wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Revised:  The quantitative results reveal that the personalized modeling method significantly outperforms the general modeling approach, reducing the errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Edit intent: clarity
Original:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Revised:  The quantitative results also reveal that the learned reward function is robust, which is manifested by only a marginal decline in proximity to the human driving trajectories when applying the reward function in the testing conditions. For the testing performance, the personalized modeling method significantly outperforms the general modeling approach, reducing the errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Edit intent: meaning-changed
Original:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Revised:  The quantitative results also reveal that the personalized modeling method outperforms the general modeling approach, reducing the errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Edit intent: clarity
Original:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Revised:  The quantitative results also reveal that the personalized modeling method significantly outperforms the general modeling approach, reducing the modeling errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Edit intent: clarity
Original:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Revised:  The quantitative results also reveal that the personalized modeling method significantly outperforms the general modeling approach, reducing the errors in human likeness (a custom metric to gauge accuracy) by 23 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Edit intent: meaning-changed
Original:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Revised:  The quantitative results also reveal that the personalized modeling method significantly outperforms the general modeling approach, reducing the errors in human likeness by 24 \%, and these two methods deliver better results compared to other baseline methods .
Edit intent: meaning-changed
Original:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods . Moreover, it is found that estimating the response actions of surrounding vehicles plays an integral role in estimating the trajectory accurately and achieving a better generalization ability .
Revised:  The quantitative results also reveal that the personalized modeling method significantly outperforms the general modeling approach, reducing the errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
Edit intent: coherence
Original: A doubly robust (DR) estimator is crucial in causal inference , which consists of two nuisance parameters : the conditional mean outcome and  logging policy (  probability of choosing an action)  .
Revised: A doubly robust (DR) estimator is crucial in causal inference , which consists of two nuisance parameters : the conditional mean outcome and logging policy ( probability of choosing an action) , is crucial in causal inference .
Edit intent: coherence
Original:  This paper provides a DR estimator for dependent samples obtained in adaptive experimentsand introduces two related topics. First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Revised:  This paper provides a DR estimator for dependent samples obtained from adaptive experiments. To obtain an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Edit intent: coherence
Original:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Revised:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples with non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Edit intent: fluency
Original:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Revised:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators , we propose adaptive-fitting as a variant of sample-splitting. We also report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Edit intent: coherence
Original:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Revised:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that our proposed DR estimator tends to show better performances compared to other estimators utilizing the true logging policy instead of its estimator .
Edit intent: clarity
Original:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Revised:  First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy .
Edit intent: clarity
Original: We propose a novel framework for non-parametric policy evaluationin static and dynamic settings. Under the assumption of selection on observables, we consider treatment effects of the population, of sub-populations , and of alternative populations that may have alternative covariate distributions.
Revised: We propose a family of reproducing kernel ridge estimators for nonparametric and semiparametric policy evaluation. The framework includes (i) treatment effects of the population, of sub-populations , and of alternative populations that may have alternative covariate distributions.
Edit intent: meaning-changed
Original:  Under the assumption of selection on observables, we consider treatment effects of the population, of sub-populations , and of alternative populations that may have alternative covariate distributions. We further consider the decomposition of a total effect into a direct effect and an indirect effect ( as mediated by a particular mechanism) .
Revised:  Under the assumption of selection on observables, we consider treatment effects of the population, of sub-populations , and of alternative populations ; (ii) the decomposition of a total effect into a direct effect and an indirect effect ( as mediated by a particular mechanism) .
Edit intent: coherence
Original:  Across settings, we allow for treatments that may be discrete , continuous, or even text. Across settings, we allow for estimation of not only counterfactual mean outcomes but also counterfactual distributions of outcomes. We unify analyses across settings by showing that all of these causal learning problems reduce to the re-weighting of a prediction, i.e. causal adjustment. We implement the re-weighting as an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a  closed form solution that can be computed in one line of code.
Revised:  Across settings, we allow for treatments that may be discrete or continuous, and low, high, or infinite dimensional. We consider estimation of means, increments, and distributions of counterfactual outcomes. Each estimator is an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a closed form solution that can be computed in one line of code.
Edit intent: clarity
Original:  We implement the re-weighting as an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a  closed form solution that can be computed in one line of code.
Revised:  We implement the re-weighting as an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a one line, closed form solution that can be computed in one line of code.
Edit intent: meaning-changed
Original:  We implement the re-weighting as an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a  closed form solution that can be computed in one line of code. We prove uniform consistency and provide finite sample rates of convergence.
Revised:  We implement the re-weighting as an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a closed form solution . For the nonparametric case, we prove uniform consistency and provide finite sample rates of convergence.
