wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool)  to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Revised:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool) to select the most informative subset of nodes and the corresponding edges based on the clustering assignments of nodes and their nearest neighbor .
Edit intent: clarity
Original:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool)  to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Revised:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool) to select the most informative subset of vertices based on the distance of nodes and their nearest neighbor .
Edit intent: clarity
Original:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool)  to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Revised:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool) to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbors to the cluster centers .
Edit intent: clarity
Original:  After obtaining the clustering assignments of the selected nodes, a classifier is trained using these selected nodes and the final clustering assignments for all the nodes can be obtained by this classifier . Experiments on three benchmark graph datasets demonstrate the superiority compared with several state-of-the-art algorithms.
Revised:  Finally, to obtain the clustering assignment of all nodes, a classifier is trained using these selected nodes and the final clustering assignments for all the nodes can be obtained by this classifier . Experiments on three benchmark graph datasets demonstrate the superiority compared with several state-of-the-art algorithms.
Edit intent: clarity
Original:  After obtaining the clustering assignments of the selected nodes, a classifier is trained using these selected nodes and the final clustering assignments for all the nodes can be obtained by this classifier . Experiments on three benchmark graph datasets demonstrate the superiority compared with several state-of-the-art algorithms.
Revised:  After obtaining the clustering assignments of the selected nodes, a classifier is trained using the clustering results of the selected nodes . Experiments on three benchmark graph datasets demonstrate the superiority compared with several state-of-the-art algorithms.
Edit intent: clarity
Original:  After obtaining the clustering assignments of the selected nodes, a classifier is trained using these selected nodes and the final clustering assignments for all the nodes can be obtained by this classifier . Experiments on three benchmark graph datasets demonstrate the superiority compared with several state-of-the-art algorithms.
Revised:  After obtaining the clustering assignments of the selected nodes, a classifier is trained using these selected nodes and the final clustering assignments for all the nodes can be obtained by this classifier . Experiments on five benchmark graph datasets demonstrate the superiority compared with several state-of-the-art algorithms.
Edit intent: meaning-changed
Original:  After obtaining the clustering assignments of the selected nodes, a classifier is trained using these selected nodes and the final clustering assignments for all the nodes can be obtained by this classifier . Experiments on three benchmark graph datasets demonstrate the superiority compared with several state-of-the-art algorithms.
Revised:  After obtaining the clustering assignments of the selected nodes, a classifier is trained using these selected nodes and the final clustering assignments for all the nodes can be obtained by this classifier . Experiments on three benchmark graph datasets demonstrate the superiority of the proposed method over state-of-the-art algorithms.
Edit intent: clarity
Original:  Different from other approaches, our focus is on the unique characteristics of AI technology and the differences between non-AI computing systems and AI systems. We further elaborate on the human-centered AI (HCAI) approach that we proposed in 2019.
Revised:  Different from other approaches, our focus is on the unique characteristics of AI technology and the new challenges that HCI professionals face when applying the human-centered AI (HCAI) approach that we proposed in 2019.
Edit intent: meaning-changed
Original:  We further elaborate on the human-centered AI (HCAI) approach that we proposed in 2019. Our review and analysis highlight unique issues in developing AI systems which HCI professionals have not encountered in non-AI computing systems.
Revised:  We further elaborate on the human-centered AI (HCAI) approach in the development of AI systems. We also identified seven main issues in human interaction with AI systems, which HCI professionals have not encountered in non-AI computing systems.
Edit intent: meaning-changed
Original:  To further enable the implementation of  HCAI , we promote the research and application of human-AI interaction (HAII) as an interdisciplinary collaboration.
Revised:  To further enable the implementation of the HCAI approach, we identified new HCI opportunities tied to specific HCAI-driven design goals to guide HCI professionals in addressing these new issues. Finally, our assessment of current HCI methods shows the limitations of these methods in support of developing AI systems. We propose alternative methods that can help overcome these limitations and effectively help HCI professionals apply the HCAI , we promote the research and application of human-AI interaction (HAII) as an interdisciplinary collaboration.
