wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this work, we propose a simple and effective method to cover  much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Revised:  In this work, we propose a simple and effective method to cover much larger proportion of the attack search space, called Adversarial and Mixup Data Augmentation (AMDA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Edit intent: meaning-changed
Original:  In this work, we propose a simple and effective method to cover  much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Revised:  In this work, we propose a simple and effective method to cover much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, AMDA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Edit intent: clarity
Original:  In this work, we propose a simple and effective method to cover  much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Revised:  In this work, we propose a simple and effective method to cover much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training samples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Edit intent: fluency
Original:  In this work, we propose a simple and effective method to cover  much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Revised:  In this work, we propose a simple and effective method to cover much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete text adversarial examples in conventional ADA.
Edit intent: clarity
Original:  In the text classification experiments of BERT and RoBERTa, MixADA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the original data.
Revised:  In text classification experiments of BERT and RoBERTa, MixADA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the original data.
Edit intent: fluency
Original:  In the text classification experiments of BERT and RoBERTa, MixADA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the original data.
Revised:  In the text classification experiments of BERT and RoBERTa, AMDA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the original data.
Edit intent: fluency
Original:  In the text classification experiments of BERT and RoBERTa, MixADA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the original data.
Revised:  In the text classification experiments of BERT and RoBERTa, MixADA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the clean data.
Edit intent: clarity
Original:  However, with the increasing frequency of extreme events, resilience has now become an important consideration. Resilience quantifies the ability of the system to absorb and recover from extreme conditions. Graph theory is a widely used framework for modeling complex engineered systems to evaluate their resilience to attacks. Most existing methods in resilience analysis are based on an iterative approach that explores each node/link of a graph .
Revised:  However, a majority of the methods available in the literature on the identification of critical nodes/links are based on an iterative approach that explores each node/link of a graph .
Edit intent: clarity
Original:  Most existing methods in resilience analysis are based on an iterative approach that explores each node/link of a graph . These methods suffer from high computational complexity and the resulting analysis is network specific.
Revised:  Most existing methods in resilience analysis are based on an iterative approach that explores each node/link of a graph at a time, repeating for all nodes/links in the graph. Such methods suffer from high computational complexity and the resulting analysis is network specific.
Edit intent: meaning-changed
Original:  These methods suffer from high computational complexity and the resulting analysis is network specific. To address these challenges, we propose a graph neural network (GNN) based framework for approximating node resilience in large complex networks.
Revised:  These methods suffer from high computational complexity and the resulting analysis is also network-specific. To overcome these challenges, we propose a graph neural network (GNN) based framework for approximating node resilience in large complex networks.
Edit intent: clarity
Original:  The proposed framework defines a GNN  model that learns the node rank on a small representative subset of nodes . Then, the trained model can be employed to predict the ranks of unseen nodes in similar types of graphs .
Revised:  The proposed framework defines a GNN model that learns the node rank on a small representative subset of nodes /links. An appropriately trained model can be employed to predict the ranks of unseen nodes in similar types of graphs .
Edit intent: clarity
Original:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Revised:  To guide efforts at debiasing these systems , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Edit intent: clarity
Original:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Revised:  To address and combat this , the NLP community relies on a variety of metrics that quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Edit intent: clarity
Original:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Revised:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in models . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Edit intent: clarity
Original:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Revised:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, measuring bias in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Edit intent: coherence
Original:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into.
Revised:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, measuring bias in downstream tasks that the word embeddings are plugged into.
Edit intent: coherence
Original:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings are plugged into. This research examines whether easy-to-measure intrinsic metrics correlate well to real world extrinsic metrics . We measure both intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.
Revised:  To address and combat this , the NLP community relies on a variety of metrics to identify and quantify bias in black-box modelsand to guide efforts at debiasing . Some of these metrics are intrinsic, and are measured in word embedding spaces, and some are extrinsic, which measure the bias present downstream in the tasks that the word embeddings enable. Do these intrinsic and extrinsic metrics correlate with each other? We compare intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.
Edit intent: clarity
Original:  We measure both intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.
Revised:  We measure both intrinsic and extrinsic metrics across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.
Edit intent: clarity
Original:  We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data.
Revised:  We urge researchers working on debiasing to focus on extrinsic measures of bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data.
Edit intent: clarity
Original:  We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data.
Revised:  We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and to make using these measures more feasible via creation of additional challenge sets and annotated test data.
Edit intent: clarity
Original:  We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of additional challenge sets and annotated test data.
Revised:  We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement more feasible via creation of new challenge sets and annotated test data.
Edit intent: clarity
Original:  In this paper, inspired by the Early-Bird Lottery Tickets  studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models.
Revised:  In this paper, inspired by the Early-Bird Lottery Tickets recently studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models.
Edit intent: meaning-changed
Original:  We are the first to identify structured winning tickets in the early stage of BERT training , and use them for efficient training. Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks  show that EarlyBERT easily achieves comparable performance to standard BERT  with 35~45\% less training time. 
Revised:  We are the first to identify structured winning tickets in the early stage of BERT training . We apply those tickets towards efficient BERT training, and conduct comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks show that EarlyBERT easily achieves comparable performance to standard BERT with 35~45\% less training time. 
Edit intent: clarity
Original:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks  show that EarlyBERT easily achieves comparable performance to standard BERT  with 35~45\% less training time. 
Revised:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks . Our results show that EarlyBERT easily achieves comparable performance to standard BERT with 35~45\% less training time. 
Edit intent: clarity
Original:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks  show that EarlyBERT easily achieves comparable performance to standard BERT  with 35~45\% less training time. 
