wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Arguably, the most decisive component in the pipeline is the classifier , but it is not the only thing that we need to consider. Despite that the classifier may be interpretable, resulting models are too complex to be considered comprehensible, making it impossible for humans to comprehend the actual decisions.
Revised:  Although the classifier may be interpretable, resulting models are too complex to be considered comprehensible, making it impossible for humans to comprehend the actual decisions.
Edit intent: coherence
Original:  The purpose of this paper is to present a feature selection mechanism (the first step in the pipeline) that is able to improve comprehensibility by using less but more meaningful features while achieving a good performance in microblogging contexts where interpretability is mandatory.
Revised:  This paper presents a feature selection mechanism (the first step in the pipeline) that is able to improve comprehensibility by using less but more meaningful features while achieving a good performance in microblogging contexts where interpretability is mandatory.
Edit intent: coherence
Original:  The purpose of this paper is to present a feature selection mechanism (the first step in the pipeline) that is able to improve comprehensibility by using less but more meaningful features while achieving a good performance in microblogging contexts where interpretability is mandatory.
Revised:  The purpose of this paper is to present a feature selection mechanism that is able to improve comprehensibility by using less but more meaningful features while achieving a good performance in microblogging contexts where interpretability is mandatory.
Edit intent: coherence
Original:  The purpose of this paper is to present a feature selection mechanism (the first step in the pipeline) that is able to improve comprehensibility by using less but more meaningful features while achieving a good performance in microblogging contexts where interpretability is mandatory.
Revised:  The purpose of this paper is to present a feature selection mechanism (the first step in the pipeline) that is able to improve comprehensibility by using less but more meaningful features while achieving good performance in microblogging contexts where interpretability is mandatory.
Edit intent: fluency
Original:  We adopt a human-guided machine learning framework (using more than 40,000 rigorously selected tweets from more than 20,000 distinct Twitter users) to capture public opinion on the potential vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Revised:  We adopt a human-guided machine learning framework (using more than 40,000 rigorously selected tweets from more than 20,000 distinct Twitter users) to capture public opinions on the potential vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Edit intent: fluency
Original:  We compare demographics, social capital, income, religious status, and political affiliations of these three groups to investigate the scope and causes of public opinions. A lower vaccine acceptance level for potential COVID-19 vaccines is observed among socioeconomically disadvantaged groups. We further aggregate opinions at the state and country levels, and find that the  percentage of the pro-vaccine group is lower in the Southeast part of the United States.
Revised:  We aggregate opinions at the state and country levels, and find that the percentage of the pro-vaccine group is lower in the Southeast part of the United States.
Edit intent: coherence
Original:  We further aggregate opinions at the state and country levels, and find that the  percentage of the pro-vaccine group is lower in the Southeast part of the United States.
Revised:  We further aggregate opinions at the state and country levels, and find that the major changes in the percentages of different opinion groups roughly correspond to the major pandemic-related events. Interestingly, the percentage of the pro-vaccine group is lower in the Southeast part of the United States.
Edit intent: meaning-changed
Original:  Interestingly, the major changes in the percentages of different opinion groups roughly correspond to the major pandemic-related events . Next,
Revised:  Using multinomial logistic regression, we compare demographics, social capital, income, religious status, political affiliations, geo-locations, sentiment of personal pandemic experience and non-pandemic experience, and county-level pandemic severity perception of these three groups to investigate the scope and causes of public opinions on vaccines. We find that socioeconomically disadvantaged groups are more likely to hold polarized opinions on potential COVID-19 vaccines. The anti-vaccine opinion is the strongest among the people who have the worst personal pandemic experience . Next,
Edit intent: meaning-changed
Original:  by conducting counterfactual analyses, we find that the U.S. public is most concerned about the safety, effectiveness, and political issues regarding potential vaccines for COVID-19 . In particular, we also investigate public opinion about the newly emerged mRNA vaccines and find opinions vary significantly across by religious beliefs with proportionally more religious people in the anti-vaccine group .
Revised:  by conducting counterfactual analyses, we find that the U.S. public is most concerned about the safety, effectiveness, and political issues regarding potential vaccines for COVID-19 , and improving personal pandemic experience increases the vaccine acceptance level .
Edit intent: meaning-changed
Original:  In a recent breakthrough, Le and Solomon (2019) established the precise dependencies on \varepsilon>0 and d\in N of the minimum lightness of an (1+\varepsilon) -spanner , and observed that additional Steiner points can substantially improve the lightness.
Revised:  In a recent breakthrough, Le and Solomon (2019) established the precise dependencies on \varepsilon>0 and d\in N of the minimum lightness of (1+\varepsilon) -spanner , and observed that additional Steiner points can substantially improve the lightness.
Edit intent: fluency
Original:  In a recent breakthrough, Le and Solomon (2019) established the precise dependencies on \varepsilon>0 and d\in N of the minimum lightness of an (1+\varepsilon) -spanner , and observed that additional Steiner points can substantially improve the lightness.
