wanyu/IteraTeR_full_sent huggingface dataset:
Original:  SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguish the target object from the interference objects whose real sizes are obviously different from the target object.
Revised:  SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguishing a target object from the interference objects whose real sizes are obviously different from the target object.
Edit intent: fluency
Original:  SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguish the target object from the interference objects whose real sizes are obviously different from the target object.
Revised:  SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguish the target object from interference objects whose real sizes are obviously different from the target object.
Edit intent: fluency
Original:  The real size of the object can help to exclude the interference object  categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features.
Revised:  The real size of an object can help to exclude the interference object categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features.
Edit intent: fluency
Original:  The real size of the object can help to exclude the interference object  categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features.
Revised:  The real size of the object can help exclude the interference object categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features.
Edit intent: fluency
Original:  The real size of the object can help to exclude the interference object  categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features.
Revised:  The real size of the object can help to exclude the interference object 's categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features.
Edit intent: fluency
Original:  The real size of the object can help to exclude the interference object  categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features.
Revised:  The real size of the object can help to exclude the interference object categories whose real size ranges do not match the real size of the object, which greatly reduces the object's categories' number in the label set used for the downstream object recognition based on object features.
Edit intent: clarity
Original:  Here, we consider the problem of learning a predictor whose risk across such shifts is invariant. A key challenge to learning such risk-invariant predictors is shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle .
Revised:  Here, we study a flexible, causally-motivated approach to enforcing such invariances, paying special attention to shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle .
Edit intent: clarity
Original:  A key challenge to learning such risk-invariant predictors is shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle .
Revised:  A key challenge to learning such risk-invariant predictors is shortcut learning, where a robust predictor can achieve optimal i.i.d generalization in principle .
Edit intent: clarity
Original:  A key challenge to learning such risk-invariant predictors is shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle . We propose a flexible, causally-motivated approach to address this challenge. Specifically, we propose a regularization scheme that makes use of auxiliary labelsfor potential shortcut features, which are often available at training time . Drawing on the causal structure of the problem, we enforce a conditional independence between the representation used to predict the main label and the auxiliary labels.
Revised:  A key challenge to learning such risk-invariant predictors is shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle , but instead it relies on spurious correlations or shortcuts in practice. Our approach uses auxiliary labels, typically available at training time . Drawing on the causal structure of the problem, we enforce a conditional independence between the representation used to predict the main label and the auxiliary labels.
Edit intent: coherence
Original:  Specifically, we propose a regularization scheme that makes use of auxiliary labelsfor potential shortcut features, which are often available at training time . Drawing on the causal structure of the problem, we enforce a conditional independence between the representation used to predict the main label and the auxiliary labels.
Revised:  Specifically, we propose a regularization scheme that makes use of auxiliary labelsfor potential shortcut features, which are often available at training time , to enforce conditional independences between the latent factors that determine these labels.
Edit intent: clarity
Original:  We show both theoretically and empirically that this causally-motivated regularization scheme yields robust predictors that generalize well both in-distribution and under distribution shifts, and does so with better sample efficiency than standard regularization or weighting approaches .
Revised:  We show both theoretically and empirically that causally-motivated regularization scheme yields robust predictors that generalize well both in-distribution and under distribution shifts, and does so with better sample efficiency than standard regularization or weighting approaches .
Edit intent: clarity
Original:  We show both theoretically and empirically that this causally-motivated regularization scheme yields robust predictors that generalize well both in-distribution and under distribution shifts, and does so with better sample efficiency than standard regularization or weighting approaches .
Revised:  We show both theoretically and empirically that this causally-motivated regularization schemes (a) lead to more robust estimators that generalize well both in-distribution and under distribution shifts, and does so with better sample efficiency than standard regularization or weighting approaches .
Edit intent: meaning-changed
Original:  We show both theoretically and empirically that this causally-motivated regularization scheme yields robust predictors that generalize well both in-distribution and under distribution shifts, and does so with better sample efficiency than standard regularization or weighting approaches .
Revised:  We show both theoretically and empirically that this causally-motivated regularization scheme yields robust predictors that generalize well under distribution shift, and (b) have better finite sample efficiency compared to usual regularization schemes, even in the absence of distribution shifts. Our analysis highlights important theoretical properties of training techniques commonly used in causal inference, fairness, and disentanglement literature .
Edit intent: meaning-changed
Original: We present CoTexT, a  transformer-based architecture encoder-decoder pre-trained model that learns the representative context between natural language (NL) and programming language (PL) through multi-task learning.
Revised: We present CoTexT, a pre-trained, transformer-based architecture encoder-decoder pre-trained model that learns the representative context between natural language (NL) and programming language (PL) through multi-task learning.
Edit intent: meaning-changed
Original:  CoTexT is pre-trained , in self-supervised fashion, based on large programming language corpus to learn general-purpose understanding and code-text generation supporting downstream NL-PL task such as code summarizing/documentation, code generation, defect detection, code debugging, etc .
Revised:  CoTexT is pre-trained on large programming language corpus to learn general-purpose understanding and code-text generation supporting downstream NL-PL task such as code summarizing/documentation, code generation, defect detection, code debugging, etc .
Edit intent: clarity
Original:  CoTexT is pre-trained , in self-supervised fashion, based on large programming language corpus to learn general-purpose understanding and code-text generation supporting downstream NL-PL task such as code summarizing/documentation, code generation, defect detection, code debugging, etc .
Revised:  CoTexT is pre-trained , in self-supervised fashion, based on large programming language corpora to learn a general understanding of language and code. CoTexT supports downstream NL-PL task such as code summarizing/documentation, code generation, defect detection, code debugging, etc .
