wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In four regulation scenarios--two by two combinations regarding whether AV and PT are allowed to change supplies--we find that since AV can release the bus operator from low-demand routes, the competition can lead to higher profits of both, and higher system efficiency , simultaneously, rather than a one-sided loss-gain result. For PT, after supply updates, spatially the services are concentrated to short feeder routes directly to the subway station , and temporally concentrated to peak hours.
Revised:  In four regulation scenarios--two by two combinations regarding whether AV and PT are allowed to change supplies--we find that since AV can release the bus operator from low-demand routes, the competition can lead to higher profits of both, and higher system efficiency for both operators compared to the status quo. After the supply updates, spatially the services are concentrated to short feeder routes directly to the subway station , and temporally concentrated to peak hours.
Edit intent: clarity
Original:  For system efficiencyand sustainability, bus supply adjustment can increase the bus average load and reduce total PCE , while the AV supply adjustment shows the oppositeeffect. For policy implications, the paper suggests that PT should be allowed to optimize its supply strategies under specific operation goal constraints, and AV operation should be regulated to relieve its externality on the system, including limiting the number of licenses, operation time, and service areas, which makes AV operate like a complementary mode to PT .
Revised:  For system efficiencyand sustainability, bus supply adjustment can increase the bus average load and reduce total PCE , while the AV supply adjustment does the opposite .
Edit intent: coherence
Original: In this paper , we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
Revised: This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
Edit intent: coherence
Original:  Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step.
Revised:  Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step.
Edit intent: clarity
Original:  Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step.
Revised:  Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction that predicts the next n tokens simultaneously based on previous context tokens at each time step.
Edit intent: fluency
Original:  The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area , where the latter pay nodal prices whereas the former are charged a fixed price , which is derived by the underlying nodal prices.
Revised:  The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area . Flexible users will pay nodal prices whereas the former are charged a fixed price , which is derived by the underlying nodal prices.
Edit intent: clarity
Original:  The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area , where the latter pay nodal prices whereas the former are charged a fixed price , which is derived by the underlying nodal prices.
Revised:  The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area , where the latter pay nodal prices , whereas non-flexible consumers will be charged a fixed price , which is derived by the underlying nodal prices.
Edit intent: clarity
Original:  The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area , where the latter pay nodal prices whereas the former are charged a fixed price , which is derived by the underlying nodal prices.
Revised:  The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area , where the latter pay nodal prices whereas the former are charged a fixed price derived from the underlying nodal prices.
Edit intent: coherence
Original:  In addition, it determines how the distribution system operator should manage the local grid by optimally determining the lines to be expanded, and the collected network tariff levied on network users, while accounting for both congestion rent and investment costs.
Revised:  Moreover, the developed approach shows how a distribution system operator should manage the local grid by optimally determining the lines to be expanded, and the collected network tariff levied on network users, while accounting for both congestion rent and investment costs.
Edit intent: clarity
Original:  In addition, it determines how the distribution system operator should manage the local grid by optimally determining the lines to be expanded, and the collected network tariff levied on network users, while accounting for both congestion rent and investment costs.
Revised:  In addition, it determines how the distribution system operator should manage the local grid by optimally determining the lines to be expanded, and the collected network tariff levied on grid users, while accounting for both congestion rent and investment costs.
Edit intent: clarity
Original:  While the majority of questions in these datasets require perception for recognizing existence, properties, and spatial relationships of entities, a significant portion of questions pose challenges that correspond to reasoning tasks -- tasks that can only be answered through a synthesis of perception and knowledge about the world, logic and / or reasoning.
Revised:  While the majority of questions in these datasets require perception for recognizing existence, properties, and spatial relationships of entities, a significant portion of questions pose challenges that correspond to reasoning tasks - tasks that can only be answered through a synthesis of perception and knowledge about the world, logic and / or reasoning.
Edit intent: fluency
Original:  For example,  models answer the complex reasoning question "Is the banana ripe enough to eat?" correctly, but fail on the associated perception question "Are the bananas mostly green or yellow?" indicating that the model likely answered the reasoning question correctly but for the wrong reason.