Edit intent: meaning-changed
Original:  In this model, prey particles spread to neighboring empty sites at rate p, and predator particles spread only to neighboring sites occupied by prey particles at rate 1, killing the prey particle present at that site.
Revised:  In this model, each site can be in one of three states: unoccupied, occupied by a single prey, or occupied by a single predator. Prey particles spread to neighboring empty sites at rate p, and predator particles spread only to neighboring sites occupied by prey particles at rate 1, killing the prey particle present at that site.
Edit intent: meaning-changed
Original:  We estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3.
Revised:  Using Monte Carlo simulations on the square lattice, we estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3.
Edit intent: meaning-changed
Original:  We estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3.
Revised:  We estimate the value of p_c = 0.49451 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3.
Edit intent: meaning-changed
Original:  We estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3.
Revised:  We estimate the value of p_c to be 0.4943 \pm 0.00001, and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3.
Edit intent: meaning-changed
Original:  We estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3.
Revised:  We estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponents are consistent with the 2D isotropic percolation value 4/3.
Edit intent: clarity
Original:  We estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the 2D isotropic percolation value 4/3. We further show Chase-Escape percolation cannot fully be in the 2D isotropic percolationuniversality class as for p < p_c on a D-dimensional hypercubical lattice, the probability that the number of predators in the absorbing configuration is greater than s is bounded from below by \exp(-Kp^{-1 in contrast to the exponentially decaying cluster size distribution of isotropic percolation .
Revised:  We estimate the value of p_c to be 0.4943 \pm 0.0005 and the critical exponent for the divergence of the correlation length \nu to be consistent with the undirected percolation universality class. We define a discrete-time parallel-update version of the model, which brings out the relation between chase-escape and undirected bond percolation. For all p < p_c on a D-dimensional hypercubical lattice, the probability that the number of predators in the absorbing configuration is greater than s is bounded from below by \exp(-Kp^{-1 in contrast to the exponentially decaying cluster size distribution of isotropic percolation .
Edit intent: meaning-changed
Original:  We further show Chase-Escape percolation cannot fully be in the 2D isotropic percolationuniversality class as for p < p_c on a D-dimensional hypercubical lattice, the probability that the number of predators in the absorbing configuration is greater than s is bounded from below by \exp(-Kp^{-1 in contrast to the exponentially decaying cluster size distribution of isotropic percolation .
Revised:  We further show Chase-Escape percolation cannot fully be in the 2D isotropic percolationuniversality class as for p < p_c in D-dimensions, the number of predators in the absorbing configuration is greater than s is bounded from below by \exp(-Kp^{-1 in contrast to the exponentially decaying cluster size distribution of isotropic percolation .
Edit intent: clarity
Original:  We further show Chase-Escape percolation cannot fully be in the 2D isotropic percolationuniversality class as for p < p_c on a D-dimensional hypercubical lattice, the probability that the number of predators in the absorbing configuration is greater than s is bounded from below by \exp(-Kp^{-1 in contrast to the exponentially decaying cluster size distribution of isotropic percolation .
Revised:  We further show Chase-Escape percolation cannot fully be in the 2D isotropic percolationuniversality class as for p < p_c on a D-dimensional hypercubical lattice, the probability that the number of predators in the absorbing configuration has a stretched-exponential distribution in contrast to the exponentially decaying cluster size distribution of isotropic percolation .
Edit intent: meaning-changed
Original:  We further show Chase-Escape percolation cannot fully be in the 2D isotropic percolationuniversality class as for p < p_c on a D-dimensional hypercubical lattice, the probability that the number of predators in the absorbing configuration is greater than s is bounded from below by \exp(-Kp^{-1 in contrast to the exponentially decaying cluster size distribution of isotropic percolation . We then study the problem starting from an initial condition with predator particles on all lattice points of the line y=0 and prey particles on the line y=1.
Revised:  We further show Chase-Escape percolation cannot fully be in the 2D isotropic percolationuniversality class as for p < p_c on a D-dimensional hypercubical lattice, the probability that the number of predators in the absorbing configuration is greater than s is bounded from below by \exp(-Kp^{-1 in contrast to the exponential distribution in the standard percolation theory. We also study the problem starting from an initial condition with predator particles on all lattice points of the line y=0 and prey particles on the line y=1.
Edit intent: clarity
Original:  In this case, for p_c<p < 1, the center of mass of the  prey and predator fronts travel at the same speed and the two fronts fluctuate, but remain close to each other.
Revised:  In this case, for p_c<p < 1, the center of mass of the fluctuating prey and predator fronts travel at the same speed and the two fronts fluctuate, but remain close to each other.