Edit intent: meaning-changed
Original:  To further enable the implementation of  HCAI , we promote the research and application of human-AI interaction (HAII) as an interdisciplinary collaboration. There are many opportunities for HCI professionals to play a key role to make unique contributions to the main HAII areas as we identified. To support future HCI practice in the HAII area, we also offer enhanced HCI methods and strategic recommendations. In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI , enabling HCI professionals to address the unique issues of AI systems and develop human-centered AI systems.
Revised:  To further enable the implementation of HCAI approach to the development of AI systems. We also offer strategic recommendations for HCI professionals to address the unique issues of AI systems and develop human-centered AI systems.
Edit intent: clarity
Original:  In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI , enabling HCI professionals to address the unique issues of AI systems and develop human-centered AI systems.
Revised:  In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI , enabling HCI professionals to effectively influence the development of AI systems and develop human-centered AI systems.
Edit intent: clarity
Original:  In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI , enabling HCI professionals to address the unique issues of AI systems and develop human-centered AI systems.
Revised:  In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI , enabling HCI professionals to address the unique issues of AI systems with the HCAI approach, eventually developing HCAI systems.
Edit intent: clarity
Original:  To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases to appear in the translations. In many cases, however, those methods are evaluated on general domain corpora, where the terms are mostly uni- and bi-grams (>98\%).
Revised:  To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases to appear in the translation output. However, in many cases, however, those methods are evaluated on general domain corpora, where the terms are mostly uni- and bi-grams (>98\%).
Edit intent: clarity
Original:  To encourage span-level representations in generation, we additionally impose a source-sentence conditioned masked span prediction loss in the decoder and observe improvements on both terminology translation as well as BLEU scores.
Revised:  To encourage span-level representations in generation, we additionally impose a source-sentence conditioned masked span prediction models, we propose a simple and effective training strategy that achieves consistent improvements on both terminology translation as well as BLEU scores.
Edit intent: clarity
Original:  To encourage span-level representations in generation, we additionally impose a source-sentence conditioned masked span prediction loss in the decoder and observe improvements on both terminology translation as well as BLEU scores. Experimental results on three domain-specific corpora in two language pairs demonstrate that the proposed training scheme can improve the performance of existing lexically constrained methods that can operate both with or without a term dictionary at test time .
Revised:  To encourage span-level representations in generation, we additionally impose a source-sentence conditioned masked span prediction loss in the decoder and observe improvements on both terminology and sentence-level translation for three domain-specific corpora in two language pairs demonstrate that the proposed training scheme can improve the performance of existing lexically constrained methods that can operate both with or without a term dictionary at test time .
Edit intent: clarity
Original:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Revised:  Given that a key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Edit intent: fluency
Original:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Revised:  Given thata key challenge with this task is the limited size of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Edit intent: fluency
Original:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Revised:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies on pretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Edit intent: fluency
Original:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Revised:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from different state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Edit intent: fluency
Original:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Revised:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based language models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Edit intent: fluency
Original:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Revised:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), and on a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Edit intent: fluency
Original:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further en-hancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Revised:  Given thata key challenge with this task is the limitedsize of annotated data, our model relies onpretrained contextual representations from dif-ferent state-of-the-art transformer-based lan-guage models (i.e., BERT and RoBERTa), andon a variety of training methods for further enhancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training.
Edit intent: fluency
Original:  Our analysis of this model's learning dynamics reveals that the convergence rate of its parameters is exponentially faster along directions corresponding to the larger principal components of the data, at a rate governed by the singular values.
Revised:  Our asymptotic analysis, assuming that the hidden layers are wide enough, reveals that the convergence rate of its parameters is exponentially faster along directions corresponding to the larger principal components of the data, at a rate governed by the singular values.
Edit intent: meaning-changed
Original: We introduce kernel thinning, a simple algorithm for generating better-than-Monte-Carlo approximations to distributions \mathbb{P a suitable reproducing kernel k , and O(n^2) time, kernel thinning returns \sqrt{n in the associated reproducing kernel Hilbert space.
