wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour.
Revised:  The data-driven nature of machine learning technique enabled a reliable estimate of the relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour.
Edit intent: clarity
Original:  The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour.
Revised:  The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this research to find that a nonlinear effect exists in the relationship between construction workers' personality traits and safety behaviour.
Edit intent: clarity
Original:  The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour. The developed model is found to be sufficient to have satisfactory accuracy in explaining and predicting workers' safety behaviour. This finding provides the empirical evidence to support the usefulness of personality traits as effective predictors of people's safety behaviour at work. In addition, this study could have practical implications.
Revised:  The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship . This research has practical implications.
Edit intent: coherence
Original:  The machine learning model developed can help identify vulnerable workers who are more prone to undertake unsafe behaviours, which is proven to have good prediction accuracy and is thereby potentially useful for decision making and safety management on construction sites .
Revised:  The machine learning model developed can help identify vulnerable workers who are more prone to undertake unsafe behaviours, which is proven to have good prediction accuracy and is thereby potentially useful for assisting project decision-makers to assess how prone workers are to carry out unsafe behaviours in the workplace .
Edit intent: clarity
Original: In augmented reality (AR)  , correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources for the analysing, rendering and 3D registration on the areas of interest.
Revised: Augmented reality (AR) , correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources for the analysing, rendering and 3D registration on the areas of interest.
Edit intent: fluency
Original: In augmented reality (AR)  , correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources for the analysing, rendering and 3D registration on the areas of interest.
Revised: In augmented reality (AR) overlays digital content onto the reality. In AR system , correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources for the analysing, rendering and 3D registration on the areas of interest.
Edit intent: meaning-changed
Original: In augmented reality (AR)  , correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources for the analysing, rendering and 3D registration on the areas of interest.
Revised: In augmented reality (AR) , correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources on the areas of interest.
Edit intent: coherence
Original:  To bridge the gap between the  real-world scene and the scene augmented by virtual information, we construct the ARVR saliency dataset with 100 diverse videos evaluated by 20 people.
Revised:  To bridge the gap between the saliency prediction on real-world scene and the scene augmented by virtual information, we construct the ARVR saliency dataset with 100 diverse videos evaluated by 20 people.
Edit intent: meaning-changed
Original:  The saliency annotations of head and eye movements for both original and augmented videos are collected which constitute the ARVR dataset  .
Revised:  The saliency annotations of head and eye movements for both original and augmented videos are collected which constitute the ARVR dataset . We also design a model which is capable of solving the saliency prediction problem in AR. Local block images are extracted to simulate the viewport and offset the projection distortion. Conspicuous visual cues in local viewports are extracted to constitute the spatial features. The optical flow information is estimated as the important temporal feature. We also consider the interplay between virtual information and reality. The composition of the augmentation information is distinguished, and the joint effects of adversarial augmentation and complementary augmentation are estimated. We generate a graph by taking each block image as one node. Both the visual saliency mechanism and the characteristics of viewing behaviors are considered in the computation of edge weights on the graph which are interpreted as Markov chains. The fraction of the visual attention that is diverted to each block image is estimated through equilibrium distribution on of this chain .
Edit intent: meaning-changed
Original: The goal of our paper is to semantically edit parts of an image to match a given text that describes desired attributes (e.g., texture, colour, and background), while preserving other contents that are irrelevant to the text.
Revised: The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e.g., texture, colour, and background), while preserving other contents that are irrelevant to the text.
Edit intent: fluency
Original: The Entity Linking (EL) approaches have been a long-standing research field and find applicability in various use cases such as semantic search, text annotation, question answering, etc. Although effective and robust, current approaches are still limited to particular knowledge repositories (e.g. Wikipedia) or specific knowledge graphs (e.g. Freebase, DBpedia, and YAGO). The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information.
Revised: The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information.
Edit intent: coherence
Original:   In this paper, we advance the state-of-the-art approaches by developing a context-aware attentive neural network approach for entity linking on Wikidata.
Revised:  Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we advance the state-of-the-art approaches by developing a context-aware attentive neural network approach for entity linking on Wikidata.
Edit intent: meaning-changed
Original:  This work, first of its kind, opens a new direction for the research community to pay attention to developing context-aware EL approaches for collaborative knowledge graphs.abstract 
Revised:  This work, first of its kind, opens a new direction for the research community to pay attention to developing context-aware EL approaches for collaborative knowledge graphs.abstract approx 8\% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.
Edit intent: meaning-changed
Original: In quantitative finance, useful features are constructed by human experts. However, this method is of low efficient. Thus, automatic feature construction algorithms have received more and more attention. The SOTA technic in this field is to use reverse polish expression to represent the features, and then use genetic programming to reconstruct it .
