wanyu/IteraTeR_full_sent huggingface dataset:
Original: Does an independent central bank (CBI) reduce inflation ? Despite numerous articles suggesting it does, this question has not been satisfactorily answered because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into economic analyses.
Revised: Does an independent central bank (CBI) reduce inflation ? Despite numerous articles suggesting it does, this question has not been satisfactorily answered because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses.
Edit intent: clarity
Original:  We develop a causal model that summarizes the economic process of inflation and estimate the effect of CBI on inflation with modern doubly robust effect estimation techniques.
Revised:  We develop a causal model that summarizes the economic process of inflation . Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimation techniques.
Edit intent: meaning-changed
Original:  We develop a causal model that summarizes the economic process of inflation and estimate the effect of CBI on inflation with modern doubly robust effect estimation techniques.We incorporate a large amount of variables in our directed acyclic graph and overcome endogeneity issues of previous studies.Our approach includes machine learning algorithms which are tailored to the question of interest and reduce the chance of model misspecification .
Revised:  We develop a causal model that summarizes the economic process of inflation and estimate the effect of CBI on inflation with modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms which are tailored to the question of interest and reduce the chance of model misspecification .
Edit intent: meaning-changed
Original:  We do not find strong support for the hypothesis that CBI causes lower inflation. If a country had legislated CBI for every year between 1998 and 2008, it would have had an average reduction in inflationof -0.63 (-2.33; 1.07) percentage points in 2010.
Revised:  We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation.
Edit intent: meaning-changed
Original:  The algorithm achieves O\left(m ^2 n\right) expected regret under the stochastic input model and O\left((m ^2 +\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Revised:  The algorithm achieves O\left(m n\right) expected regret under the stochastic input model and O\left((m ^2 +\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Edit intent: fluency
Original:  The algorithm achieves O\left(m ^2 n\right) expected regret under the stochastic input model and O\left((m ^2 +\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Revised:  The algorithm achieves O\left(m ^2 n\right) expected regret under the stochastic input model and O\left((m +\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Edit intent: fluency
Original:  Furthermore, the algorithm is generalized to a multi-dimensional LP setting which covers a wider range of applications and features for the same performance guarantee .
Revised:  Furthermore, the algorithm is generalized to a multi-dimensional LP setting which covers a wider range of applications . To complete the discussion, we employ the notion of permutational Rademacher complexity and analyze the regret of two online LP algorithms in the literature under the random permutation model. Furthermore, we demonstrate how to convert the possibly infeasible solution to a feasible one through a randomized procedure .
Edit intent: meaning-changed
Original:  We analyze the algorithm in two different models, the stochastic input model and the random permutationmodel , with minimal  assumptions on the input of the LP . The algorithm achieves O\left(m n\right) expected regret under the stochastic input model and O\left((m+\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Revised:  We analyze the algorithm in two different models, stochastic input and random permutation , with minimal assumptions on the input of the LP . The algorithm achieves O\left(m n\right) expected regret under the stochastic input model and O\left((m+\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Edit intent: fluency
Original:  We analyze the algorithm in two different models, the stochastic input model and the random permutationmodel , with minimal  assumptions on the input of the LP . The algorithm achieves O\left(m n\right) expected regret under the stochastic input model and O\left((m+\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Revised:  We analyze the algorithm in two different models, the stochastic input model and the random permutationmodel , with minimal technical assumptions on the input of the LP . The algorithm achieves O\left(m n\right) expected regret under the stochastic input model and O\left((m+\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Edit intent: meaning-changed
Original:  We analyze the algorithm in two different models, the stochastic input model and the random permutationmodel , with minimal  assumptions on the input of the LP . The algorithm achieves O\left(m n\right) expected regret under the stochastic input model and O\left((m+\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Revised:  We analyze the algorithm in two different models, the stochastic input model and the random permutationmodel , with minimal assumptions on the input data . The algorithm achieves O\left(m n\right) expected regret under the stochastic input model and O\left((m+\log n)n\right) expected regret under the random permutation model, and it achieves O(m n) expected constraint violation under both models, where n is the number of decision variables and m is the number of constraints.
Edit intent: clarity
Original:  We also show that prominent factors , such as the size effect and momentum , exhibit time varying stock return predictiveness  .
Revised:  We also show that prominent factors , such as the size effect and momentum , exhibit time varying stock return predictiveness . We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk .
Edit intent: meaning-changed
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Edit intent: clarity
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) . Moreover, the bound on the number of rounds is improved from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Edit intent: meaning-changed
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Edit intent: fluency
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var Var X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Edit intent: meaning-changed
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , where X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Edit intent: clarity
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar n) to O(\log Var X / \log\log Var X ), which we show to be tight.
Edit intent: meaning-changed
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log X / \log\log Var X ), which we show to be tight.
Edit intent: fluency
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var n / \log\log Var X ), which we show to be tight.
Edit intent: meaning-changed
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log X ), which we show to be tight.
Edit intent: fluency
Original: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var X ), which we show to be tight.
Revised: We present a truly simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007) -- and improve it from O(\log Var X) , whereVar X is the variance of the input data set, to O(\log Var X / \log\log Var n ), which we show to be tight.
