wanyu/IteraTeR_full_sent huggingface dataset:
Original: Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS. In this work, we have developed an Intent Bulk Labeling system for SDS developers . The users can interactively label and augment training data from unlabeled utterance corpora using advanced clustering and visual labeling methods.
Revised: Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS. In this work, we have developed an Intent Bulk Labeling system where SDS developers can interactively label and augment training data from unlabeled utterance corpora using advanced clustering and visual labeling methods.
Edit intent: clarity
Original:  To this end, we present our new Arabic Stance Detection dataset (AraStance) of 910 claims from a diverse set of sources comprising three fact-checking websites and one news website.
Revised:  To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claim--article pairs from a diverse set of sources comprising three fact-checking websites and one news website.
Edit intent: meaning-changed
Original:  AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is wellbalanced between related and unrelated documents with respect to the claims.
Revised:  AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is well-balanced between related and unrelated documents with respect to the claims.
Edit intent: fluency
Original: Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-crafted frameworks for robotic manipulation tasks . Surprisingly, the extension of these methods to the multiview domain is relatively unexplored.
Revised: Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-crafted frameworks for robotic manipulation . Surprisingly, the extension of these methods to the multiview domain is relatively unexplored.
Edit intent: clarity
Original: Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-crafted frameworks for robotic manipulation tasks . Surprisingly, the extension of these methods to the multiview domain is relatively unexplored.
Revised: Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-crafted frameworks for robotic manipulation tasks . Surprisingly, an extension of these methods to the multiview domain is relatively unexplored.
Edit intent: fluency
Original: Absolute Concentration Robustness (ACR) was introduced by Shinar and Feinberg as a way to define species concentration robustness in mass action dynamical systems. The idea was to devise a mathematical condition that will ensure robustness in the function of the biological system being modeled.
Revised: Absolute Concentration Robustness (ACR) was introduced by Shinar and Feinberg as a way to define species concentration robustness in mass action dynamical system. Their aim was to devise a mathematical condition that will ensure robustness in the function of the biological system being modeled.
Edit intent: clarity
Original: We describe a method to select the nodes in Graph datasets for training so that the model trained on the points selected will be be better than the ones if we select other points for the purpose of training .
Revised: The process of selecting points for training a machine learning model is often a challenging task. Many times, we will have a lot of data, but for training, we require the labels and labeling is often costly. So we need to select the nodes in Graph datasets for training so that the model trained on the points selected will be be better than the ones if we select other points for the purpose of training .
Edit intent: meaning-changed
Original: We describe a method to select the nodes in Graph datasets for training so that the model trained on the points selected will be be better than the ones if we select other points for the purpose of training .
Revised: We describe a method to select the nodes in Graph datasets for training so that the model trained on the points selected will be be better than the ones trained on any other training set. We propose a novel method to select the nodes in graph datasets using the concept of graph centrality. Two methods are proposed - one using a smart selection strategy, where the model is required to be trained only once and another using active learning method .
Edit intent: meaning-changed
Original:  This is a very important aspect as the process of labelling the points is often a costly affair. The usual Active Learning methods are good but the penalty involved with these methods is that, we need to re-train the model after selecting the nodes in each iteration of Active Learning cycle. We come up with a method which use the concept of Graph Centrality to select the nodes for labeling and training initially and the training is needed to perform only once .
Edit intent: coherence
Original:  This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to freshen chronic disease prevention literacy targeted for times when e-commerce user experience has been assimilated into most people's daily life .
Revised:  This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to freshen chronic disease prevention literacy targeted for times when e-commerce user experience has been assimilated into most people's daily lives .
Edit intent: fluency
Original:  Further, such empirical findings could be adopted to locate online shoppers at high risk of  chronic diseases with fair accuracy (e.g., area under the receiver operating characteristic curve%DIFDELCMD < ] %%% AUC=0.68 for depression and AUC=0.70 for diabetes) , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Revised:  Further, such empirical findings could be adopted to locate online shoppers at high risk of these chronic diseases with fair accuracy (e.g., area under the receiver operating characteristic curve%DIFDELCMD < ] %%% AUC=0.68 for depression and AUC=0.70 for diabetes) , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Edit intent: clarity
Original:  Further, such empirical findings could be adopted to locate online shoppers at high risk of  chronic diseases with fair accuracy (e.g., area under the receiver operating characteristic curve%DIFDELCMD < ] %%% AUC=0.68 for depression and AUC=0.70 for diabetes) , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Revised:  Further, such empirical findings could be adopted to locate online shoppers at high risk of chronic diseases with fair accuracy area under the receiver operating characteristic curve%DIFDELCMD < ] %%% AUC=0.68 for depression and AUC=0.70 for diabetes) , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Edit intent: coherence
Original:  Further, such empirical findings could be adopted to locate online shoppers at high risk of  chronic diseases with fair accuracy (e.g., area under the receiver operating characteristic curve%DIFDELCMD < ] %%% AUC=0.68 for depression and AUC=0.70 for diabetes) , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Revised:  Further, such empirical findings could be adopted to locate online shoppers at high risk of chronic diseases with fair accuracy (e.g., %DIFDELCMD < ] %%% AUC=0.68 for depression and AUC=0.70 for diabetes) , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Edit intent: clarity
Original:  Further, such empirical findings could be adopted to locate online shoppers at high risk of  chronic diseases with fair accuracy (e.g., area under the receiver operating characteristic curve%DIFDELCMD < ] %%% AUC=0.68 for depression and AUC=0.70 for diabetes) , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Revised:  Further, such empirical findings could be adopted to locate online shoppers at high risk of chronic diseases with fair accuracy (e.g., area under the receiver operating characteristic curve%DIFDELCMD < ] %%% , closely matching the performance of screening surveys benchmarked against medical diagnosis.
