wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our observations indicate attention diversification since the COVID-19 outbreak in Mainland China and a higher rank diversity at the top 15 ranks on HSL due to the COVID-19 related hashtags, a drastic attention decay shortly after the outburst and a slower decay for a longer period .
Revised:  Our observations indicate attention diversification since the COVID-19 outbreak in Mainland China and a higher rank diversity at the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider .
Edit intent: meaning-changed
Original:  A Prager-Synge type result relates the  errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Revised:  A Prager--Synge type result relates the errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Edit intent: fluency
Original:  A Prager-Synge type result relates the  errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Revised:  A Prager-Synge type result relates the velocity errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Edit intent: fluency
Original:  A Prager-Synge type result relates the  errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Revised:  A Prager-Synge type result relates the errors of divergence-free primal and )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Edit intent: meaning-changed
Original:  A Prager-Synge type result relates the  errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Revised:  A Prager-Synge type result relates the errors of divergence-free primal and H( perfectly equilibrated dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Edit intent: meaning-changed
Original:  A Prager-Synge type result relates the  errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Revised:  A Prager-Synge type result relates the errors of divergence-free primal and H( )-conforming dual mixed methods for the velocity gradient) with an equilibration constraint that needs special care when discretised.
Edit intent: fluency
Original:  A Prager-Synge type result relates the  errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity gradient) with an equilibration constraint that needs special care when discretised. To relax the constraints on the primal and dual method , a more general result is derived that enables the use of a recently developed mass conserving mixed stress discretisation to design equilibrated fluxes that yield pressure-independent guaranteed upper bounds for any pressure-robust ( but not necessarily divergence-free) primal discretisation.
Revised:  A Prager-Synge type result relates the errors of divergence-free primal and H( )-conforming dual mixed methods ( for the velocity stress. The first main result of the paper is a framework with relaxed constraints on the primal and dual method , a more general result is derived that enables the use of a recently developed mass conserving mixed stress discretisation to design equilibrated fluxes that yield pressure-independent guaranteed upper bounds for any pressure-robust ( but not necessarily divergence-free) primal discretisation.
Edit intent: clarity
Original:  To relax the constraints on the primal and dual method , a more general result is derived that enables the use of a recently developed mass conserving mixed stress discretisation to design equilibrated fluxes that yield pressure-independent guaranteed upper bounds for any pressure-robust ( but not necessarily divergence-free) primal discretisation.
Revised:  To relax the constraints on the primal and dual method . This enables to use a recently developed mass conserving mixed stress discretisation to design equilibrated fluxes that yield pressure-independent guaranteed upper bounds for any pressure-robust ( but not necessarily divergence-free) primal discretisation.
Edit intent: coherence
Original:  To relax the constraints on the primal and dual method , a more general result is derived that enables the use of a recently developed mass conserving mixed stress discretisation to design equilibrated fluxes that yield pressure-independent guaranteed upper bounds for any pressure-robust ( but not necessarily divergence-free) primal discretisation.
Revised:  To relax the constraints on the primal and dual method , a more general result is derived that enables the use of a recently developed mass conserving mixed stress discretisation for the design of equilibrated fluxes and to obtain pressure-independent guaranteed upper bounds for any pressure-robust ( but not necessarily divergence-free) primal discretisation.
Edit intent: clarity
Original:  To relax the constraints on the primal and dual method , a more general result is derived that enables the use of a recently developed mass conserving mixed stress discretisation to design equilibrated fluxes that yield pressure-independent guaranteed upper bounds for any pressure-robust ( but not necessarily divergence-free) primal discretisation.
Revised:  To relax the constraints on the primal and dual method , a more general result is derived that enables the use of a recently developed mass conserving mixed stress discretisation to design equilibrated fluxes that yield pressure-independent guaranteed upper bounds for any pressure-robust ( not necessarily divergence-free) primal discretisation.
Edit intent: fluency
Original:  Moreover, a provably efficient local design of the equilibrated fluxes is presented that reduces the numerical costsof the error estimator. All theoretical findings are verified by numerical examples which also show that the efficiency indices of our novel guaranteed upper bounds for the velocity error are close to 1.
Revised:  Moreover, a provably efficient local design of the equilibrated fluxes with comparably low numerical costs. Numerical examples verify the theoretical findings and show that efficiency indices of our novel guaranteed upper bounds for the velocity error are close to 1.
Edit intent: clarity
Original:  A key ingredient in our model is the construction of Finsler geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features,  region-based homogeneity  and/or curvature regularization.
Revised:  A key ingredient in the introduced model is the construction of Finsler geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, region-based homogeneity and/or curvature regularization.
