wanyu/IteraTeR_full_sent huggingface dataset:
Original: Text data augmentation, i.e.  the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g.  Machine Translation, Question Answering, Text Classification, etc.). Motivated by a business application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic text augmentation framework combining different methods, utilizing BERT language model, multi-step back-translation and heuristics.
Revised: Text data augmentation, i.e. the creation of synthetic textual data from an original text, is challenging as augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g. Machine Translation, Text Classification). Initially motivated by an application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic text augmentation framework combining different methods, utilizing BERT language model, multi-step back-translation and heuristics.
Edit intent: clarity
Original:  Motivated by a business application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic text augmentation framework combining different methods, utilizing BERT language model, multi-step back-translation and heuristics.
Revised:  Motivated by a business application of Business Email Compromise (BEC) detection, we propose a corpus and task agnostic augmentation framework used as a service to augment English texts within our company. Our proposal combines different methods, utilizing BERT language model, multi-step back-translation and heuristics.
Edit intent: meaning-changed
Original:  An important property in the analysis is that the local energy projector commutes with the divergence operator  .
Revised:  An important property in the analysis is that the local energy projector commutes with the divergence operator . With the help of a divergence-free interpolation operator onto a generalized Raviart-Thomas element space, a pressure-robust nonconforming virtual element method is developed by simply modifying the right hand side of the previous discretization .
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Original:  For OPE, we estimate exploitability that is often used as a metric for determining how close a strategy profile is to a Nash equilibrium in two-player zero-sum games.
Revised:  For OPE, we estimate exploitability that is often used as a metric for determining how close a policy profile (i.e., a tuple of policies) is to a Nash equilibrium in two-player zero-sum games.
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Original:  We prove the exploitability estimation error bounds for OPE and regret bounds for OPL based on the doubly robust and double reinforcement learning estimators .
Revised:  We prove the exploitability estimation error bounds for the proposed estimators. We then propose the methods to find the best candidate policy profile by selecting the policy profile that minimizes the estimated exploitability from a given policy profile class. We prove the regret bounds of the policy profiles selected by our methods .
Edit intent: meaning-changed
Original: In trichromats, color vision entails the projection of an infinite-dimensional space (the one containing all possible electromagnetic power spectra) onto the 3-dimensional space determined by the three types of cones.
Revised: In trichromats, color vision entails the projection of an infinite-dimensional space (the one containing all possible electromagnetic power spectra) onto the 3-dimensional space that modulates the activity of the three types of cones.
Edit intent: clarity
Original:  The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of different wavelengths (the primaries) by adjusting their intensities.
Revised:  The classes of equivalence of metamerism are revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of different wavelengths (the primaries) by adjusting their intensities.
Edit intent: fluency
Original:  The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of different wavelengths (the primaries) by adjusting their intensities.
Revised:  The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers adjust the intensity of three monochromatic light beams of different wavelengths (the primaries) by adjusting their intensities.
Edit intent: clarity
Original:  The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of different wavelengths (the primaries) by adjusting their intensities.
Revised:  The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of three pre-set wavelengths (the primaries) by adjusting their intensities.
Edit intent: clarity
Original:  The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of different wavelengths (the primaries) by adjusting their intensities. The linear relation between the color matching functions and the absorption probabilities of each type of cone is here used to find the collection of primariesthat need to be chosen in order to obtain quasi orthogonal, or alternatively, almost-always positive, color-matching functions.
Revised:  The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of different wavelengths (the primaries) to produce a mixture that is perceptually equal to a given monochromatic target stimulus. Here we use the linear relation between the color matching functions and the absorption probabilities of each type of cone is here used to find the collection of primariesthat need to be chosen in order to obtain quasi orthogonal, or alternatively, almost-always positive, color-matching functions.
Edit intent: clarity
Original:  The linear relation between the color matching functions and the absorption probabilities of each type of cone is here used to find the collection of primariesthat need to be chosen in order to obtain quasi orthogonal, or alternatively, almost-always positive, color-matching functions. Moreover, previous studies have shown that there is a certain trial-to-trial and subject-to-subject variability in the color matching functions. So far, no theoretical description has been offered to explain the trial-to-trial variability , whereas the sources of the subject-to-subject variability have been associated with individual differences in the properties of the peripheral visual system.
Revised:  The linear relation between the color matching functions and the absorption probabilities of each type of cone to find particularly useful triplets of primaries. As a second goal, we also derive an analytical description of the trial-to-trial variability , whereas the sources of the subject-to-subject variability have been associated with individual differences in the properties of the peripheral visual system.