Revised:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks show that EarlyBERT achieves comparable performance to standard BERT with 35~45\% less training time. 
Edit intent: clarity
Original:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks  show that EarlyBERT easily achieves comparable performance to standard BERT  with 35~45\% less training time. 
Revised:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks show that EarlyBERT easily achieves comparable performance to standard BERT , with 35~45\% less training time. 
Edit intent: fluency
Original:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks  show that EarlyBERT easily achieves comparable performance to standard BERT  with 35~45\% less training time. 
Revised:  Comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks show that EarlyBERT easily achieves comparable performance to standard BERT with 35~45\% less training time. Code is available at URL
Edit intent: meaning-changed
Original: The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for many natural language understanding tasks. In relation extraction , dependency trees that contain rich syntactic clues have been widely used to help capture long-term dependencies in text . Graph neural networks (GNNs), one of the means to encode dependency graphs, has been shown effective in several prior works.
Revised: The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for relation extraction (RE) tasks . Graph neural networks (GNNs), one of the means to encode dependency graphs, has been shown effective in several prior works.
Edit intent: clarity
Original:  In relation extraction , dependency trees that contain rich syntactic clues have been widely used to help capture long-term dependencies in text . Graph neural networks (GNNs), one of the means to encode dependency graphs, has been shown effective in several prior works.
Revised:  In relation extraction , dependency trees that contain rich syntactic clues have been widely used to help capture long-term dependencies in text . Graph neural networks (GNNs), one of the means to encode dependency graphs, have been shown to be effective in prior works.
Edit intent: clarity
Original:  In this work, we leverage the idea of graph pooling and propose the Mirror Graph Convolution Network (MrGCN) , a GNN model with  pooling-unpooling structures tailored to relation extraction .
Revised:  In this work, we leverage the idea of graph pooling and propose the Mirror Graph Convolution Network , a GNN model with pooling-unpooling structures tailored to relation extraction .
Edit intent: clarity
Original:  In this work, we leverage the idea of graph pooling and propose the Mirror Graph Convolution Network (MrGCN) , a GNN model with  pooling-unpooling structures tailored to relation extraction .
Revised:  In this work, we leverage the idea of graph pooling and propose the Mirror Graph Convolution Network (MrGCN) , a GNN model with a pooling-unpooling structures tailored to relation extraction .
Edit intent: fluency
Original:  In this work, we leverage the idea of graph pooling and propose the Mirror Graph Convolution Network (MrGCN) , a GNN model with  pooling-unpooling structures tailored to relation extraction .
Revised:  In this work, we leverage the idea of graph pooling and propose the Mirror Graph Convolution Network (MrGCN) , a GNN model with pooling-unpooling structure tailored to RE tasks .
Edit intent: clarity
Original:  the unpooling branch restores the pooled graph to its original resolution such that token-level relation extraction can be performed . Experiments on two  datasets demonstrate the effectiveness of our method, showing significant improvements over previous results .
Revised:  the unpooling branch restores the pooled graph to its original resolution for token-level relation extraction can be performed . Experiments on two datasets demonstrate the effectiveness of our method, showing significant improvements over previous results .
Edit intent: clarity
Original:  the unpooling branch restores the pooled graph to its original resolution such that token-level relation extraction can be performed . Experiments on two  datasets demonstrate the effectiveness of our method, showing significant improvements over previous results .
Revised:  the unpooling branch restores the pooled graph to its original resolution such that token-level RE tasks . Experiments on two datasets demonstrate the effectiveness of our method, showing significant improvements over previous results .
Edit intent: clarity
Original:  the unpooling branch restores the pooled graph to its original resolution such that token-level relation extraction can be performed . Experiments on two  datasets demonstrate the effectiveness of our method, showing significant improvements over previous results .
Revised:  the unpooling branch restores the pooled graph to its original resolution such that token-level relation extraction can be performed . Experiments on two discourse-level relation extraction datasets demonstrate the effectiveness of our method, showing significant improvements over previous results .
Edit intent: meaning-changed
Original:  the unpooling branch restores the pooled graph to its original resolution such that token-level relation extraction can be performed . Experiments on two  datasets demonstrate the effectiveness of our method, showing significant improvements over previous results .
Revised:  the unpooling branch restores the pooled graph to its original resolution such that token-level relation extraction can be performed . Experiments on two datasets demonstrate the effectiveness of our method, showing significant improvements over prior methods especially when modeling long-term dependencies is necessary. Moreover, we propose Clause Matching (CM), a novel graph pooling method that merges nodes based on dependency relations in graph. CM can largely reduce the graph size while retaining the main semantics of the input text .
Edit intent: meaning-changed
Original: This paper is focused on a rigorous variant of the shear strength reduction method (SSR) and the corresponding determination of safety factors. The SSR-based safety factor is defined as a solution of an optimization problem that is independent of the plastic flow rule and the space discretization. In case of nonassociative plasticity, a modified Davis approach is used .
Revised: This paper is focused on a rigorous variant of the shear strength reduction method (SSR) problem with applications to slope stability problems. This new variant is derived on the basis of recent results by Tschuchnigg et al. 2015, where limit analysis and a modified Davis approach is used .
Edit intent: meaning-changed
Original:  In case of nonassociative plasticity, a modified Davis approach is used . The optimization problem is analyzed and the corresponding duality between the static and kinematic principles  is derived.
Revised:  In case of nonassociative plasticity, a modified Davis approach were used for approximation of the standard SSR method. The OPT-SSR method computes the factor of safety without performing an elasto-plastic analysis, similarly as in limit analysis. It is shown that this optimization problem is analyzed and the corresponding duality between the static and kinematic principles is derived.
Edit intent: meaning-changed