wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this survey, we discuss six different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition, and Linear Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects.
Revised:  In this survey, we discuss six different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition, andSub-quadratic Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects.
Edit intent: meaning-changed
Original: Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this URLanizes the plethora of work done by the 'deep learning for NLP' community in the past few years and presents it as a coherent story.
Revised: Given the critical need of building applications with efficient and small models, and the large amount of recently published work inthis area, we believe that this URLanizes the plethora of work done by the 'deep learning for NLP' community in the past few years and presents it as a coherent story.
Edit intent: fluency
Original: Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this URLanizes the plethora of work done by the 'deep learning for NLP' community in the past few years and presents it as a coherent story.
Revised: Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this URLanizes the plethora of work done by the 'deep learning for NLP' community in the past fewyears and presents it as a coherent story.
Edit intent: fluency
Original: Task-Oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generator (NLG).
Revised: Task-oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generator (NLG).
Edit intent: fluency
Original: Task-Oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generator (NLG).
Revised: Task-Oriented dialogue systems use four connected modules , namely, Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generator (NLG).
Edit intent: clarity
Original: Task-Oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generator (NLG).
Revised: Task-Oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language Generator (NLG).
Edit intent: fluency
Original: Task-Oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generator (NLG).
Revised: Task-Oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generation (NLG).
Edit intent: fluency
Original:  Differently, language models  such as GPT-2 (Radford et al., 2019) and GPT-3  Brown et al., 2020) allows few-shot learning by priming the model with few-examples .
Revised:  Differently, language models , such as GPT-2 (Radford et al., 2019) and GPT-3 Brown et al., 2020) allows few-shot learning by priming the model with few-examples .
Edit intent: fluency
Original:  Differently, language models  such as GPT-2 (Radford et al., 2019) and GPT-3  Brown et al., 2020) allows few-shot learning by priming the model with few-examples .
Revised:  Differently, language models such as GPT-2 (Radford et al., 2019) and GPT-3 ( Brown et al., 2020) allows few-shot learning by priming the model with few-examples .
Edit intent: fluency
Original:  Differently, language models  such as GPT-2 (Radford et al., 2019) and GPT-3  Brown et al., 2020) allows few-shot learning by priming the model with few-examples .
Revised:  Differently, language models such as GPT-2 (Radford et al., 2019) and GPT-3 Brown et al., 2020) , allow few-shot learning by priming the model with few-examples .
Edit intent: fluency
Original:  Differently, language models  such as GPT-2 (Radford et al., 2019) and GPT-3  Brown et al., 2020) allows few-shot learning by priming the model with few-examples .
Revised:  Differently, language models such as GPT-2 (Radford et al., 2019) and GPT-3 Brown et al., 2020) allows few-shot learning by priming the model with few examples .
Edit intent: fluency
Original:  Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the pareto front using the bisection method.
Revised:  Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method.
Edit intent: fluency
Original:   On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability.
Revised:  Results: On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability.
Edit intent: fluency
Original:   On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability.
Revised:  On a dataset of 21 prostate cases collected as part of clinical workflow , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability.
Edit intent: clarity
Original:   On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability.
Revised:  On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces Pareto optimal plans that perform well in regards to clinical acceptability.
Edit intent: fluency
Original:   On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability.
Revised:  On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that are clinically acceptable in regards to clinical acceptability.
Edit intent: clarity
Original:   On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability. Compared to the SF scores of manually generated plans, SF scores for POPS plans were significantly better (p=2.6e-7).
Revised:  On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC) , our proposed POPS algorithm produces pareto optimal plans that perform well in regards to dose conformity, dose homogeneity, and sparing URLans-at-risk. Conclusion:
Edit intent: meaning-changed
Original:  To promote jump sparsity, we use an isotropic and anisotropic \ell^{0 combined with a quadratic data fidelity, similarly as studied in [1] for general problems in signal recovery .
Revised:  We enforce this assumption by considering both an isotropic and anisotropic \ell^{0 combined with a quadratic data fidelity, similarly as studied in [1] for general problems in signal recovery .
Edit intent: meaning-changed
Original:  To promote jump sparsity, we use an isotropic and anisotropic \ell^{0 combined with a quadratic data fidelity, similarly as studied in [1] for general problems in signal recovery .
Revised:  To promote jump sparsity, we use an isotropic and an anisotropic \ell^0 regularisation on the image gradient combined with a quadratic data fidelity, similarly as studied in [1] for general problems in signal recovery .
Edit intent: meaning-changed
Original:  To promote jump sparsity, we use an isotropic and anisotropic \ell^{0 combined with a quadratic data fidelity, similarly as studied in [1] for general problems in signal recovery .
Revised:  To promote jump sparsity, we use an isotropic and anisotropic \ell^{0 combined with a quadratic data fidelity, similarly as studied in [1] for general signal recovery problems .
Edit intent: clarity
Original:  For the numerical realisation of the model, we consider a converging ADMM algorithm. Differently from 1%DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Revised:  For the numerical realisation of the model, we 1%DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Edit intent: clarity
Original:  Differently from 1%DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Revised:  Differently from %DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Edit intent: fluency
Original:  Differently from 1%DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Revised:  Differently from 1%DIFDELCMD < ]%%% 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Edit intent: fluency
Original:  Differently from 1%DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Revised:  Differently from 1%DIFDELCMD < ]%%% , %DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Edit intent: clarity
Original:  Differently from 1%DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% , where approximate graph cuts and dynamic programming techniques were used for solving the non-convex substeps in the case of multivariate data, the proposed splitting allows to compute explicitly their solution by means of hard-thresholding and standard conjugate-gradient solvers.