Revised: In automatic financial feature construction task, the state of the art technic leverages reverse polish expression to represent the features, and then use genetic programming to reconstruct it .
Edit intent: clarity
Original:  We derive a new Fuk-Nagaev inequality for a class of \tau-dependent processes with heavier than Gaussian tails, nesting \alpha-mixing processes as a special case, and establish estimation, prediction, and inferential properties, including convergence rates of the HAC estimator for the long-run variance based on LASSO residuals.
Revised:  We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals. The treatment relies on a new Fuk-Nagaev inequality for a class of \tau-dependent processes with heavier than Gaussian tails, nesting \alpha-mixing processes as a special case, and establish estimation, prediction, and inferential properties, including convergence rates of the HAC estimator for the long-run variance based on LASSO residuals.
Edit intent: meaning-changed
Original:  We derive a new Fuk-Nagaev inequality for a class of \tau-dependent processes with heavier than Gaussian tails, nesting \alpha-mixing processes as a special case, and establish estimation, prediction, and inferential properties, including convergence rates of the HAC estimator for the long-run variance based on LASSO residuals. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that the text data can be a useful addition to more traditional numerical data .
Revised:  We derive a new Fuk-Nagaev inequality for a class of \tau-dependent processes with heavier than Gaussian tails, which is of independent interest .
Edit intent: coherence
Original: Time series regression analysis relies on the heteroskedasticity- and autocorrelation-consistent (HAC) estimation of the asymptotic variance to conduct proper inference.
Revised: We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance to conduct proper inference.
Edit intent: meaning-changed
Original: Time series regression analysis relies on the heteroskedasticity- and autocorrelation-consistent (HAC) estimation of the asymptotic variance to conduct proper inference. This paper develops such inferential methods for high-dimensional time series regressions .
Revised: Time series regression analysis relies on the heteroskedasticity- and autocorrelation-consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional time series regressions .
Edit intent: clarity
Original:   The treatment relies on a new Fuk-Nagaev inequality for a class of \tau -dependent processes with heavier than Gaussian tails, which is of independent interest  .
Revised:  This leads to valid time series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk-Nagaev inequality for a class of \tau -dependent processes with heavier than Gaussian tails, which is of independent interest .
Edit intent: meaning-changed
Original:   The treatment relies on a new Fuk-Nagaev inequality for a class of \tau -dependent processes with heavier than Gaussian tails, which is of independent interest  .
Revised:  The treatment relies on a new Fuk-Nagaev inequality for a class of \tau -dependent processes with heavier than Gaussian tails, which is of independent interest . In an empirical application, we study the Granger causal relationship between the VIX and financial news .
Edit intent: meaning-changed
Original:  The main aim of the challenge is to evaluate new technologies for text-dependent speaker verification (TD-SV) . There is one more task in the SdSVC which is text-independent speaker verification which is explained in a separate description file.
Revised:  The main goal of the challenge is to evaluate new technologies for text-dependent speaker verification (TD-SV) . There is one more task in the SdSVC which is text-independent speaker verification which is explained in a separate description file.
Edit intent: clarity
Original:  The main aim of the challenge is to evaluate new technologies for text-dependent speaker verification (TD-SV) . There is one more task in the SdSVC which is text-independent speaker verification which is explained in a separate description file. The evaluation dataset in the challenge is recently released multi-purpose DeepMine dataset. The dataset has three parts and among them part1 is for text-dependent speaker verification .
Revised:  The main aim of the challenge is to evaluate new technologies for text-dependent speaker verification (TD-SV) . There is one more task in the SdSVC which is text-independent (TI) speaker verification (SV) in a short duration scenario. The proposed challenge evaluates SdSV with varying degree of phonetic overlap between the enrollment and test utterances (cross-lingual). It is the first challenge with a broad focus on systematic benchmark and analysis on varying degrees of phonetic variability on short-duration speaker recognition. We expect that modern methods (deep neural networks in particular) will play a key role .
Edit intent: meaning-changed
Original:  To understand how such superior performance is achieved, we can probe a trained deep neural network by studying neuron activations, that is , combinations of neuron firings, at any layer of the network in response to a particular input.
Revised:  To understand how such performance is achieved, we can probe a trained deep neural network by studying neuron activations, that is , combinations of neuron firings, at any layer of the network in response to a particular input.
Edit intent: clarity
Original:  To understand how such superior performance is achieved, we can probe a trained deep neural network by studying neuron activations, that is , combinations of neuron firings, at any layer of the network in response to a particular input.
Revised:  To understand how such superior performance is achieved, we can probe a trained deep neural network by studying neuron activations, i. e. , combinations of neuron firings, at any layer of the network in response to a particular input.