Edit intent: clarity
Original:  So far, no theoretical description has been offered to explain the trial-to-trial variability , whereas the sources of the subject-to-subject variability have been associated with individual differences in the properties of the peripheral visual system.
Revised:  So far, no theoretical description has been offered to explain the trial-to-trial variability and the correlations of color matching functions stemming from Poissonian noise in photon capture. We analyze how the statistical properties of the peripheral visual system.
Edit intent: meaning-changed
Original:  So far, no theoretical description has been offered to explain the trial-to-trial variability , whereas the sources of the subject-to-subject variability have been associated with individual differences in the properties of the peripheral visual system. Here we explore the role of the Poissonian nature of photon capture on the wavelength-dependence of the trial-to-trial variability in the color matching functions, as well as their correlations .
Revised:  So far, no theoretical description has been offered to explain the trial-to-trial variability , whereas the sources of the subject-to-subject variability have been associated with individual differences in the properties of the responses to color-matching experiments vary with the retinal composition and the wavelengths of peak absorption probability, and compare them with experimental data on subject-to-subject variability obtained previously .
Edit intent: meaning-changed
Original:  By adding a few specially-crafted files to the autocompleter's training corpus  , or else by directly fine-tuning the autocompleter on these files  , the attacker can influence its suggestions for attacker-chosen contexts.
Revised:  By adding a few specially-crafted files to the autocompleter's training corpus (data poisoning) , or else by directly fine-tuning the autocompleter on these files , the attacker can influence its suggestions for attacker-chosen contexts.
Edit intent: meaning-changed
Original:  By adding a few specially-crafted files to the autocompleter's training corpus  , or else by directly fine-tuning the autocompleter on these files  , the attacker can influence its suggestions for attacker-chosen contexts.
Revised:  By adding a few specially-crafted files to the autocompleter's training corpus , or else by directly fine-tuning the autocompleter on these files (model poisoning) , the attacker can influence its suggestions for attacker-chosen contexts.
Edit intent: meaning-changed
Original:  We moreover show that these attacks can be targeted: an autocompleter poisoned by a targeted attack is much more likely to suggest the insecure completion for certain files (e.g., those from a specific repo ) .
Revised:  Moreover, we show that these attacks can be targeted: an autocompleter poisoned by a targeted attack is much more likely to suggest the insecure completion for certain files (e.g., those from a specific repo ) .
Edit intent: fluency
Original:  We moreover show that these attacks can be targeted: an autocompleter poisoned by a targeted attack is much more likely to suggest the insecure completion for certain files (e.g., those from a specific repo ) .
Revised:  We moreover show that these attacks can be targeted: an autocompleter poisoned by a targeted attack is much more likely to suggest the insecure completion for certain files (e.g., those from a specific repo or specific developer .
Edit intent: meaning-changed
Original:  Compared to the average treatment effect (ATE) that solely relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and ensures positive findings guarantee uniformly valid treatment effects over underrepresented minority groups .
Revised:  Compared to the average treatment effect (ATE) , whose validity relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and ensures positive findings guarantee uniformly valid treatment effects over underrepresented minority groups .
Edit intent: clarity
Original:  Compared to the average treatment effect (ATE) that solely relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and ensures positive findings guarantee uniformly valid treatment effects over underrepresented minority groups .
Revised:  Compared to the average treatment effect (ATE) that solely relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and positive findings guarantee uniformly valid treatment effects over underrepresented minority groups .
Edit intent: coherence
Original:  Compared to the average treatment effect (ATE) that solely relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and ensures positive findings guarantee uniformly valid treatment effects over underrepresented minority groups .
Revised:  Compared to the average treatment effect (ATE) that solely relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and ensures positive findings guarantee uniformly valid treatment effects over subpopulations .
Edit intent: meaning-changed
Original:  With a data-driven group structure, the BGRE estimators exhibit comparable accuracy of clustering with the nonsupervised machine learning algorithm Kmeans and outperform Kmeans in a two-step procedure .
Revised:  With a data-driven group structure, the BGRE estimators exhibit comparable accuracy of clustering with the nonsupervised machine learning algorithm Kmeans and outperform Kmeans in a two-step Bayesian grouped estimator whose group structure relies on Kmeans .
Edit intent: meaning-changed
Original: Based on the analysis of the empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of the COVID-19 pandemic that depends not on time, but on the number of infections itself.
Revised: The ongoing COVID-19 pandemic is challenging every part of society. From a scientific point of view the first major task is to predict the dynamics of the empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of the COVID-19 pandemic that depends not on time, but on the number of infections itself.
