wanyu/IteraTeR_v2 huggingface dataset:
Original:  Instead of making predictions with the natural languagequery only, we ask the user for additional information using a small number of binary and multiple-choice questionsin order to better help users accomplish their goals while minimizing their effort .
Revised:  Instead of making predictions with the natural languagequery only, we ask the user for additional information using binary or multi-choice questions .
Edit intent: clarity
Original:  We evaluate an algorithm in terms of its ]  ]  ] worst-case expected performance: the expected performance over potential test/training sets , for worst-case datapoints, x_1,\ldots,x_n.
Revised:  We evaluate an algorithm in terms of its ] ] ]. A given estimation algorithm is evaluated based on its " worst-case expected performance: the expected performance over potential test/training sets , for worst-case datapoints, x_1,\ldots,x_n.
Edit intent: clarity
Original:  This framework is a departure from more typical distributional assumptions on the datapoints (e.g. that data is drawn independently, or according to an exchangeable process), and can model a number of natural data collection processes, including processes with dependencies such as "snowball sampling" and "chain sampling", and settings where test and training sets satisfy chronological constraints (e.g. the test instances were observed after the training instances).
Edit intent: coherence
Original:  The algorithm , and its proof , leverage a surprising connection to the Grothendieck problem  .
Revised:  The algorithm and proof leverage a surprising connection to the Grothendieck problem .
Edit intent: clarity
Original:  We propose an efficient batch policy that with O(\log n \times\log k) adaptive rounds of observations can achieve an almost tight ( 1-1/e-\epsilon ) approximation guarantee with respect to an optimal policy that carries out k actions in a fully sequential setting .
Revised:  We propose an efficient batch policy that with O(\log n \times\log k) adaptive rounds of observations can achieve an almost tight 1-1/e-\epsilon ) approximation guarantee with respect to an optimal policy that carries out k actions in a fully sequential setting .
Edit intent: fluency
Original:  We propose an efficient batch policy that with O(\log n \times\log k) adaptive rounds of observations can achieve an almost tight ( 1-1/e-\epsilon ) approximation guarantee with respect to an optimal policy that carries out k actions in a fully sequential setting .
Revised:  We propose an efficient batch policy that with O(\log n \times\log k) adaptive rounds of observations can achieve an almost tight ( 1-1/e-\epsilon approximation guarantee with respect to an optimal policy that carries out k actions in a fully sequential setting .
Edit intent: fluency
Original:  We propose an efficient batch policy that with O(\log n \times\log k) adaptive rounds of observations can achieve an almost tight ( 1-1/e-\epsilon ) approximation guarantee with respect to an optimal policy that carries out k actions in a fully sequential setting .
Revised:  We propose an efficient batch policy that with O(\log n \times\log k) adaptive rounds of observations can achieve an almost tight ( 1-1/e-\epsilon ) approximation guarantee with respect to an optimal policy that carries out k actions in a fully sequential manner .
Edit intent: clarity
Original:  We first prove the conjecture by Golovin and Krause  that the greedy policy achieves the asymptotically tight logarithmic approximation guarantee without resorting to stronger notions of adaptivity.
Revised:  We first prove the conjecture by Golovin and Krause by showing that the greedy policy achieves the asymptotically tight logarithmic approximation guarantee without resorting to stronger notions of adaptivity.
Edit intent: clarity
Original:  Based on the two types of relations, we use  graph convolutional network to learn the deep correlations between styles automatically.
Revised:  Based on the two types of relations, we use a graph convolutional network to learn the deep correlations between styles automatically.
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I S ketchingL ow-rankE stimation forT ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportance ketchingL ow-rankE stimation forT ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS L ow-rankE stimation forT ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketching ow-rankE stimation forT ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL E stimation forT ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rank stimation forT ensors \underline{\underline{ (ISLET).
Edit intent: clarity
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE T ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation for ensors \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT \underline{\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{I\underline{ (ISLET).
Edit intent: coherence
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{mportance\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{ow-rank\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{E\underline{ (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{T (ISLET).
Edit intent: fluency
Original: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{  (ISLET).
Revised: In this paper, we develop a novel procedure for low-rank tensor regression, namely I mportanceS ketchingL ow-rankE stimation forT ensors \underline{\underline{ ensors (ISLET).