Edit intent: meaning-changed
Original: This paper studies the joint impact of congestion surcharge and wage regulation on transportation network companies (TNCs) . These impacts are assessed by a market equilibrium model that captures the incentives of the passengers, drivers , and the platform, and accounts for the congestion externalities of the TNC vehicles.
Revised: This paper studies the joint impact of congestion surcharge and wage regulation on transportation network companies (TNCs) of the imposition of a congestion charge and a driver minimum wage. The impact is assessed using a market equilibrium model that captures the incentives of the passengers, drivers , and the platform, and accounts for the congestion externalities of the TNC vehicles.
Edit intent: meaning-changed
Original:  These impacts are assessed by a market equilibrium model that captures the incentives of the passengers, drivers , and the platform, and accounts for the congestion externalities of the TNC vehicles.
Revised:  These impacts are assessed by a market equilibrium model to calculate the changes in the number of passenger trips and trip fare, number of drivers employed, the TNC platform profit, the number of TNC vehicles , and the platform, and accounts for the congestion externalities of the TNC vehicles.
Edit intent: meaning-changed
Original:  These impacts are assessed by a market equilibrium model that captures the incentives of the passengers, drivers , and the platform, and accounts for the congestion externalities of the TNC vehicles. Under a wage floor on TNC drivers, we consider two schemes of congestion surcharges : (a) surcharge based on each TNC trip , and (b) surcharge based on each vehicle hour (regardless of whether the vehicle has a passenger or not) .
Revised:  These impacts are assessed by a market equilibrium model that captures the incentives of the passengers, drivers , and city revenue. Two charges are considered : (a) surcharge based on each TNC trip , and (b) surcharge based on each vehicle hour (regardless of whether the vehicle has a passenger or not) .
Edit intent: clarity
Original:  Under a wage floor on TNC drivers, we consider two schemes of congestion surcharges : (a) surcharge based on each TNC trip , and (b) surcharge based on each vehicle hour (regardless of whether the vehicle has a passenger or not) .
Revised:  Under a wage floor on TNC drivers, we consider two schemes of congestion surcharges : (a) a charge per TNC trip similar to an excise tax , and (b) surcharge based on each vehicle hour (regardless of whether the vehicle has a passenger or not) .
Edit intent: meaning-changed
Original:  Under a wage floor on TNC drivers, we consider two schemes of congestion surcharges : (a) surcharge based on each TNC trip , and (b) surcharge based on each vehicle hour (regardless of whether the vehicle has a passenger or not) .
Revised:  Under a wage floor on TNC drivers, we consider two schemes of congestion surcharges : (a) surcharge based on each TNC trip , and (b) a charge per vehicle operating hour (whether or not it has a passenger or not) .
Edit intent: clarity
Original:  Under a wage floor on TNC drivers, we consider two schemes of congestion surcharges : (a) surcharge based on each TNC trip , and (b) surcharge based on each vehicle hour (regardless of whether the vehicle has a passenger or not) . We show that both congestion surcharges can reduce the TNC ridership, but their impacts are mitigated by the wage floor and cannot significantly reduce the number of TNC vehicles .
Revised:  Under a wage floor on TNC drivers, we consider two schemes of congestion surcharges : (a) surcharge based on each TNC trip , and (b) surcharge based on each vehicle hour (regardless of whether the vehicle has a passenger ) similar to a road tax. Both charges reduce the number of TNC trips, but this reduction is limited by the wage floor and cannot significantly reduce the number of TNC vehicles .
Edit intent: clarity
Original:  We show that both congestion surcharges can reduce the TNC ridership, but their impacts are mitigated by the wage floor and cannot significantly reduce the number of TNC vehicles . In contrast to the trip-based surcharge, we advocate the time-based congestion surcharge, which penalizes idle vehicle time and improves the vehicle occupancy.
Revised:  We show that both congestion surcharges can reduce the TNC ridership, but their impacts are mitigated by the wage floor and cannot significantly reduce the number of TNC vehicles reduced is not significant. The time-based charge is preferable to the trip-based surcharge, we advocate the time-based congestion surcharge, which penalizes idle vehicle time and improves the vehicle occupancy.
Edit intent: meaning-changed
Original:  Through a case study for San Francisco, we show that the time-based congestion surcharge offers a Pareto improvement. It leads to higher passenger surplus, higher driver surplus, higher platform profits, and higher tax revenue for the city.
Revised:  Through a case study for San Francisco, we show that the time-based charge is found to be Pareto superior to the trip-based charge as it yields higher passenger surplus, higher driver surplus, higher platform profits, and higher tax revenue for the city.
Edit intent: clarity
Original:  Previous attempts to learn variational auto-encoders for language data ? have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the ? occurrence of posterior collapse with VAEs.
Revised:  Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the ? occurrence of posterior collapse with VAEs.
Edit intent: fluency
Original:  Previous attempts to learn variational auto-encoders for language data ? have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the ? occurrence of posterior collapse with VAEs.
Revised:  Previous attempts to learn variational auto-encoders for language data ? have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapse with VAEs.
Edit intent: fluency
Original: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Revised: SentenceMIM is a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Edit intent: clarity
Original: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Revised: We introduce sentenceMIM, a probabilistic auto-encoder for language data , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Edit intent: clarity
Original: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Revised: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning to provide a fixed length representation of variable length language observations (\ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Edit intent: meaning-changed