Edit intent: coherence
Original:  A key ingredient in our model is the construction of Finsler geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features,  region-based homogeneity  and/or curvature regularization.
Revised:  A key ingredient in our model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, region-based homogeneity and/or curvature regularization.
Edit intent: meaning-changed
Original:  A key ingredient in our model is the construction of Finsler geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features,  region-based homogeneity  and/or curvature regularization.
Revised:  A key ingredient in our model is the construction of Finsler geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity and/or curvature regularization.
Edit intent: fluency
Original:  A key ingredient in our model is the construction of Finsler geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features,  region-based homogeneity  and/or curvature regularization.
Revised:  A key ingredient in our model is the construction of Finsler geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, region-based homogeneity features and/or curvature regularization.
Edit intent: fluency
Original:  Moreover, we also introduce a way to build objective simple closed contours , each of which is treated as the concatenation of two disjoint open paths . Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
Revised:  Moreover, we also introduce a way to build simple closed contours , each of which is treated as the concatenation of two disjoint open paths . Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
Edit intent: clarity
Original:  Moreover, we also introduce a way to build objective simple closed contours , each of which is treated as the concatenation of two disjoint open paths . Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
Revised:  Moreover, we also introduce a way to build objective simple closed contours as the concatenation of two disjoint open paths . Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
Edit intent: coherence
Original:  Moreover, we also introduce a way to build objective simple closed contours , each of which is treated as the concatenation of two disjoint open paths . Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
Revised:  Moreover, we also introduce a way to build objective simple closed contours , each of which is treated as the concatenation of two disjoint open curves . Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
Edit intent: clarity
Original:   In this paper, we proposed a motion-based instance segmentation task and created a new annotated dataset based on KITTI , which will be released publicly.
Revised:  Other generic video object segmentation tasks have dealt with instance-wise motion segmentation but on much simpler setting and have not dealt with the multi-task learning problem for both semantic and class agnostic instance segmentation. In this paper, we proposed a motion-based instance segmentation task and created a new annotated dataset based on KITTI , which will be released publicly.
Edit intent: meaning-changed
Original:  We make use of the YOLACT model to solve the instance motion segmentationnetwork by feeding inflow and image as input and instance motion masks as output. We extend it to a multi-task model that learns semantic and motion instance segmentation in a computationally efficient manner.
Revised:  Our dataset provide extra class annotations which is crucial for studying class agnostic segmentation. We further propose an efficient multi-task model that learns semantic and motion instance segmentation in a computationally efficient manner.
Edit intent: meaning-changed
Original:  We extend it to a multi-task model that learns semantic and motion instance segmentation in a computationally efficient manner. Our model is based on sharing a prototype generation network between the two tasks and learning separate prototype coefficients per task .
Revised:  We extend it to a multi-task learning approach that learns an extra class agnostic instance segmentation head through sharing the prototype generation network between the two tasks and learning separate prototype coefficients per task .
Edit intent: meaning-changed
Original:  Based on quadratic and linear voting, we formulated three weighted aggregation methods that differ in the ratio of votes to cumulative points and the weighting of the minority to all members, and calculated the frequency distributions of the aggregation results, assuming that the distributions of votes follow normal distributions .
Revised:  Based on quadratic and linear voting, we formulated three weighted aggregation methods that differ in the ratio of votes to cumulative points and the weighting of the minority to all members, and assuming that the distributions of votes follow normal distributions .
Edit intent: coherence
Original:  Based on quadratic and linear voting, we formulated three weighted aggregation methods that differ in the ratio of votes to cumulative points and the weighting of the minority to all members, and calculated the frequency distributions of the aggregation results, assuming that the distributions of votes follow normal distributions . From these calculation results, we found that minority opinions are  likely to be reflected as weighting increases proportionally in two of the above three methods.
Revised:  Based on quadratic and linear voting, we formulated three weighted aggregation methods that differ in the ratio of votes to cumulative points and the weighting of the minority to all members, and calculated the frequency distributions of the aggregation results, assuming that the distributions of votes follow normal distributions , we calculated the frequency distributions of the aggregation results. We found that minority opinions are likely to be reflected as weighting increases proportionally in two of the above three methods.
Edit intent: coherence
Original:  This means that Sen and Gotoh's idea of considering the social position of unfortunate people on ordinal ranking , that welfare economicsconsiders under an axiomatic approach, was shown by weighting the minority's positionality on cardinal voting.
Revised:  This implies that Sen and Gotoh's idea of considering the social position of unfortunate people on ordinal ranking , that welfare economicsconsiders under an axiomatic approach, was shown by weighting the minority's positionality on cardinal voting.