Edit intent: fluency
Original:  Further, we show through numerical studies that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods whilst having substantial storage and run-time advantages including capabilities for parallel and distributed computing.
Revised:  Further, we show through numerical study that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods whilst having substantial storage and run-time advantages including capabilities for parallel and distributed computing.
Edit intent: fluency
Original:  Further, we show through numerical studies that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods whilst having substantial storage and run-time advantages including capabilities for parallel and distributed computing.
Revised:  Further, we show through numerical studies that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods while having substantial storage and run-time advantages including capabilities for parallel and distributed computing.
Edit intent: fluency
Original: Fake news has altered society in negative ways as evidenced in politics and culture.
Revised: Fake news has altered society in negative ways in politics and culture.
Edit intent: clarity
Original:  Using automatic fake news detection algorithms is an efficient way to combat the rampant dissemination of fake news.
Revised:  Using automatic machine learning classification models is an efficient way to combat the rampant dissemination of fake news.
Edit intent: clarity
Original:  Using automatic fake news detection algorithms is an efficient way to combat the rampant dissemination of fake news.
Revised:  Using automatic fake news detection algorithms is an efficient way to combat the widespread dissemination of fake news.
Edit intent: fluency
Original:  However, using an effectivedataset has been a problem for fake news research and detection model development.
Revised:  However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development.
Edit intent: clarity
Original:  Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at this scale and breadth .
Revised:  Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadth .
Edit intent: clarity
Original:  However, social media users are not typically a representative sample of the intended population --- a "selection bias".
Revised:  However, social media users are not typically a representative sample of the intended population - a "selection bias".
Edit intent: fluency
Original:  Across five tasks for predicting US county population health statistics from Twitter, we explore standard restratification techniques --- bias mitigation approaches that reweight people-specific variables according to how under-sampled their socio-demographic groups are.
Revised:  Across five tasks for predicting U.S. county population health statistics from Twitter, we explore standard restratification techniques --- bias mitigation approaches that reweight people-specific variables according to how under-sampled their socio-demographic groups are.
Edit intent: fluency
Original:  Across five tasks for predicting US county population health statistics from Twitter, we explore standard restratification techniques --- bias mitigation approaches that reweight people-specific variables according to how under-sampled their socio-demographic groups are.
Revised:  Across five tasks for predicting US county population health statistics from Twitter, we explore standard restratification techniques - bias mitigation approaches that reweight people-specific variables according to how under-sampled their socio-demographic groups are.
Edit intent: fluency
Original:  Combining approaches, we find substantial improvements over non-restratified models as well , yielding a 35.4 \% increase in variance explained for predicting surveyed life satisfaction, and an 10.0 \% average increase across all tasks.
Revised:  Combining approaches, we find substantial improvements over non-restratified models , yielding a 35.4 \% increase in variance explained for predicting surveyed life satisfaction, and an 10.0 \% average increase across all tasks.
Edit intent: clarity
Original:  However, social media users are not typically a representative sample of the intended population - a "selection bias".
Revised:  However, social media users are not typically a representative sample of the intended population -- a "selection bias".
Edit intent: fluency
Original:  Across six tasks for predicting U.S. county population health statistics from Twitter, we explore standard restratification techniques - bias mitigation approaches that reweight people-specific variables according to how under-sampled their socio-demographic groups are.
Revised:  Across six tasks for predicting U.S. county population health statistics from Twitter, we find standard restratification techniques - bias mitigation approaches that reweight people-specific variables according to how under-sampled their socio-demographic groups are.
Edit intent: clarity
Original:  We found standard restratification provided no improvement and often degraded population prediction accuracy .
Revised:  We found standard restratification provided no improvement and often degrade prediction accuracies .
Edit intent: clarity
Original:  The core reason for this seemed to be both shrunken  and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address : predictive redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Revised:  The core reasons for this seems to be both shrunken and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address : predictive redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Edit intent: fluency
Original:  The core reason for this seemed to be both shrunken  and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address : predictive redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Revised:  The core reason for this seemed to be both shrunken and sparse estimates of each population's socio-demographics . We thus develop and evaluate three methods to address : predictive redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Edit intent: coherence
Original:  The core reason for this seemed to be both shrunken  and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address : predictive redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Revised:  The core reason for this seemed to be both shrunken and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address these problems: estimator redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Edit intent: clarity
Original:  The core reason for this seemed to be both shrunken  and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address : predictive redistribution to account for shrinking, as well as adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Revised:  The core reason for this seemed to be both shrunken and sparse estimates of each population's socio-demographics for which we thus develop and evaluate three methods to address : predictive redistribution to account for shrinking, and adaptive binning and informed smoothing to handle sparse socio-demographic estimates.