Edit intent: coherence
Original:  In this paper, we construct a challenging and realistic ASR federated experimental setup consisting of clients with heterogeneous data distributions using the French Common Voice dataset, a large heterogeneous dataset containing over 10k speakers .
Revised:  In this paper, we construct a challenging and realistic ASR federated experimental setup consisting of clients with heterogeneous data distributions using the French and Italian sets of the CommonVoice dataset, a large heterogeneous dataset containing over 10k speakers .
Edit intent: meaning-changed
Original:  In this paper, we construct a challenging and realistic ASR federated experimental setup consisting of clients with heterogeneous data distributions using the French Common Voice dataset, a large heterogeneous dataset containing over 10k speakers .
Revised:  In this paper, we construct a challenging and realistic ASR federated experimental setup consisting of clients with heterogeneous data distributions using the French Common Voice dataset, a large heterogeneous dataset containing thousands of different speakers, acoustic environments and noises .
Edit intent: meaning-changed
Original:  We present the first empirical study on attention-based sequence-to-sequence  E2E  ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients.
Revised:  We present the first empirical study on attention-based sequence-to-sequence End-to-End ( E2E ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients.
Edit intent: meaning-changed
Original:  We present the first empirical study on attention-based sequence-to-sequence  E2E  ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients.
Revised:  We present the first empirical study on attention-based sequence-to-sequence E2E ) ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients.
Edit intent: fluency
Original:  We present the first empirical study on attention-based sequence-to-sequence  E2E  ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients.
Revised:  We present the first empirical study on attention-based sequence-to-sequence E2E ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, compared in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients.
Edit intent: coherence
Original:  We present the first empirical study on attention-based sequence-to-sequence  E2E  ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients.
Revised:  We present the first empirical study on attention-based sequence-to-sequence E2E ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10 clients and cross-device with 2k-clients.
Edit intent: fluency
Original:  We present the first empirical study on attention-based sequence-to-sequence  E2E  ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2k-clients. In particular, the WER-based weighting method is proposed to better adapt FL to the context of ASR by integrating the error rate metric with the aggregation process .
Revised:  We present the first empirical study on attention-based sequence-to-sequence E2E ASR model with three aggregation weighting strategies -- standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, are conducted in two realistic FL scenarios: cross-silo with 10-clients and cross-device with 2K and 4K clients .
Edit intent: meaning-changed
Original:  In this paper, we propose a vision-based human arm gesture recognition model based on the 1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic data sets have shown the effectiveness of this method in identifying arm gestures.
Revised:  In this paper, we propose a vision-based hand gesture recognition model based on the 1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic data sets have shown the effectiveness of this method in identifying arm gestures.
Edit intent: clarity
Original:  In this paper, we propose a vision-based human arm gesture recognition model based on the 1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic data sets have shown the effectiveness of this method in identifying arm gestures.
Revised:  In this paper, we propose a vision-based human arm gesture recognition model based on the 1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on binary and gray-scale data sets have shown the effectiveness of this method in identifying arm gestures.
Edit intent: meaning-changed
Original:  In this paper, we propose a vision-based human arm gesture recognition model based on the 1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic data sets have shown the effectiveness of this method in identifying arm gestures.
Revised:  In this paper, we propose a vision-based human arm gesture recognition model based on the 1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic data sets have shown the effectiveness of this method in identifying hand gestures.
Edit intent: clarity
Original:  Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54 x ) and a similar amount of network traffic (1.002 x ) compared to the standard federated learning of a complete model.
Revised:  Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54 \times ) and a similar amount of network traffic (1.002 x ) compared to the standard federated learning of a complete model.
Edit intent: meaning-changed
Original:  Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54 x ) and a similar amount of network traffic (1.002 x ) compared to the standard federated learning of a complete model.
Revised:  Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54 x ) and a similar amount of network traffic (1.002 \times ) compared to the standard federated learning of a complete model.