Revised:  For example, in Figure 1, models answer the complex reasoning question "Is the banana ripe enough to eat?" correctly, but fail on the associated perception question "Are the bananas mostly green or yellow?" indicating that the model likely answered the reasoning question correctly but for the wrong reason.
Edit intent: meaning-changed
Original:  We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting Sub-VQA , a new dataset consisting of 200K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split.
Revised:  We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting VQA-introspect , a new dataset consisting of 200K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split.
Edit intent: meaning-changed
Original:  We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting Sub-VQA , a new dataset consisting of 200K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split.
Revised:  We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting Sub-VQA , a new dataset1 which consists of 238K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split.
Edit intent: meaning-changed
Original:  Additionally, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend do the same parts of the image when answering the reasoning question and the perception sub questions .
Revised:  Our evaluation shows that state-of-the-art VQA models have comparable performance in answering perception and reasoning questions, but suffer from consistency problems. To address this shortcoming, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend do the same parts of the image when answering the reasoning question and the perception sub questions .
Edit intent: meaning-changed
Original:  Additionally, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend do the same parts of the image when answering the reasoning question and the perception sub questions .
Revised:  Additionally, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend to the same parts of the image when answering the reasoning question and the perception sub questions .
Edit intent: fluency
Original:  Additionally, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend do the same parts of the image when answering the reasoning question and the perception sub questions .
Revised:  Additionally, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend do the same parts of the image when answering the reasoning question and the perception sub question .
Edit intent: fluency
Original:  We show that SQuINT improves model consistency by 7.8 \%, also marginally improving its performance on the Reasoning questions in VQA, while also displaying qualitatively better attention maps.
Revised:  We show that SQuINT improves model consistency by ~5 \%, also marginally improving its performance on the Reasoning questions in VQA, while also displaying qualitatively better attention maps.
Edit intent: meaning-changed
Original:  We show that SQuINT improves model consistency by 7.8 \%, also marginally improving its performance on the Reasoning questions in VQA, while also displaying qualitatively better attention maps.
Revised:  We show that SQuINT improves model consistency by 7.8 \%, also marginally improving performance on the Reasoning questions in VQA, while also displaying qualitatively better attention maps.
Edit intent: clarity
Original:  While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases which help the language model understand the given task.
Revised:  While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task.
Edit intent: clarity
Original:  Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data  for 12 subjects .
Revised:  Here we describe an automated approach for analyzing %DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data for 12 subjects .
Edit intent: clarity
Original:  Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data  for 12 subjects .
Revised:  Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and naturalistic behavior video data for 12 subjects .
Edit intent: clarity
Original:  Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data  for 12 subjects .
Revised:  Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data . We take a behavior-first approach to analyzing the long-term recordings. Using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching on the behavioral video data, we find and annotate spontaneous human upper-limb movement events. We show results from our approach applied to data collected for 12 subjects .
Edit intent: meaning-changed
Original:  Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data  for 12 subjects .
Revised:  Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data for 12 human subjects over 7--9 days for each subject .
Edit intent: meaning-changed
Original:  Our pipeline discovers and annotates thousands of instances of human upper-limb movement events in long-term (7--9 day) naturalistic behavior data using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching .
Revised:  Our pipeline discovers and annotates over 40,000 instances of naturalistic human upper-limb movement events in long-term (7--9 day) naturalistic behavior data using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching .
Edit intent: meaning-changed
Original:  In this paper, we propose a graph embedding method, called ExEm, which using dominating-set theory and deep learning approaches . In the proposed method, the dominating set theory is applied to the collaborative network and dominating nodes of this network are found.
Revised:  In this paper, we propose a graph embedding method, called ExEm, which using dominating-set theory and deep learning approaches to capture node representations. ExEm finds dominating nodes of the collaborative network and dominating nodes of this network are found.
Edit intent: clarity
Original:  In the proposed method, the dominating set theory is applied to the collaborative network and dominating nodes of this network are found. After that, a set of random walks is created which starts from dominating nodes(experts). The main condition for constricting these random walks is the existence of another dominating node . After making the walks that satisfy the stated conditions, they are stored as a sequence in a corpus. In the next step, the corpus is fed to the SKIP-GRAM neural network model.