Edit intent: clarity
Original:  CoTexT is pre-trained , in self-supervised fashion, based on large programming language corpus to learn general-purpose understanding and code-text generation supporting downstream NL-PL task such as code summarizing/documentation, code generation, defect detection, code debugging, etc .
Revised:  CoTexT is pre-trained , in self-supervised fashion, based on large programming language corpus to learn general-purpose understanding and code-text generation supporting downstream NL-PL tasks such as code summarizing/documentation, code generation, defect detection, code debugging, etc .
Edit intent: fluency
Original:  CoTexT is pre-trained , in self-supervised fashion, based on large programming language corpus to learn general-purpose understanding and code-text generation supporting downstream NL-PL task such as code summarizing/documentation, code generation, defect detection, code debugging, etc .
Revised:  CoTexT is pre-trained , in self-supervised fashion, based on large programming language corpus to learn general-purpose understanding and code-text generation supporting downstream NL-PL task such as code summarizing/documentation, code generation, defect detection, and code debugging .
Edit intent: coherence
Original:  We train CoTexT on different combination of available PL corpus including both "bimodal" and "unimodal" data where the former is the combinations of both natural texts and their corresponding code snippets in an input sequence and the latter is merely code snippets.
Revised:  We train CoTexT on different combinations of available PL corpus including both "bimodal" and "unimodal" data where the former is the combinations of both natural texts and their corresponding code snippets in an input sequence and the latter is merely code snippets.
Edit intent: fluency
Original:  We train CoTexT on different combination of available PL corpus including both "bimodal" and "unimodal" data where the former is the combinations of both natural texts and their corresponding code snippets in an input sequence and the latter is merely code snippets.
Revised:  We train CoTexT on different combination of available PL corpus including both "bimodal" and "unimodal" data . Here, bimodal data is the combination of text and corresponding code snippets in an input sequence and the latter is merely code snippets.
Edit intent: coherence
Original:  We train CoTexT on different combination of available PL corpus including both "bimodal" and "unimodal" data where the former is the combinations of both natural texts and their corresponding code snippets in an input sequence and the latter is merely code snippets.
Revised:  We train CoTexT on different combination of available PL corpus including both "bimodal" and "unimodal" data where the former is the combinations of both natural texts and their corresponding code snippets , whereas unimodal data is merely code snippets.
Edit intent: clarity
Original:  We evaluate multi-task learning CoTexT on different generation and classification tasks on CodeXGLUE and it achieves state-of-the-art on all downstream tasks .
Revised:  We evaluate multi-task learning : we perform Code Summarization on 6 different programming languages and Code Refinement on both small and medium size featured in the CodeXGLUE dataset. We further conduct extensive experiments to investigate CoTexT on other tasks within the CodeXGlue dataset, including Code Generation and Defect Detection. We consistently achieve SOTA results in these tasks, demonstrating the versatility of our models .
Edit intent: meaning-changed
Original:  Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images . This issue is commonly referred to as covariate shift or dataset bias. Domain adaptation attempts to address this problemby leveraging domain shift characteristics from labeled data in a related domain when learning a classifier for label-scarce target dataset. There are a plethora of works to adapt object classification and semantic segmentation models to label-scarce target dataset through unsupervised domain adaptation.
Revised:  Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images , termed as domain adaptation problem. There is a plethora of works to adapt object classification and semantic segmentation models to label-scarce target dataset through unsupervised domain adaptation.
Edit intent: clarity
Original:  Considering that object detection is a fundamental task in computer vision, many recent works have recently focused on addressing the domain adaptation issue for object detection as well. In this paper, we provide a brief introduction to the domain adaptation problem for object detection and present an overview of various methods proposed to date for addressing this problem .
Revised:  Considering that object detection is a fundamental task in computer vision, many recent works have focused on developing novel domain adaptive detection techniques. Here, we describe in detail the domain adaptation problem for object detection and present an overview of various methods proposed to date for addressing this problem .
Edit intent: meaning-changed
Original:  We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, getting them familiar with the current status of the progress , and providing them with promising direction for future research.
Revised:  We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, and familiarizing them with the current status of the progress , and providing them with promising direction for future research.
Edit intent: coherence
Original:  We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, getting them familiar with the current status of the progress , and providing them with promising direction for future research.
Revised:  We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, getting them familiar with the current status of the progress while providing promising directions for future research.
Edit intent: clarity
Original:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from  (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Revised:  Existing methods for spatial interpolation, such as variants of kriging and spatial autoregressive models, tend to suffer from (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Edit intent: meaning-changed
Original:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from  (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Revised:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from at least one of the following limitations: (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Edit intent: meaning-changed
Original:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from  (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Revised:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from (a) the assumption of stationarity , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Edit intent: meaning-changed
Original:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from  (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Revised:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from (a) a trade-off between modelling local or global spatial interaction , (b) the assumption of isotropy , and (c) the assumption of homogeneity of intermediate locations between points .
Edit intent: meaning-changed
Original:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from  (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the assumption of homogeneity of intermediate locations between points .
Revised:  Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from (a) a trade-off between modelling local or global spatial interaction , (b) the assumption there is only one possible path between two points , and (c) the trade-off between modelling local or global spatial interaction .
Edit intent: coherence
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Revised:  Addressing these issues in this work , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Edit intent: meaning-changed
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Revised:  Addressing these issues , we propose the use of Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Edit intent: clarity
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Revised:  Addressing these issues , we propose a value propagation method, inspired by Markov reward processes (MRPs) as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Edit intent: fluency
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Revised:  Addressing these issues , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and we introduce three variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Edit intent: meaning-changed
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Revised:  Addressing these issues , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a basic static discount MRP (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Edit intent: meaning-changed