wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that our approach substantially outperforms baselines and other alternative approaches given the same amount of data, and, through detailed analysis, we argue that data augmentation with the pseudo data from unsupervised machine translation is especially effective for BWEs because (1) the pseudo data makes the source and target corpora (partially) parallel;
Revised:  We show that our approach outperforms other alternative approaches given the same amount of data, and, through detailed analysis, we argue that data augmentation with the pseudo data from unsupervised machine translation is especially effective for BWEs because (1) the pseudo data makes the source and target corpora (partially) parallel;
Edit intent: clarity
Original:  We show that our approach substantially outperforms baselines and other alternative approaches given the same amount of data, and, through detailed analysis, we argue that data augmentation with the pseudo data from unsupervised machine translation is especially effective for BWEs because (1) the pseudo data makes the source and target corpora (partially) parallel;
Revised:  We show that our approach substantially outperforms baselines and other alternative approaches given the same amount of data, and, through detailed analysis, we show that data augmentation with the pseudo data from unsupervised machine translation is especially effective for BWEs because (1) the pseudo data makes the source and target corpora (partially) parallel;
Edit intent: clarity
Original:  We show that our approach substantially outperforms baselines and other alternative approaches given the same amount of data, and, through detailed analysis, we argue that data augmentation with the pseudo data from unsupervised machine translation is especially effective for BWEs because (1) the pseudo data makes the source and target corpora (partially) parallel;
Revised:  We show that our approach substantially outperforms baselines and other alternative approaches given the same amount of data, and, through detailed analysis, we argue that data augmentation with the pseudo data from unsupervised machine translation is especially effective for mapping-based CLWEs because (1) the pseudo data makes the source and target corpora (partially) parallel;
Edit intent: meaning-changed
Original:  We study the differences in the two equations as they pertain to the one-dimensional rock-paper-scissors game . In particular, we show that a constant amplitude traveling wave solution exists for the infinite population case and show how population collapse prevents its formation in the finite population case. This is closely related to recent work by Postlethwaite and Rucklidge on traveling waves in a similar but distinct rock-paper-scissor setting. Additional solution classes in variations of rock-paper-scissors are also studied .
Revised:  We study the differences in the two equations as they pertain to the one-dimensional rock-paper-scissors game .
Edit intent: coherence
Original:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds  . Different from existing near-optimal methods, PStorm requires  only one or O(1) samples in every update.
Revised:  With a momentum-based variance reduction technique, PStorm can achieve the optimal complexity result O (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds . Different from existing near-optimal methods, PStorm requires only one or O(1) samples in every update.
Edit intent: meaning-changed
Original:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds  . Different from existing near-optimal methods, PStorm requires  only one or O(1) samples in every update.
Revised:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds and \Theta(\varepsilon^{-1 . Different from existing near-optimal methods, PStorm requires only one or O(1) samples in every update.
Edit intent: meaning-changed
Original:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds  . Different from existing near-optimal methods, PStorm requires  only one or O(1) samples in every update.
Revised:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds . Different from existing optimal methods, PStorm requires only one or O(1) samples in every update.
Edit intent: fluency
Original:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds  . Different from existing near-optimal methods, PStorm requires  only one or O(1) samples in every update.
Revised:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds . Different from existing near-optimal methods, PStorm can still achieve a near-optimal complexity result only one or O(1) samples in every update.
Edit intent: clarity
Original:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds  . Different from existing near-optimal methods, PStorm requires  only one or O(1) samples in every update.
Revised:  With a momentum-based variance reduction technique, PStorm can achieve a near-optimal complexity result (\varepsilon^{-3}) to produce a stochastic \varepsilon-stationary solution, if a mean-squared smoothness condition holds . Different from existing near-optimal methods, PStorm requires (\varepsilon^{-3 only one or O(1) samples in every update.
Edit intent: fluency
Original:  In addition, for large-scale machine learning problems, PStorm can generalize better by small-batch training than other near-optimal methods that require large-batch training and the vanilla SGM, as we demonstrate on training a sparse fully-connected  neural network.