Edit intent: meaning-changed
Original:  In this case, for p_c<p < 1, the center of mass of the  prey and predator fronts travel at the same speed and the two fronts fluctuate, but remain close to each other. We find that the speed of the pinned Chase-Escape front is strictly smaller than the speed of the Eden front when its spreading rate is equal to p and show that it is caused by the prey sites at the leading edge being eaten up by the predator.
Revised:  In this case, for p_c<p < 1, the center of mass of the prey and predator fronts travel at the same speed . This speed is strictly smaller than the speed of the Eden front when its spreading rate is equal to p and show that it is caused by the prey sites at the leading edge being eaten up by the predator.
Edit intent: coherence
Original:  We find that the speed of the pinned Chase-Escape front is strictly smaller than the speed of the Eden front when its spreading rate is equal to p and show that it is caused by the prey sites at the leading edge being eaten up by the predator. We study the depinning transition between the two fronts in the model at p= 1. At the depinning point, on a line of length L, the variance of the width of red front increases as L^{\alphaf(t/L^z) where f a scaling function with \alpha\approx 1 and z=1.67}%DIFDELCMD < \pm %%% 0.1 .
Revised:  We find that the speed of the pinned Chase-Escape front is strictly smaller than the speed of an Eden front with the same value of p, but with no predators. At p= 1. At the depinning point, on a line of length L, the variance of the width of red front increases as L^{\alphaf(t/L^z) where f a scaling function with \alpha\approx 1 and z=1.67}%DIFDELCMD < \pm %%% 0.1 .
Edit intent: meaning-changed
Original:  We study the depinning transition between the two fronts in the model at p= 1. At the depinning point, on a line of length L, the variance of the width of red front increases as L^{\alphaf(t/L^z) where f a scaling function with \alpha\approx 1 and z=1.67}%DIFDELCMD < \pm %%% 0.1 .
Revised:  We study the depinning transition between the two fronts in the model at p= f(t/L^z) where f a scaling function with \alpha\approx 1 and z=1.67}%DIFDELCMD < \pm %%% 0.1 .
Edit intent: clarity
Original:  We study the depinning transition between the two fronts in the model at p= 1. At the depinning point, on a line of length L, the variance of the width of red front increases as L^{\alphaf(t/L^z) where f a scaling function with \alpha\approx 1 and z=1.67}%DIFDELCMD < \pm %%% 0.1 .
Revised:  We study the depinning transition between the two fronts in the model at p= 1. At the depinning point, on a line of length L, the variance of the width of red front increases as L^{\alphaf(t/L^z) where f a scaling function with \alpha\approx 1 and z=1.67}%DIFDELCMD < \pm %%% 1, the fronts undergo a depinning transition. The fluctuations of the front follow Kardar-Parisi-Zhang scaling both above and below this depinning transition .
Edit intent: meaning-changed
Original:  In this paper, we propose a new class of interpretable neural network models that can achieve both high prediction accuracy and interpretability in regression problems with time series cross-sectional data. Our model can essentially be written as a simple function of a limited number of interpretable features .
Revised:  In this paper, we propose a new class of interpretable neural network models that can achieve both high prediction accuracy and interpretability . The model can be written as a simple function of a limited number of interpretable features .
Edit intent: clarity
Original:  Third, analyze the degradation rate of AWD-LSTM to smaller data using degradation test and compare it with transformer-based models. ----- Ang mga low-resource languages tulad ng Filipino ay gipit sa accessible na datos kaya't mahirap gumawa ng mga applications sa wikang ito.
Revised:  Third, analyze the the performance of AWD-LSTM to smaller data using degradation test and compare it with transformer-based models. ----- Ang mga low-resource languages tulad ng Filipino ay gipit sa accessible na datos kaya't mahirap gumawa ng mga applications sa wikang ito.
Edit intent: clarity
Original:  Third, analyze the degradation rate of AWD-LSTM to smaller data using degradation test and compare it with transformer-based models. ----- Ang mga low-resource languages tulad ng Filipino ay gipit sa accessible na datos kaya't mahirap gumawa ng mga applications sa wikang ito.
Revised:  Third, analyze the degradation rate of AWD-LSTM in low-resource setting using degradation test and compare it with transformer-based models. ----- Ang mga low-resource languages tulad ng Filipino ay gipit sa accessible na datos kaya't mahirap gumawa ng mga applications sa wikang ito.
Edit intent: clarity
Original:  Sa mga nagdaang taon, nanaig ang mga transformer-based TL techniques pagdating sa low-resource tasks ngunit ito ay magastos sa resources. Kaya nangangailangan ng mas mura pero epektibong alternatibo.
Revised:  Sa mga nagdaang taon, nanaig ang mga transformer-based TL techniques pagdating sa low-resource tasks ngunit ito ay mataas na compute and memory requirements kaya nangangailangan ng mas mura pero epektibong alternatibo.
Edit intent: meaning-changed