Edit intent: coherence
Original:  We show each of our methods can significantly improve over the standard restratification approaches.
Revised:  We show that each of these methods significantly outperforms the standard restratification approaches.
Edit intent: clarity
Original:  Shannon's noisy coding theorem established the existence of such codes (without efficient constructions or decoding) with block length O(1/\delta^2).
Revised:  Shannon's noisy coding theorem established the existence of such codes (without efficient constructions or decoding) with block length O(1/\delta^2).
Edit intent: fluency
Original: Semi-supervised learning is a widely used training framework for graph node classification .
Revised: Graph Neural Networks (GNNs) are efficient approaches to process graph-structured data. Modelling long-distance node relations is essential for GNN training and applications .
Edit intent: clarity
Original:  This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than the classical algorithm such as Optimal Anytime(OA)  in sparse environments.
Revised:  This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than the classical algorithms, such as Optimal Anytime(OA) in sparse environments.
Edit intent: fluency
Original:  This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than the classical algorithm such as Optimal Anytime(OA)  in sparse environments.
Revised:  This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than the classical algorithm such as Optimal Anytime(OA) , in sparse environments.
Edit intent: fluency
Original:  However, the implementation of this algorithm is limited because the memory the algorithm required grows indefinitely as the paths get optimized.
Revised:  However, the implementation of this algorithm in systems with limited memory is hindered because the number of nodes in the tree grows indefinitely as the paths get optimized.
Edit intent: clarity
Original:  This paper proposes an improved version of the Multi-agent RRT*( MA-RRT* ) algorithm , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that the MA-RRT*FN performs as well as the MA-RRT*  while the memory required is  fixed.
Revised:  This paper proposes an improved version of MA-RRT* ) algorithm , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that the MA-RRT*FN performs as well as the MA-RRT* while the memory required is fixed.
Edit intent: fluency
Original:  This paper proposes an improved version of the Multi-agent RRT*( MA-RRT* ) algorithm , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that the MA-RRT*FN performs as well as the MA-RRT*  while the memory required is  fixed.
Revised:  This paper proposes an improved version of the Multi-agent RRT*( MA-RRT* , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that the MA-RRT*FN performs as well as the MA-RRT* while the memory required is fixed.
Edit intent: clarity
Original:  This paper proposes an improved version of the Multi-agent RRT*( MA-RRT* ) algorithm , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that the MA-RRT*FN performs as well as the MA-RRT*  while the memory required is  fixed.
Revised:  This paper proposes an improved version of the Multi-agent RRT*( MA-RRT* ) algorithm , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that MA-RRT*FN performs as well as the MA-RRT* while the memory required is fixed.
Edit intent: fluency
Original:  This paper proposes an improved version of the Multi-agent RRT*( MA-RRT* ) algorithm , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that the MA-RRT*FN performs as well as the MA-RRT*  while the memory required is  fixed.
Revised:  This paper proposes an improved version of the Multi-agent RRT*( MA-RRT* ) algorithm , called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the memory of the MA-RRT* . The results show that the MA-RRT*FN performs close to MA-RRT* while the memory required is fixed.
Edit intent: clarity
Original:  However, the implementation of this algorithm  in systems with limited memory is hindered because the number of nodes in the tree grows indefinitely as the paths get optimized.
Revised:  However, the implementation of this algorithm is hindered in systems with limited memory is hindered because the number of nodes in the tree grows indefinitely as the paths get optimized.
Edit intent: fluency
Original:  However, the implementation of this algorithm  in systems with limited memory is hindered because the number of nodes in the tree grows indefinitely as the paths get optimized.
Revised:  However, the implementation of this algorithm in systems with limited memory because the number of nodes in the tree grows indefinitely as the paths get optimized.
Edit intent: clarity
Original:  This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes  which are not likely on the path reaching the goal.
Revised:  This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes on the path which are not likely on the path reaching the goal.
Edit intent: fluency