Edit intent: meaning-changed
Original:  Building upon these concepts, we define fair-PAC  learning, where a fair PAC-learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any  malfare concept.
Revised:  Building upon these concepts, we define fair-PAC (FPAC) learning, where a fair PAC-learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any malfare concept.
Edit intent: clarity
Original:  Building upon these concepts, we define fair-PAC  learning, where a fair PAC-learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any  malfare concept.
Revised:  Building upon these concepts, we define fair-PAC learning, where an FPAC learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any malfare concept.
Edit intent: clarity
Original:  Building upon these concepts, we define fair-PAC  learning, where a fair PAC-learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any  malfare concept.
Revised:  Building upon these concepts, we define fair-PAC learning, where a fair PAC-learner is an algorithm that learns an \varepsilon-\delta malfare-optimal model with bounded sample complexity, for any data distribution, and for any (axiomatically justified) malfare concept.
Edit intent: meaning-changed
Original:  This places fair-PAC learning on firm theoretical ground, as it yields statistical , and in some cases computational , efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models .
Revised:  This places FPAC learning on firm theoretical ground, as it yields statistical , and in some cases computational , efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models .
Edit intent: clarity
Original:  This places fair-PAC learning on firm theoretical ground, as it yields statistical , and in some cases computational , efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models .
Revised:  This places fair-PAC learning on firm theoretical ground, as it yields statistical and computational efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models .
Edit intent: clarity
Original:  This places fair-PAC learning on firm theoretical ground, as it yields statistical , and in some cases computational , efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models .
Revised:  This places fair-PAC learning on firm theoretical ground, as it yields statistical , and in some cases computational , efficiency guarantees for many well-studied machine-learning models, and is also practically relevant, as it democratizes fair ML by providing concrete training algorithms and rigorous generalization guarantees for these models 
Edit intent: fluency
Original:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) has been presented in R.D. Kongskov%DIFDELCMD < & %%% Y. Dong, SSVM 2017 , LNCS 10302, 2017;
Revised:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) R.D. Kongskov%DIFDELCMD < & %%% Y. Dong, SSVM 2017 , LNCS 10302, 2017;
Edit intent: clarity
Original:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) has been presented in R.D. Kongskov%DIFDELCMD < & %%% Y. Dong, SSVM 2017 , LNCS 10302, 2017;
Revised:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) has been presented in %DIFDELCMD < & %%% Y. Dong, SSVM 2017 , LNCS 10302, 2017;
Edit intent: coherence
Original:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) has been presented in R.D. Kongskov%DIFDELCMD < & %%% Y. Dong, SSVM 2017 , LNCS 10302, 2017;
Revised:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) has been presented in R.D. Kongskov%DIFDELCMD < & %%% was developed by Kongskov et al. in 2017 , LNCS 10302, 2017;
Edit intent: meaning-changed
Original:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) has been presented in R.D. Kongskov%DIFDELCMD < & %%% Y. Dong, SSVM 2017 , LNCS 10302, 2017; R.D. Kongskov, Y. Dong K. Knudsen, BIT 59, 2019  in the case of impulse and Gaussian noise.
Revised:  In order to deal with these problems, the Directional Total Generalized Variation (DTGV) has been presented in R.D. Kongskov%DIFDELCMD < & %%% Y. Dong, SSVM 2017 K. Knudsen, BIT 59, 2019 in the case of impulse and Gaussian noise.
Edit intent: coherence
Original:  In this work we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback-Leibler divergence.
Revised:  In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback-Leibler divergence.
Edit intent: clarity
Original:  In this paper, we investigate the instability and asymptotic behavior of planar fronts and planar pulses of the bidomain Allen--Cahn equation and the bidomain FitzHugh--Nagumo equation in two spatial dimension.
Revised:  In this paper, we investigate the instability and asymptotic behavior of planar fronts and planar pulses of the bidomain Allen-Cahn equation and the bidomain FitzHugh--Nagumo equation in two spatial dimension.
Edit intent: fluency
Original:  In this paper, we investigate the instability and asymptotic behavior of planar fronts and planar pulses of the bidomain Allen--Cahn equation and the bidomain FitzHugh--Nagumo equation in two spatial dimension.
Revised:  In this paper, we investigate the instability and asymptotic behavior of planar fronts and planar pulses of the bidomain Allen--Cahn equation and the bidomain FitzHugh-Nagumo equation in two spatial dimension.
Edit intent: fluency
Original:  In our computational studies of the bidomain FitzHugh--Nagumo pulse solution, we show that the pulses can also become unstable much like the bidomain Allen--Cahn fronts.
Revised:  In our computational studies of the bidomain FitzHugh-Nagumo pulse solution, we show that the pulses can also become unstable much like the bidomain Allen--Cahn fronts.
Edit intent: fluency