Revised:  In the proposed method, the dominating set theory is applied to the collaborative network and constructs intelligent random walks that comprise of at least two dominating nodes. One dominating node should appear at the beginning of each path sampled to characterize the local neighborhoods. Moreover, the second dominating node reflects the global structure information. To learn the node embeddings, ExEm exploits three embedding methods including
Edit intent: meaning-changed
Original:  Expert embeddings can be used for various purposes including accurately modeling experts' expertise or computing experts' scores in expert recommendation systems. Hence, we also introduce a novel strategy  to calculate experts' scores by using the extracted expert embedding vectors.
Revised:  Expert embeddings can be applied to many applications. In order to extend these embeddings into the expert recommendation system, we introduce a novel strategy to calculate experts' scores by using the extracted expert embedding vectors.
Edit intent: clarity
Original:  Hence, we also introduce a novel strategy  to calculate experts' scores by using the extracted expert embedding vectors. The effectiveness of ExEm is validated through assessing its performance on multi-label classification, link prediction, and recommendation tasks .
Revised:  Hence, we also introduce a novel strategy to calculate experts' scores and recommend experts. At the end, we conduct extensive experiments to validate the effectiveness of ExEm is validated through assessing its performance on multi-label classification, link prediction, and recommendation tasks .
Edit intent: coherence
Original:  In this work, we extend this approach to the task of dialogue state tracking for goal-oriented dialogues, in which the data naturally exhibits a hierarchical structure over utterances and related annotations . Deep generative data augmentation for dialogue state tracking requires the generative model to be aware of the hierarchically structured data .
Revised:  In this work, we extend this approach to the task of dialogue state tracking for goal-oriented dialogues, in which the data naturally exhibits a hierarchical structure over utterances and related annotations , deep generative data augmentation for dialogue state tracking requires the generative model to be aware of the hierarchically structured data .
Edit intent: fluency
Original:  We propose  Variational Hierarchical Dialog Autoencoder (VHDA) for modeling various aspects of goal-oriented dialogues , including linguistic and underlying annotation structures. Our experiments show that our  model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Revised:  We propose Variational Hierarchical Dialog Autoencoder (VHDA) for modeling various aspects of goal-oriented dialogues , including linguistic features and underlying structured annotations, namely dialog acts and goals. We also propose two training policies to mitigate issues that arise with training VAE-based models. Experiments show that our model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Edit intent: meaning-changed
Original:  Our experiments show that our  model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Revised:  Our experiments show that our hierarchical model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Edit intent: meaning-changed
Original:  Our experiments show that our  model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Revised:  Our experiments show that our model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialog state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Edit intent: fluency
Original:  Our experiments show that our  model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Revised:  Our experiments show that our model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving the dialog state tracking performances on several datasets .
Edit intent: clarity
Original:  Our experiments show that our  model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on several datasets .
Revised:  Our experiments show that our model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialogue state trackers, ultimately improving their final dialogue state tracking performances on various dialog domains. Surprisingly, the ability to jointly generate dialog features enables our model to outperform previous state-of-the-arts in related subtasks, such as language generation and user simulation .
Edit intent: meaning-changed
Original:  On real data, experiments illustrate that our proposed optimisation method produces higher risk-adjusted returns than mean variance portfolio optimisation. The predictive distribution is superior in every measure  , producing a higher mean, lower standard deviation and less kurtosis.
Revised:  On real data, experiments illustrate that our proposed optimisation method produces higher risk-adjusted returns than mean-variance portfolio optimization. Moreover, the predictive distribution is superior in every measure , producing a higher mean, lower standard deviation and less kurtosis.
Edit intent: coherence
Original: On the basis of the evident ability of neuronal olfactory systems to evaluate the intensity of an odorous stimulus and also recognize the identity of the odorant over a large range of concentrations, a few biologically-realistic hypotheses on some of the underlying neural processes are made.
Revised: On the basis of the evident ability of neuronal olfactory systems to evaluate the intensity of an odorous stimulus and at the same time also recognise the identity of the odorant over a large range of concentrations, a few biologically-realistic hypotheses on some of the underlying neural processes are made.
Edit intent: clarity