wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For 0<\lambda<1, we are able to find four boundary curves x_1(h),%DIFDELCMD < \breve{x}%%% (h), x_2(h) and x_3(h) for the wealth level x that are nonlinear functions of h such that the feedback optimal consumption satisfies: (i) c^*(x,h)=0 when x\leq x_1(h); (ii) 0<c^*(x,h)<\lambda h when x_1(h)<x<%DIFDELCMD < \breve{x}%%% (h);
Revised:  For 0<\lambda<1, we are able to find four boundary curves x_1(h),%DIFDELCMD < \breve{x}%%% %DIFDELCMD < \breve{x}%%% (h);
Edit intent: meaning-changed
Original:  (ii) 0<c^*(x,h)<\lambda h when x_1(h)<x<%DIFDELCMD < \breve{x}%%% (h); (iii) \lambda h\leq c^*(x,h)<h when%DIFDELCMD < \breve{x}%%% (h)\leq x<x_2(h);
Revised:  (ii) 0<c^*(x,h)<\lambda h when x_1(h)<x<%DIFDELCMD < \breve{x}%%% %DIFDELCMD < \breve{x}%%% (h)\leq x<x_2(h);
Edit intent: coherence
Original:  (iii) \lambda h\leq c^*(x,h)<h when%DIFDELCMD < \breve{x}%%% (h)\leq x<x_2(h); (iv) c^*(x,h)=h but the running maximum process remains flat when x_2(h)\leq x<x_3(h); (v) c^*(x,h)=h and the running maximum process increases when x=x_3(h). Similar conclusions can be made in a simpler fashion for two extreme cases \lambda=0 and \lambda=1. Numerical examples are also presented to illustrate some theoretical results and financial insights .
Revised:  (iii) \lambda h\leq c^*(x,h)<h when%DIFDELCMD < \breve{x}%%% strategies can be obtained in the closed form in each region. The complete proof of the verification theorem is provided and numerical examples are presented to illustrate some theoretical results and financial insights .
Edit intent: meaning-changed
Original: This paper extends the horseshoe prior of Carvalho et al. (2010) to the Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm that speeds up computation significantly in high dimensions.
Revised: This paper extends the horseshoe prior of Carvalho et al. (2010) to Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm that speeds up computation significantly in high dimensions.
Edit intent: fluency
Original: This paper extends the horseshoe prior of Carvalho et al. (2010) to the Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm that speeds up computation significantly in high dimensions.
Revised: This paper extends the horseshoe prior of Carvalho et al. (2010) to the Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm for computation in high dimensions.
Edit intent: clarity
Original:  The Monte Carlo design considers several sparsity structures (sparse, dense, block) and error structures (i. i.d. errors and heteroskedastic errors). A number of LASSO based estimators (frequentist and Bayesian) are pitted against the HS-BQR to better gauge the performance of the method on the different designs. The HS-BQR yields just as good, or better performance than the other estimators considered when evaluated using coefficient bias and forecast error.
Revised:  The Monte Carlo design considers several sparsity structures (sparse, dense, block) and error structures . Compared to alternative shrinkage priors, the proposed HS-BQR yields just as good, or better performance than the other estimators considered when evaluated using coefficient bias and forecast error.
Edit intent: clarity
Original:  In the empirical application, in which we evaluate forecast densities of US inflation, the HS-BQR provides well calibrated forecast densities whose individual quantiles, have the highest pseudo R squared, highlighting its potential for Value-at-Risk estimation .
Revised:  In the GaR application, we forecast tail risks as well as complete forecast densities using the McCracken and Ng (2020) database. Quantile specific and density calibration score functions show that the HS-BQR provides well calibrated forecast densities whose individual quantiles, have the highest pseudo R squared, highlighting its potential for Value-at-Risk estimation .
Edit intent: meaning-changed
Original:  In the empirical application, in which we evaluate forecast densities of US inflation, the HS-BQR provides well calibrated forecast densities whose individual quantiles, have the highest pseudo R squared, highlighting its potential for Value-at-Risk estimation .
Revised:  In the empirical application, in which we evaluate forecast densities of US inflation, the HS-BQR provides the best performance, especially at short and medium run horizons. The ability to produce well calibrated density forecasts and accurate downside risk measures in large data contexts makes the HS-BQR a promising tool for nowcasting applications and recession modelling .
Edit intent: meaning-changed
Original: Consider a panel data setting with observations of N units over T time steps. Each of the N units undergoes exactly one of D interventions at time step T_0, with 1 \le T_0 < T, prior to which all units experience no intervention, i.e., control.
Revised: Consider a panel data setting with observations of N units over T time periods. Each unit undergoes one of D interventions at time step T_0, with 1 \le T_0 < T, prior to which all units experience no intervention, i.e., control.
Edit intent: clarity
Original:  Each of the N units undergoes exactly one of D interventions at time step T_0, with 1 \le T_0 < T, prior to which all units experience no intervention, i.e., control.
Revised:  Each of the N units undergoes exactly one of D interventions at time period T_0, with 1 \le T_0 < T, prior to which all units experience no intervention, i.e., control.