Revised:  In addition, for large-scale machine learning problems, PStorm can generalize better by small-batch training than other optimal methods that require large-batch training and the vanilla SGM, as we demonstrate on training a sparse fully-connected neural network.
Edit intent: clarity
Original:  In addition, for large-scale machine learning problems, PStorm can generalize better by small-batch training than other near-optimal methods that require large-batch training and the vanilla SGM, as we demonstrate on training a sparse fully-connected  neural network.
Revised:  In addition, for large-scale machine learning problems, PStorm can generalize better by small-batch training than other near-optimal methods that require large-batch training and the vanilla SGM, as we demonstrate on training a sparse fully-connected neural network and a sparse convolutional neural network.
Edit intent: meaning-changed
Original:  This approach combines the best from both worlds, firstly the advantages of a piece-wise discontinuous high--order accurate approximation and secondly the advantages of an unfitted to the true geometry grid around possibly complex objects and/or geometrical deformations.
Revised:  This approach combines the best from both worlds, firstly the advantages of a piece-wise discontinuous high-order accurate approximation and secondly the advantages of an unfitted to the true geometry grid around possibly complex objects and/or geometrical deformations.
Edit intent: fluency
Original:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ]  ] velocity and pressure ghost penalty terms.
Revised:  To enhance stability we enrich the discrete variational formulation with a pressure stabilization term. Moreover, the present contribution adopts high order ghost penalty strategies to address the ill conditioning of the system matrix caused by small truncated elements with respect to the unfitted boundary. Motivated by continuous unfitted FEM ] ] velocity and pressure ghost penalty terms.
Edit intent: meaning-changed
Original:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ]  ] velocity and pressure ghost penalty terms.
Revised:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone 21,75,76] ] velocity and pressure ghost penalty terms.
Edit intent: meaning-changed
Original:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ]  ] velocity and pressure ghost penalty terms.
Revised:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ] along with other unfitted mesh surveys grounded on discontinuous spaces ] velocity and pressure ghost penalty terms.
Edit intent: meaning-changed
Original:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ]  ] velocity and pressure ghost penalty terms.
Revised:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ] 10,44-46,74] velocity and pressure ghost penalty terms.
Edit intent: meaning-changed
Original:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ]  ] velocity and pressure ghost penalty terms.
Revised:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ] ], we use proper velocity and pressure ghost penalty terms.
Edit intent: meaning-changed
Original:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ]  ] velocity and pressure ghost penalty terms. The present contribution should prove valuable in engineering applications where special emphasis is placed on the optimal effective approximation attaining much smaller relative errors in coarser meshes.
Revised:  A fully stabilized frame is required for equal order finite elements, both --proper for higher-order-- pressure Poisson stabilization in the bulk of the domain, as well as boundaryzone ] ] velocity and pressure ghost penalties defined on faces of cut cells to establish a robust high-order method, in spite of the cell agglomeration technique usually applied on dG methods. The current presentation should prove valuable in engineering applications where special emphasis is placed on the optimal effective approximation attaining much smaller relative errors in coarser meshes.
Edit intent: meaning-changed
Original:  Our analysis of the stability properties of the proposed scheme reveals that a delicate scaling of the stabilization parameters is required for the equal higher-order case. This is also supported by numerical evidence from test experiments. Additionally, a geometrically robust estimate for the condition number of the stiffness matrix is provided. Numerical examples illustrate the implementation of the method and verify the theoretical findings .
Revised:  Numerical examples verify the theoretical findings .
Edit intent: coherence
Original:  Based on an energy estimate for the viscous-plastic sea-ice model we define an energy for the elastic-viscous-plastic and viscous-plastic sea-ice model .
Revised:  To show that the stabilized Crouzeix-Raviart approximation is qualitative consistent with the solution of the continuous sea-ice model we define an energy for the elastic-viscous-plastic and viscous-plastic sea-ice model .