Revised:  In a recent breakthrough, Le and Solomon (2019) established the precise dependencies on \varepsilon>0 and d\in N of the minimum lightness of an (1+\varepsilon) -spanners , and observed that additional Steiner points can substantially improve the lightness.
Edit intent: fluency
Original:  Le and Solomon (2020) constructed Steiner (1+\varepsilon)-spanners of lightness O(\varepsilon^{-1}\log\Delta) in the plane, where \Delta \in \Omega(\log n ) is the spread of the point set, defined as the ratio between the maximum and minimum distance between a pair of points.
Revised:  Le and Solomon (2020) constructed Steiner (1+\varepsilon)-spanners of lightness O(\varepsilon^{-1}\log\Delta) in the plane, where \Delta \geq \Omega(\sqrt{n ) is the spread of the point set, defined as the ratio between the maximum and minimum distance between a pair of points.
Edit intent: meaning-changed
Original: Recent results on a thermodinamically conceived numerical scheme applied on wave propagation in viscoelastic/rheological solids are generalized here, both in the sense that the scheme is extended to four spacetime dimensions and in the aspect of the virtues of a thermodynamical approach.
Revised: The recent results attained from a thermodynamically conceived numerical scheme applied on wave propagation in viscoelastic/rheological solids are generalized here, both in the sense that the scheme is extended to four spacetime dimensions and in the aspect of the virtues of a thermodynamical approach.
Edit intent: clarity
Original:  In other words, are frequently observed subsequences assigned a correspondingly large probability by the VAE? Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Revised:  We develop a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Edit intent: clarity
Original:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Revised:  Using a set of sequence statistics to assess the "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Edit intent: clarity
Original:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Revised:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Edit intent: fluency
Original:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Revised:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs of recent interest: the pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Edit intent: meaning-changed
Original:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Revised:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, the VAE, and a site-independent model, using natural and synthetic datasets.
Edit intent: clarity
Original:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and a site-independent model, using natural and synthetic datasets.
Revised:  Using a set of sequence statistics we comparatively assess the accuracy, or "generative capacity" , of three GPSMs : a pairwise Potts Hamiltonian, a vanilla VAE, and the site-independent model, using natural and synthetic datasets.
Edit intent: fluency
Original:  We show that the vanilla VAE's generative capacity lies between the pairwise Potts and site-independent models.
Revised:  We show that the generative capacity of the Potts Hamiltonian model is the largest, in that the higher order mutational statistics generated by the model agree with those observed for natural sequences. In contrast, we show that the VAE's generative capacity lies between the pairwise Potts and site-independent models.
Edit intent: meaning-changed
Original:  Importantly, our work measures GPSM generative capacity in terms of higher-order sequence covariation  and provides a new framework for evaluating and interpreting GPSM accuracy that emphasizes the role of epistasis.
Revised:  Importantly, our work measures GPSM generative capacity in terms of higher-order sequence covariation statistics which we have developed, and provides a new framework for evaluating and interpreting GPSM accuracy that emphasizes the role of epistasis.
Edit intent: meaning-changed
Original: Event Detection, a fundamental task of Information Extraction, tends to struggle when it needs to recognize novel event types with a few samples , i. e. Few-Shot Event Detection (FSED). Previous identify-then-classify paradigm attempts to solve this problem in the pipeline manner but ignores the trigger discrepancy between event types, thus suffering from the error propagation.
Revised: Event Detection, a fundamental task of Information Extraction, tends to struggle when it needs to recognize novel event types with a few samples . The previous work attempts to solve this problem in the pipeline manner but ignores the trigger discrepancy between event types, thus suffering from the error propagation.
Edit intent: clarity
Original:  To this end, we first design the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which builds prototypical amortization networks to approximate the transition scores between labels based on the label prototypes.
Revised:  To this end, we first propose the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which builds prototypical amortization networks to approximate the transition scores between labels based on the label prototypes.
Edit intent: clarity
Original:  To this end, we first design the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which builds prototypical amortization networks to approximate the transition scores between labels based on the label prototypes.
Revised:  To this end, we first design the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which approximates the transition scores between labels based on the label prototypes.
Edit intent: clarity
Original:  We conduct experiments on the benchmark dataset FewEvent and the experimental results show that the tagging based methods are better than existing pipeline and joint learning methods. In addition, the proposed PA-CRF  achieves the best results on the public dataset .
Revised:  We conduct experiments on the benchmark dataset FewEvent and the experimental results show that the tagging based methods are better than existing identify-then-classify models and our PA-CRF achieves the best results on the public dataset .
Edit intent: clarity
Original:  We present a method to train models with procedurally generated Q-A pairs from captions using techniques, such as templates and annotation frameworks like QASRL. As most VQA models rely on dense and costly object annotations extracted from object detectors, we propose spatial-pyramid image patches as a simple but effective alternative to object bounding boxes, and demonstrate that our method uses fewer human annotations .
Revised:  We present a method to train models with synthetic Q-A pairs from captions using techniques, such as templates and annotation frameworks like QASRL. As most VQA models rely on dense and costly object annotations extracted from object detectors, we propose spatial-pyramid image patches as a simple but effective alternative to object bounding boxes, and demonstrate that our method uses fewer human annotations .