Edit intent: clarity
Original:  Each of the N units undergoes exactly one of D interventions at time step T_0, with 1 \le T_0 < T, prior to which all units experience no intervention, i.e., control.
Revised:  Each of the N units undergoes exactly one of D interventions at time step T_0, with 1 \le T_0 < T, prior to which all units are under control.
Edit intent: clarity
Original:  We furnish an algorithm to estimate the causal parameter , which utilizes principal component regression (PCR) as a key subroutine. We argue that PCR implicitly de-noises the observations, which are corrupted by idiosyncratic measurement error, and thus advocate for its usage in panel data settings. Formally, we establish consistency and asymptotic normality of the estimated causal parameter.
Revised:  We furnish an estimator for this causal parameter and establish its consistency and asymptotic normality of the estimated causal parameter.
Edit intent: clarity
Original:  There are two families of solutions to this challenge. One family of algorithms, namely retriever-readers, first retrieves some pieces of text that are probably relevant to the question, and then feeds the retrieved text to a neural network to get the answer.
Revised:  There are two families of solutions available: retriever-readers, first retrieves some pieces of text that are probably relevant to the question, and then feeds the retrieved text to a neural network to get the answer.
Edit intent: clarity
Original:  One family of algorithms, namely retriever-readers, first retrieves some pieces of text that are probably relevant to the question, and then feeds the retrieved text to a neural network to get the answer.
Revised:  One family of algorithms, namely retriever-readers, and knowledge-graph-based approaches. A retriever-reader usually first uses information retrieval methods like TF-IDF to locate some documents or paragraphs that are likely to be relevant to the question, and then feeds the retrieved text to a neural network to get the answer.
Edit intent: meaning-changed
Original:  Our algorithm first reads off-line the corpus to generate collections of all answerable questions associated with their answers, and then queries the pre-constructed question spaces online to find answers that are most likely to be asked in the given way.
Revised:  Our reader-retriever first uses an offline reader to read the corpus and generate collections of all answerable questions associated with their answers, and then queries the pre-constructed question spaces online to find answers that are most likely to be asked in the given way.
Edit intent: meaning-changed
Original:  Our algorithm first reads off-line the corpus to generate collections of all answerable questions associated with their answers, and then queries the pre-constructed question spaces online to find answers that are most likely to be asked in the given way.
Revised:  Our algorithm first reads off-line the corpus to generate collections of all answerable questions associated with their answers, and then uses an online retriever to respond to user queries by searching the pre-constructed question spaces online to find answers that are most likely to be asked in the given way.
Edit intent: meaning-changed
Original:  Our algorithm first reads off-line the corpus to generate collections of all answerable questions associated with their answers, and then queries the pre-constructed question spaces online to find answers that are most likely to be asked in the given way.
Revised:  Our algorithm first reads off-line the corpus to generate collections of all answerable questions associated with their answers, and then queries the pre-constructed question spaces for answers that are most likely to be asked in the given way.
Edit intent: clarity
Original:  The final answer returned to the user is decided with an accept-or-reject mechanism that combines multiple candidate answers by comparing the level of agreement between the retriever-reader and reader-retriever results .
Revised:  The final answer returned to the user is decided with an accept-or-reject mechanism that combines multiple candidate answers by comparing the level of agreement between the retriever-reader and reader-retriever results into one single answer by examining the consistency between the two components .
Edit intent: meaning-changed
Original:  We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our toy economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss.
Revised:  We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our model economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss.
Edit intent: clarity
Original:  While we only discuss a limited number of scenarios, our model is flexible and versatile enough to allow for a much wider exploration , thus serving as a useful tool for the qualitative understanding of post-Covid recovery.
Revised:  While we only discuss a limited number of scenarios, our model is flexible and versatile enough to allow for a much wider exploration , thus serving as a useful exploratory tool for a qualitative, scenario-based understanding of post-Covid recovery.
Edit intent: clarity
Original:  The data collected during the US election campaign, contain original tweets from 822 troll accounts which have been identified as such , based on archive data released by Twitter related to state-sponsored trolls' campaigns. Then, we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges , where using graph analysis techniques  along with a game-theoretic centrality measure  , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Revised:  The data collected during the US election campaign, contain original tweets from 822 troll accounts which have been identified as such by Twitter itself. We construct and analyse a very large interaction graph of 9.3 million nodes and 169.9 million edges , where using graph analysis techniques along with a game-theoretic centrality measure , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Edit intent: clarity
Original:  Then, we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges , where using graph analysis techniques  along with a game-theoretic centrality measure  , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Revised:  Then, we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques along with a game-theoretic centrality measure , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Edit intent: coherence
Original:  Then, we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges , where using graph analysis techniques  along with a game-theoretic centrality measure  , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Revised:  Then, we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges , where using graph analysis techniques , along with a game-theoretic centrality measure , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Edit intent: fluency
Original:  Then, we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges , where using graph analysis techniques  along with a game-theoretic centrality measure  , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Revised:  Then, we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges , where using graph analysis techniques along with a game-theoretic centrality measure . Then , we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades.