Revised:  Differently from 1%DIFDELCMD < ]%%% , 2%DIFDELCMD < ]%%% propose a novel efficient ADMM splitting algorithm whose substeps solutions are computed efficiently by means of hard-thresholding and standard conjugate-gradient solvers.
Edit intent: meaning-changed
Original:  We compare quantitatively our results with several convex, nonconvex and deep-learning-based approaches for several synthetic and real-world data. Our numerical results show that combining super-resolution with gradient sparsity is particularly helpful for object detection and labelling tasks (such as QR scanning and land-cover classification), for which our results are shown to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures 3 .
Revised:  We compare quantitatively our results with several variational approaches as well as with state-of-the-art deep-learning techniques. Our experiments show that \ell^0 gradient-regularised super-resolved images can be effectively used to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures 3 .
Edit intent: clarity
Original:  Our numerical results show that combining super-resolution with gradient sparsity is particularly helpful for object detection and labelling tasks (such as QR scanning and land-cover classification), for which our results are shown to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures 3 .
Revised:  Our numerical results show that combining super-resolution with gradient sparsity is particularly helpful for object detection and labelling tasks (such as QR scanning and land-cover classification), for which our results are shown to improve the 3 .
Edit intent: clarity
Original:  Our numerical results show that combining super-resolution with gradient sparsity is particularly helpful for object detection and labelling tasks (such as QR scanning and land-cover classification), for which our results are shown to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures 3 .
Revised:  Our numerical results show that combining super-resolution with gradient sparsity is particularly helpful for object detection and labelling tasks (such as QR scanning and land-cover classification), for which our results are shown to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures accuracy of standard segmentation algorithms when applied to QR and cell detection, and landcover classification problems, in comparison to the results achieved by other approaches .
Edit intent: meaning-changed
Original: The ongoing COVID-19 pandemic caused by SARS-CoV-2 virus, a highly contagious virus, affected most of the countries of Earth. COVID-19 is causing obstacles for public URLanizations and is affecting almost every aspect of human life.
Revised: The ongoing COVID-19 pandemic caused by SARS-CoV-2 virus, a highly contagious virus, affected most of the countries on Earth.
Edit intent: coherence
Original:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Revised:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and edge weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Edit intent: fluency
Original:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Revised:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ e })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Edit intent: fluency
Original:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Revised:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ e \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Edit intent: fluency
Original:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Revised:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in E }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Edit intent: clarity
Original:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Revised:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has probing constraints ( _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Edit intent: clarity
Original:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Revised:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where _v indicates which sequences of edges adjacent to an online vertex v which can be probed.
Edit intent: clarity
Original:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v which can be probed.
Revised:  More specifically, we consider a bipartite stochastic graph G = (U,V,E) where U is the set of offline vertices, V is the set of online vertices and G has edge probabilities (p_{e})_{e \in E} and vertex weights (w_{ u })_{ u \in U }. Additionally, G has patience values (\ell _{v})_{v \in V}, where \ell_v indicates the maximum number of edges adjacent to an online vertex v can be probed.
Edit intent: clarity
Original:  We assume that U and (w_{u)_{u \in U} are }  known in advance, and that the patience, adjacent edges and edge probabilities for each \scr{C} online vertex are only revealed when the online vertex arrives.
Revised:  We assume that U )_{u \in U} are } known in advance, and that the patience, adjacent edges and edge probabilities for each \scr{C} online vertex are only revealed when the online vertex arrives.
Edit intent: meaning-changed
Original:  We assume that U and (w_{u)_{u \in U} are }  known in advance, and that the patience, adjacent edges and edge probabilities for each \scr{C} online vertex are only revealed when the online vertex arrives.
Revised:  We assume that U and (w_{u)_{u \in U} are } is known in advance, and that the patience, adjacent edges and edge probabilities for each \scr{C} online vertex are only revealed when the online vertex arrives.
Edit intent: fluency
Original:  We assume that U and (w_{u)_{u \in U} are }  known in advance, and that the patience, adjacent edges and edge probabilities for each \scr{C} online vertex are only revealed when the online vertex arrives.
Revised:  We assume that U and (w_{u)_{u \in U} are } known in advance, and that \scr{C} online vertex are only revealed when the online vertex arrives.
Edit intent: meaning-changed
Original:  We assume that U and (w_{u)_{u \in U} are }  known in advance, and that the patience, adjacent edges and edge probabilities for each \scr{C} online vertex are only revealed when the online vertex arrives.
Revised:  We assume that U and (w_{u)_{u \in U} are } known in advance, and that the patience, adjacent edges and edge probabilities for each \scr{C}_v, together with the edge probabilities and weights adjacent to an online vertex are only revealed when the online vertex arrives.
Edit intent: meaning-changed
Original:  }%DIFDELCMD < \\ %%% (2) When the online vertex probabilities are "vertex uniform"; i.e., p_{u,v = p_v for all (u,v) \in E. }%DIFDELCMD < \\ %%% (3) When the patience constraint \ell_v satisfies \ell_v \in \{ 1,|U|\} for every online vertex;
Revised:  }%DIFDELCMD < \\ %%% = p_v for all (u,v) \in E. }%DIFDELCMD < \\ %%% (3) When the patience constraint \ell_v satisfies \ell_v \in \{ 1,|U|\} for every online vertex;
Edit intent: clarity
Original:  i.e., p_{u,v = p_v for all (u,v) \in E. }%DIFDELCMD < \\ %%% (3) When the patience constraint \ell_v satisfies \ell_v \in \{ 1,|U|\} for every online vertex;
Revised:  i.e., p_{u,v = p_v for all (u,v) \in E. }%DIFDELCMD < \\ %%% 1,|U|\} for every online vertex;
Edit intent: meaning-changed