considerably while it will not reduce the accuracy of regularized solutions, thus the overall efficiency of the algorithms can be improved substantially .
Edit intent: coherence
Original:  First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factor of 0.357 given by Wolsey or (1-1/e)/2\approx 0.316 given by Khuller et al.
Revised:  First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factors of 0.357 given by Wolsey or (1-1/e)/2\approx 0.316 given by Khuller et al.
Edit intent: fluency
Original:  First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factor of 0.357 given by Wolsey or (1-1/e)/2\approx 0.316 given by Khuller et al.
Revised:  First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factor of 0.357 given by Wolsey and (1-1/e)/2\approx 0.316 given by Khuller et al.
Edit intent: coherence
Original:  More importantly, our analysis uncovers a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/\mathrm{e})\approx 0.393 in the literature to clarify a long time of misunderstanding on this issue.
Revised:  More importantly, our analysis closes a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/\mathrm{e})\approx 0.393 in the literature to clarify a long time of misunderstanding on this issue.
Edit intent: clarity
Original:  More importantly, our analysis uncovers a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/\mathrm{e})\approx 0.393 in the literature to clarify a long time of misunderstanding on this issue.
Revised:  More importantly, our analysis uncovers a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/\mathrm{e})\approx 0.393 in the literature to clarify a long-standing misconception on this issue.
Edit intent: clarity
Original:  The business logic of a dApp (or part of it) is usually implemented within one (or more) smart contract(s) developed through Solidity, an object-oriented programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum.
Revised:  The business logic of dApps is usually implemented within one (or more) smart contract(s) developed through Solidity, an object-oriented programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum.
Edit intent: clarity
Original:  The business logic of a dApp (or part of it) is usually implemented within one (or more) smart contract(s) developed through Solidity, an object-oriented programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum.
Revised:  The business logic of a dApp (or part of it) is usually implemented within smart contracts developed through Solidity, an object-oriented programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum.
Edit intent: clarity
Original:  The business logic of a dApp (or part of it) is usually implemented within one (or more) smart contract(s) developed through Solidity, an object-oriented programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum.
Revised:  The business logic of a dApp (or part of it) is usually implemented within one (or more) smart contract(s) developed through Solidity, a programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum.
Edit intent: clarity
Original:  In Ethereum, once compiled, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and thegas (in Ether) corresponds to the execution fee compensating such computing resources.
Revised:  In Ethereum, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and thegas (in Ether) corresponds to the execution fee compensating such computing resources.
Edit intent: clarity
Original:  In Ethereum, once compiled, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and thegas (in Ether) corresponds to the execution fee compensating such computing resources.
Revised:  In Ethereum, once compiled, the smart contracts run on the machines of miners gas (in Ether) corresponds to the execution fee compensating such computing resources.
Edit intent: coherence
Original:  In Ethereum, once compiled, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and thegas (in Ether) corresponds to the execution fee compensating such computing resources.
Revised:  In Ethereum, once compiled, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and the (in Ether) corresponds to the execution fee compensating such computing resources.
Edit intent: fluency
Original:  In Ethereum, once compiled, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and thegas (in Ether) corresponds to the execution fee compensating such computing resources.
Revised:  In Ethereum, once compiled, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and thegas and the gas corresponds to the execution fee compensating such computing resources.
Edit intent: clarity