Edit intent: coherence
Original:  In this work, we propose and study two extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, and (2) creating memory bottleneck for the global information  .
Revised:  In this work, we propose and study few extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, and (2) creating memory bottleneck for the global information .
Edit intent: meaning-changed
Original:  In this work, we propose and study two extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, and (2) creating memory bottleneck for the global information  .
Revised:  In this work, we propose and study two extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, and (2) creating memory bottleneck for the global information , (3) controlling memory update with dedicated layer .
Edit intent: meaning-changed
Original:  We evaluate these memory augmented Transformers on machine translation task and demonstrate that memory size positively correlates with the model performance . Attention patterns over the memory suggest that it improves the model's ability to process a global context .
Revised:  We evaluate these memory augmented Transformers on machine translation task and demonstrate that memory size positively correlates with the model performance for machine translation and language modelling tasks. Augmentation of pre-trained masked language model with memory tokens shows mixed results for tasks from GLUE benchmark. Visualization of attention patterns over the memory suggest that it improves the model's ability to process a global context .
Edit intent: meaning-changed
Original:  Attention patterns over the memory suggest that it improves the model's ability to process a global context . We expect that the application of Memory Transformer architectures to the tasks of language modeling, reading comprehension, and text summarization, as well as other NLP tasks that require the processing of long contexts will contribute to solving challenging problems of natural language understanding and generation .
Revised:  Attention patterns over the memory suggest that it improves the model's ability to process a global context .
Edit intent: clarity
Original: Questions regarding whether, and if so, under what conditions, groups exhibit '' crowd wisdom'' have spurred numerous studies in many disciplines, including management URLanizational science, psychology, sociology, complex systems, and computer science.
Revised: Questions regarding whether, and if so, under what conditions, groups exhibit `` crowd wisdom'' have spurred numerous studies in many disciplines, including management URLanizational science, psychology, sociology, complex systems, and computer science.
Edit intent: fluency
Original: Questions regarding whether, and if so, under what conditions, groups exhibit '' crowd wisdom'' have spurred numerous studies in many disciplines, including management URLanizational science, psychology, sociology, complex systems, and computer science. Substantial effort in previous research on these questions has focused on investigating the role of social influence in promoting the wisdom of the crowd or, conversely, leading the crowd astray .
Revised: Questions regarding whether, and if so, under what conditions, groups exhibit '' crowd wisdom'' has been a major focus of research across the social and computational sciences. Much of this work has focused on investigating the role of social influence in promoting the wisdom of the crowd or, conversely, leading the crowd astray .
Edit intent: clarity
Original:  Substantial effort in previous research on these questions has focused on investigating the role of social influence in promoting the wisdom of the crowd or, conversely, leading the crowd astray . Specifically, many previous studies have sought to infer the importance of social influence network attributes (such as influence centralization) to explain the accuracy of collective estimates. In this paper, we argue that this approach is limited and can lead to inconsistent conclusions. Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Revised:  Substantial effort in previous research on these questions has focused on investigating the role of social influence in promoting the wisdom of the crowd or, conversely, leading the crowd astray , resulting in conflicting conclusions about how the social network structure determines the impact of social influence. Here, we demonstrate that it is not enough to consider the network structure in isolation. Using theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Edit intent: coherence
Original:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Revised:  Based on our theoretical analysis, numerical simulation, and reanalysis of four experimental datasets (totaling 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Edit intent: clarity
Original:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Revised:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human subjects ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Edit intent: clarity
Original:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Revised:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we find that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Edit intent: coherence
Original:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Revised:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds critically depends on the interaction between the following two factors:
Edit intent: clarity
Original:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors:
Revised:  Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human URLanized in 131 independent groups ), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between 
Edit intent: clarity
Original:  (i)  centralization of the social influence network , and (ii) the features of the estimation context---i .e., the distribution of the initial (pre-influence) estimates . Specifically, we find that centralized influence is desirable in situations where a crowd is predisposed to overestimation bias and /or have a high likelihood of committing egregious errors .
Revised:  (i) the centralization of the social influence network , and (ii) the features of the estimation context---i .e., the distribution of the initial (pre-influence) estimates . Specifically, we find that centralized influence is desirable in situations where a crowd is predisposed to overestimation bias and /or have a high likelihood of committing egregious errors .
Edit intent: fluency
Original:  (i)  centralization of the social influence network , and (ii) the features of the estimation context---i .e., the distribution of the initial (pre-influence) estimates . Specifically, we find that centralized influence is desirable in situations where a crowd is predisposed to overestimation bias and /or have a high likelihood of committing egregious errors .
Revised:  (i) centralization of the social influence network and (ii) the features of the estimation context---i .e., the distribution of the initial (pre-influence) estimates . Specifically, we find that centralized influence is desirable in situations where a crowd is predisposed to overestimation bias and /or have a high likelihood of committing egregious errors .
Edit intent: fluency