Edit intent: clarity
Original:  We present a method to train models with procedurally generated Q-A pairs from captions using techniques, such as templates and annotation frameworks like QASRL. As most VQA models rely on dense and costly object annotations extracted from object detectors, we propose spatial-pyramid image patches as a simple but effective alternative to object bounding boxes, and demonstrate that our method uses fewer human annotations .
Revised:  We present a method to train models with procedurally generated Q-A pairs generated procedurally from captions. Additionally, we demonstrate the efficacy of spatial-pyramid image patches as a simple but effective alternative to object bounding boxes, and demonstrate that our method uses fewer human annotations .
Edit intent: clarity
Original:  We present a method to train models with procedurally generated Q-A pairs from captions using techniques, such as templates and annotation frameworks like QASRL. As most VQA models rely on dense and costly object annotations extracted from object detectors, we propose spatial-pyramid image patches as a simple but effective alternative to object bounding boxes, and demonstrate that our method uses fewer human annotations . We benchmark on VQA-v2, GQA, and on VQA-CP which contains a softer version of label shift.
Revised:  We present a method to train models with procedurally generated Q-A pairs from captions using techniques, such as templates and annotation frameworks like QASRL. As most VQA models rely on dense and costly object annotations extracted from object detectors, we propose spatial-pyramid image patches as a simple but effective alternative to dense and costly object bounding box annotations used in existing VQA models. Our experiments on three VQA benchmarks demonstrate the efficacy of this weakly-supervised approach, especially on the VQA-CP which contains a softer version of label shift.
Edit intent: clarity
Original:  We benchmark on VQA-v2, GQA, and on VQA-CP which contains a softer version of label shift. Our methods surpass prior supervised methods on VQA-CP and are competitive with methods without object features in fully supervised setting .
Revised:  We benchmark on VQA-v2, GQA, and on VQA-CP challenge, which tests performance under changing linguistic priors .
Edit intent: meaning-changed
Original:  The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Revised:  A 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Edit intent: fluency
Original:  The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Revised:  The 5-fold cross-validation experiment using the mean square error (MSE) as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Edit intent: meaning-changed
Original:  The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Revised:  The 5-fold cross-validation experiment using MSE as the loss function showed that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Edit intent: fluency
Original:  The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Revised:  The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree was suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Edit intent: fluency
Original:  The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model is ideal for predicting the attenuation coefficient.
Revised:  The 5-fold cross-validation experiment using MSE as the loss function shows that the decision tree is suitable for predicting the peak coefficient, and the linear regression model was ideal for predicting the attenuation coefficient.
Edit intent: fluency
Original: We present a system that enables robotsto interpret spatial language as a distribution over object locations for effective search in partially observable cityscale environments.
Revised: Humans use spatial language to naturally describe object locations and their relations. Interpreting spatial language not only adds a perceptual modality for robots, but also reduces the barrier of interfacing with humans. Previous work primarily considers spatial language as a distribution over object locations for effective search in partially observable cityscale environments.
Edit intent: meaning-changed
Original: We present a system that enables robotsto interpret spatial language as a distribution over object locations for effective search in partially observable cityscale environments. We introduce the spatial language observation space and formulate a stochastic observation model under the framework of Partially Observable Markov Decision Process (POMDP) which incorporates information extracted from the spatial languageinto the robot's belief.
Revised: We present a system that enables robotsto interpret spatial language as goal specification for instruction following tasks in fully observable domains, often paired with reference paths for reward-based learning. However, spatial language is inherently subjective and potentially ambiguous or misleading. Hence, in this paper, we consider spatial language as a form of stochastic observation. We propose SLOOP (Spatial Language Object-Oriented POMDP), a new framework for partially observable decision making with a probabilistic observation model for spatial language. We apply SLOOP to object search in city-scale environments.
Edit intent: meaning-changed
Original:  To interpret ambiguous, context-dependent prepositions (e.g. ~ front), we propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given  environment context.
Revised:  To interpret ambiguous, context-dependent prepositions (e.g. front), we propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given environment context.
Edit intent: fluency
Original:  To interpret ambiguous, context-dependent prepositions (e.g. ~ front), we propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given  environment context.
Revised:  To interpret ambiguous, context-dependent prepositions (e.g. ~ front), we design a simple convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given environment context.
Edit intent: meaning-changed
Original:  To interpret ambiguous, context-dependent prepositions (e.g. ~ front), we propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given  environment context.
Revised:  To interpret ambiguous, context-dependent prepositions (e.g. ~ front), we propose a convolutional neural network that predicts the language provider's relative frame of reference (FoR) given environment context.
Edit intent: clarity
Original:  To interpret ambiguous, context-dependent prepositions (e.g. ~ front), we propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given  environment context.
Revised:  To interpret ambiguous, context-dependent prepositions (e.g. ~ front), we propose a convolutional neural network model that learns to predict the language provider's latent frame of reference (FoR) given environment context.
Edit intent: meaning-changed