Revised: We introduce kernel thinning, a new procedure for compressing a distribution \mathbb{P a suitable reproducing kernel k , and O(n^2) time, kernel thinning returns \sqrt{n in the associated reproducing kernel Hilbert space.
Edit intent: meaning-changed
Original: We introduce kernel thinning, a simple algorithm for generating better-than-Monte-Carlo approximations to distributions \mathbb{P a suitable reproducing kernel k , and O(n^2) time, kernel thinning returns \sqrt{n in the associated reproducing kernel Hilbert space.
Revised: We introduce kernel thinning, a simple algorithm for generating better-than-Monte-Carlo approximations to distributions \mathbb{P a suitable reproducing kernel k and O(n^2) time, kernel thinning returns \sqrt{n in the associated reproducing kernel Hilbert space.
Edit intent: fluency
Original: We introduce kernel thinning, a simple algorithm for generating better-than-Monte-Carlo approximations to distributions \mathbb{P a suitable reproducing kernel k , and O(n^2) time, kernel thinning returns \sqrt{n in the associated reproducing kernel Hilbert space.
Revised: We introduce kernel thinning, a simple algorithm for generating better-than-Monte-Carlo approximations to distributions \mathbb{P a suitable reproducing kernel k , and O(n^2) time, kernel thinning compresses an n-point approximation to \mathbb{P in the associated reproducing kernel Hilbert space.
Edit intent: meaning-changed
Original:  With high probability, the maximum discrepancy in integration error is O_d(n^{-1{2}}\log n) for compactly supported P and O_d(n^{-1{2}} (\log n)^{d+1\log\log n}) for sub-exponential \mathbb{P}  .
Revised:  With high probability, the maximum discrepancy in integration error is O_d(n^{-1{2}}\log n) for compactly supported P and O_d(n^{-1{2}} (\log n)^{d+1\log\log n}) for sub-exponential \mathbb{P} on \mathbb{R .
Edit intent: meaning-changed
Original:  Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging  based particle filter algorithm . Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods.
Revised:  Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging (DMA) based particle filter algorithm . Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods.
Edit intent: clarity
Original:  Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging  based particle filter algorithm . Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods.
Revised:  Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging based particle filter (PF) algorithm. This DMA algorithm employs 2^n models, while all models share the same state-transition function and a unique set of particle values. That makes the computational complexity of this algorithm only slightly larger than a single model based PF algorithm, especially for scenarios in which n is small . Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods.
Edit intent: meaning-changed
Original: We study the statistical limits of uniform convergence for offline policy evaluation (OPE) problems (uniform OPE for short) with model-based methods under episodic MDPsetting .
Revised: We study the statistical limits of uniform convergence for offline policy evaluation (OPE) problems (uniform OPE for short) with model-based methods (for finite horizon MDP) and provides a unified view towards optimal learning for several well-motivated offline tasks .
Edit intent: meaning-changed
Original:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by Yin et al. 2021 ) is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( we call it global policy class).
Revised:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near ) is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( we call it global policy class).
Edit intent: meaning-changed
Original:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by Yin et al. 2021 ) is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( we call it global policy class).
Revised:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by Yin et al. 2021 ) is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( global policy class).
Edit intent: coherence
Original:  In this paper, we establish an \Omega(H^2 S/d_m\epsilon^2) lower bound (over model-based family) for the global uniform OPE, where d_m is the minimal state-action distribution induced by the behavior policy.
Revised:  In this paper, we establish an \Omega(H^2 S/d_m\epsilon^2) lower bound (over model-based family) for the global uniform OPE, where d_m is the minimal state-action probability induced by the behavior policy.
Edit intent: clarity
Original:  The result implies the optimal sample complexity for offline learning and separates  local uniform OPE from the global case  . Paramountly, the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings:
Revised:  This result implies the optimal sample complexity for offline learning and separates local uniform OPE from the global case . Paramountly, the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings:
Edit intent: fluency
Original:  The result implies the optimal sample complexity for offline learning and separates  local uniform OPE from the global case  . Paramountly, the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings:
Revised:  The result implies the optimal sample complexity for offline learning and separates the local uniform OPE from the global case . Paramountly, the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings:
Edit intent: fluency