Edit intent: meaning-changed
Original: Based on the analysis of the empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of the COVID-19 pandemic that depends not on time, but on the number of infections itself.
Revised: Based on the analysis of the pandemic, allowing governments to allocate proper resources and measures to fight it, as well as gauging the success of these measures by comparison with the predictions in hindsight. The vast majority of pandemic models are based on extensive models with large numbers of fit parameters, leading to individual descriptions for every hot spot on the world. This makes predictions and comparisons cumbersome, if not impossible. We here propose a different approach, by moving away from a description over time, and instead choosing the total number of infected people in an enclosed area as the COVID-19 pandemic that depends not on time, but on the number of infections itself.
Edit intent: meaning-changed
Original: Based on the analysis of the empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of the COVID-19 pandemic that depends not on time, but on the number of infections itself. This change of the independent variable overcomes the crucial issue of analyzing very different countries worldwide within one mathematical framework with similar parameters.
Revised: Based on the analysis of the empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of the independent variable overcomes the crucial issue of analyzing very different countries worldwide within one mathematical framework with similar parameters.
Edit intent: coherence
Original:  This change of the independent variable overcomes the crucial issue of analyzing very different countries worldwide within one mathematical framework with similar parameters. This was previously impossible leading to individual description for every country. Our model allows describing the pandemicincluding its endpoint surprisingly good and giving a figure of merit for the success of the measuresto fight the pandemic .
Revised:  This change of the independent variable . Analyzing a few hot spots data, we derive an empirical formula for the dynamics, dependent only on three variables. The final number of infections is strictly connected to one fit parameter we call mitigation factor, which in turn is mostly dependent only on the enclosed population. Despite its simpleness, this description applies to every of the around 50 countries we have analyzed, allows to separate different waves of the pandemic, provides a figure of merit for the success of the measuresto fight the pandemic .
Edit intent: meaning-changed
Original:  Our model allows describing the pandemicincluding its endpoint surprisingly good and giving a figure of merit for the success of the measuresto fight the pandemic .
Revised:  Our model allows describing the pandemicincluding its endpoint surprisingly good and giving a figure of merit for the overall usefulness of government measures, and shows when a pandemic is ending. Our model is robust against undetected cases, and allows all nations, in particular those with fewer resources, to reasonably predict the outcome of the pandemic in their country .
Edit intent: meaning-changed
Original:  We then leverage the pre-trained GROVER to downstream molecular property prediction tasks followed by task-specific fine-tuning, where we observe a huge improvement (more than 6\% on average) over current state-of-the-art methods on 11 challenging benchmarks.
Revised:  We then leverage the pre-trained GROVER for molecular property prediction tasks followed by task-specific fine-tuning, where we observe a huge improvement (more than 6\% on average) over current state-of-the-art methods on 11 challenging benchmarks.
Edit intent: clarity
Original:  We then leverage the pre-trained GROVER to downstream molecular property prediction tasks followed by task-specific fine-tuning, where we observe a huge improvement (more than 6\% on average) over current state-of-the-art methods on 11 challenging benchmarks.
Revised:  We then leverage the pre-trained GROVER to downstream molecular property prediction followed by task-specific fine-tuning, where we observe a huge improvement (more than 6\% on average) over current state-of-the-art methods on 11 challenging benchmarks.
Edit intent: clarity
Original:  We then leverage the pre-trained GROVER to downstream molecular property prediction tasks followed by task-specific fine-tuning, where we observe a huge improvement (more than 6\% on average) over current state-of-the-art methods on 11 challenging benchmarks.
Revised:  We then leverage the pre-trained GROVER to downstream molecular property prediction tasks followed by task-specific fine-tuning, where we observe a huge improvement (more than 6\% on average) from current state-of-the-art methods on 11 challenging benchmarks.
Edit intent: clarity
Original: Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging yet promising to automatically detect mental health problems from such data which is short, sparse and sometimes poorly phrased. However, there are efforts to automatically learn patterns using computational models on such user-generated content. While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results , we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs)
Revised: Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning methods provide no interpretability for depression prediction, hence their predictions are obscure to human. In this work , we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs)
Edit intent: meaning-changed
Original:  While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results , we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Revised:  While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results , we propose interpretive Multi-Modal Depression Detection with Hierarchical Attention Network MDHAN, for detection depressed users on social media such as Twitter-based on multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Edit intent: meaning-changed
Original:  and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Revised:  and Convolutional Neural Networks to detect depressed users on social media and explain the model prediction. We have considered user posts along with Twitter-based on multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Edit intent: meaning-changed
Original:  and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Revised:  and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Edit intent: clarity