Edit intent: meaning-changed
Original: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Revised: We present a data structure that in a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Edit intent: fluency
Original: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Revised: We present a data structure that for a dynamic graph of treedepth at most d , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Edit intent: fluency
Original: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Revised: We present a data structure that for a dynamic graph G , which is modified over time by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Edit intent: clarity
Original: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Revised: We present a data structure that for a dynamic graph G , which is updated by edge insertions and deletions, maintains an optimum-height elimination forest. The data structure achieves worst-case update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Edit intent: meaning-changed
Original: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Revised: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( d ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Edit intent: fluency
Original: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, assuming access to a dictionary on the edges of G. Underlying this result lies a data structure that maintains an optimum-depth elimination forest in a dynamic graphof treedepth at most d with update time 2^{O(d^2) .
Revised: We present a data structure that for a dynamic graph G , which is updated by edge insertions and removals, maintains the answer to the query whether G contains a simple path on k vertices with amortized update time 2^{ {\cal O ( k ^2)}, which matches the best known parameter dependency in the running time of a static fpt algorithm for computing the treedepth of a graph .
Edit intent: meaning-changed
Original:  [ESA 2014], who for the same problem achieved update time f(d) for a non-elementary function f(d)}.
Revised:  [ESA 2014], who for the same problem achieved update time f(d) for a non-elementary (i.e. tower-exponential) function f. As a by-product, we improve known upper bounds on the sizes of minimal obstructions for having treedepth d from doubly-exponential in d to d^{{\cal O(d)}.
Edit intent: meaning-changed
Original: In this survey, we provide a comprehensive description of recent neural entity linking (EL) systems . We distill their generic architecture that includes candidate generation , entity ranking , and unlinkable mention prediction components. For each of them, we summarize the prominent methods and models, including approaches to mention encoding based on the self-attention architecture.
Revised: In this survey, we provide a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in NLP. Our goal is to systemize design features of neural entity linking systems and compare their performances to the best classic methods on the common benchmarks. We distill generic architectural components of a neural EL system, like candidate generation and entity ranking summarizing the prominent methods for each of them, we summarize the prominent methods and models, including approaches to mention encoding based on the self-attention architecture.
Edit intent: meaning-changed
Original:  Since many EL models take advantage of entity embeddings to improve their generalization capabilities, we provide an overview of the widely-used entity embedding techniques. We group the variety of EL approaches by several common research directions : joint entity recognition and linking, models for global EL , domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.
Revised:  The vast variety of modifications of this general neural entity linking architecture are grouped by several common research directions : joint entity recognition and linking, models for global EL , domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.
Edit intent: clarity
Original:  We group the variety of EL approaches by several common research directions : joint entity recognition and linking, models for global EL , domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.
Revised:  We group the variety of EL approaches by several common themes : joint entity recognition and linking, models for global EL , domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.
Edit intent: clarity
Original:  We group the variety of EL approaches by several common research directions : joint entity recognition and linking, models for global EL , domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.
Revised:  We group the variety of EL approaches by several common research directions : joint entity recognition and linking, models for global linking , domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.
Edit intent: clarity
Original:  We also discuss the novel application of EL for enhancing word representation models like BERT. We systemize the critical design features of EL systems and provide their reported evaluation results .
Revised:  Since many neural models take advantage of pre-trained entity embeddings to improve their generalization capabilities, we provide an overview of popular entity embedding techniques. Finally, we briefly discuss applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models such as BERT .
Edit intent: meaning-changed
Original:  In addition, we can incorporate a box constraint that is reasonable for imaging applications . Numerical schemes are based on the alternating direction method of multipliers (ADMM), and we provide the convergence analysis of all the proposed algorithms (constrained/unconstrained and with/without the box constraint ).
Revised:  In addition, we incorporate a box constraint that is reasonable for imaging applications . Numerical schemes are based on the alternating direction method of multipliers (ADMM), and we provide the convergence analysis of all the proposed algorithms (constrained/unconstrained and with/without the box constraint ).
Edit intent: coherence