Edit intent: coherence
Original:  This means that Sen and Gotoh's idea of considering the social position of unfortunate people on ordinal ranking , that welfare economicsconsiders under an axiomatic approach, was shown by weighting the minority's positionality on cardinal voting.
Revised:  This means that Sen and Gotoh's idea of considering the social position of unfortunate people on ordinal ranking in the welfare economics, was illustrated by weighting the minority's positionality on cardinal voting.
Edit intent: clarity
Original: Systems that generate sentences from (abstract ) meaning representations (AMRs) are typically evaluated using automatic surface matching metrics that compare the generated texts to the texts that were originally given to human annotators to construct AMR meaning representations .
Revised: Systems that generate natural language text from abstract meaning representations such as AMR are typically evaluated using automatic surface matching metrics that compare the generated texts to the texts that were originally given to human annotators to construct AMR meaning representations .
Edit intent: clarity
Original: Systems that generate sentences from (abstract ) meaning representations (AMRs) are typically evaluated using automatic surface matching metrics that compare the generated texts to the texts that were originally given to human annotators to construct AMR meaning representations . However, besides well-known issues from which such metrics suffer (Callison-Burch et al., 2006;
Revised: Systems that generate sentences from (abstract ) meaning representations (AMRs) are typically evaluated using automatic surface matching metrics that compare the generated texts to reference texts from which the input meaning representations were constructed. We show that besides well-known issues from which such metrics suffer (Callison-Burch et al., 2006;
Edit intent: clarity
Original:  However, besides well-known issues from which such metrics suffer (Callison-Burch et al., 2006; Novikova et al., 2017), we show that an additional problem arises when applied for AMR-to-text evaluation because mapping from the more abstract domain of AMR to the more concrete domain of sentences allows for manifold sentence realizations.
Revised:  However, besides well-known issues from which such metrics suffer , an additional problem arises when applied for AMR-to-text evaluation because mapping from the more abstract domain of AMR to the more concrete domain of sentences allows for manifold sentence realizations.
Edit intent: coherence
Original:  Novikova et al., 2017), we show that an additional problem arises when applied for AMR-to-text evaluation because mapping from the more abstract domain of AMR to the more concrete domain of sentences allows for manifold sentence realizations.
Revised:  Novikova et al., 2017), we show that an additional problem arises when applying these metrics for AMR-to-text evaluation because mapping from the more abstract domain of AMR to the more concrete domain of sentences allows for manifold sentence realizations.
Edit intent: clarity
Original:  Novikova et al., 2017), we show that an additional problem arises when applied for AMR-to-text evaluation because mapping from the more abstract domain of AMR to the more concrete domain of sentences allows for manifold sentence realizations.
Revised:  Novikova et al., 2017), we show that an additional problem arises when applied for AMR-to-text evaluation , since an abstract meaning representation allows for numerous surface realizations.
Edit intent: clarity
Original:  The first pillar is the principle of meaning preservation \mathcal{M}: it measures to what extent the original AMR graph can be reconstructed from the generated sentence . We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Revised:  The first pillar is the principle of meaning preservation \mathcal{M}: it measures to what extent the original AMR graph can be reconstructed from the generated sentence using SOTA AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Edit intent: clarity
Original:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Revised:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and applying ( fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Edit intent: fluency
Original:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Revised:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained ) AMR evaluation metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Edit intent: clarity
Original:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Revised:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} that measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Edit intent: fluency
Original:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated sentences , which we implement using SOTA language models.
Revised:  We implement this principle by i) automatically constructing an AMR from the generated sentence using state-of-the-art AMR parsers and ii) apply fine-grained principled AMR metrics to measure the distance between the original and the reconstructed AMR. The second pillar builds on a principle of (grammatical) form \mathcal{F} , which measures the linguistic quality of the generated text , which we implement using SOTA language models.
Edit intent: clarity
Original:  We show - theoretically and experimentally - that fulfillment of both principles offers several benefits for evaluation of AMR-to-text systems, including the explainability of scores \mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Revised:  In two extensive pilot studies we show that fulfillment of both principles offers several benefits for evaluation of AMR-to-text systems, including the explainability of scores \mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Edit intent: meaning-changed
Original:  We show - theoretically and experimentally - that fulfillment of both principles offers several benefits for evaluation of AMR-to-text systems, including the explainability of scores \mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Revised:  We show - theoretically and experimentally - that fulfillment of both principles offers benefits for AMR-to-text systems, including the explainability of scores \mathcal{F}_\beta does not necessarily rely on gold AMRs, it may extend to other text generation tasks} .
Edit intent: clarity