Edit intent: clarity
Original:  We ask the following questions: What is the shape of the space of activations? That is , what is URLanizational principle behind neuron activations , and how are the activations related within a layer and across layers?
Revised:  We ask the following questions: What is the shape of the activation space? What is URLanizational principle behind neuron activations , and how are the activations related within a layer and across layers?
Edit intent: coherence
Original:  Applying tools from topological data analysis, we present TopoAct, a visual exploration system used to study topological summaries of activation vectors for a single layer as well as the evolution of such summaries across multiple layers.
Revised:  Applying tools from topological data analysis, we present TopoAct, a visual exploration system to study topological summaries of activation vectors for a single layer as well as the evolution of such summaries across multiple layers.
Edit intent: coherence
Original:  To understand how such performance is achieved, we can probe a trained deep neural network by studying neuron activations, i.e., combinations of neuron firings, at any layer of the network in response to a particular input.
Revised:  To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e., combinations of neuron firings, at any layer of the network in response to a particular input.
Edit intent: coherence
Original:  To understand how such performance is achieved, we can probe a trained deep neural network by studying neuron activations, i.e., combinations of neuron firings, at any layer of the network in response to a particular input.
Revised:  To understand how such performance is achieved, we can probe a trained deep neural network by studying neuron activations, i.e., combinations of neuron firings, at various layers of the network in response to a particular input.
Edit intent: clarity
Original: Current approaches to machine translation (MT) either translate sentences in isolation, disregarding the context they appear in, or model context on the level of the full document, without a notion of any internal structure the document may have.
Revised: Current approaches to machine translation (MT) either translate sentences in isolation, disregarding the context they appear in, or model context at the level of the full document, without a notion of any internal structure the document may have.
Edit intent: fluency
Original:  We draw inspiration from Louis and Webber (2014) who use this information to improve  MT and transfer their proposal into the framework of neural MT. We compare two different methods of including information about the topic of the section within which each sentence is found: one using side constraints and the other using a cache-based model.
Revised:  We draw inspiration from Louis and Webber (2014) who use this information to improve statistical MT and transfer their proposal into the framework of neural MT. We compare two different methods of including information about the topic of the section within which each sentence is found: one using side constraints and the other using a cache-based model.
Edit intent: meaning-changed
Original:  We create and release the data on which we run our experiments -- parallel corpora for three language pairs (Chinese-English, French-English, Bulgarian-English) from Wikipedia biographies  , preserving the boundaries of sections within the articles.
Revised:  We create and release the data on which we run our experiments - parallel corpora for three language pairs (Chinese-English, French-English, Bulgarian-English) from Wikipedia biographies , preserving the boundaries of sections within the articles.
Edit intent: fluency
Original:  We create and release the data on which we run our experiments -- parallel corpora for three language pairs (Chinese-English, French-English, Bulgarian-English) from Wikipedia biographies  , preserving the boundaries of sections within the articles.
Revised:  We create and release the data on which we run our experiments -- parallel corpora for three language pairs (Chinese-English, French-English, Bulgarian-English) from Wikipedia biographies , which we extract automatically , preserving the boundaries of sections within the articles.
Edit intent: meaning-changed
Original:  We test the ability of this type of representation to support geometrically consistent spatial tasks , such as interpolating between learned locations , and compare its performance to that of a hand-crafted representation which has, by design, a high degree of geometric consistency .
Revised:  We test the ability of this type of representation to support geometrically consistent spatial tasks such as interpolating between learned locations , and compare its performance to that of a hand-crafted representation which has, by design, a high degree of geometric consistency .
Edit intent: fluency
Original:  We test the ability of this type of representation to support geometrically consistent spatial tasks , such as interpolating between learned locations , and compare its performance to that of a hand-crafted representation which has, by design, a high degree of geometric consistency .
Revised:  We test the ability of this type of representation to support geometrically consistent spatial tasks , such as interpolating between learned locations using decoding of feature vectors. We introduce a hand-crafted representation which has, by design, a high degree of geometric consistency .
Edit intent: meaning-changed
Original:  We test the ability of this type of representation to support geometrically consistent spatial tasks , such as interpolating between learned locations , and compare its performance to that of a hand-crafted representation which has, by design, a high degree of geometric consistency .
Revised:  We test the ability of this type of representation to support geometrically consistent spatial tasks , such as interpolating between learned locations , and compare its performance to that of a hand-crafted representation that has, by design, a high degree of geometric consistency .
Edit intent: fluency
Original:  We test the ability of this type of representation to support geometrically consistent spatial tasks , such as interpolating between learned locations , and compare its performance to that of a hand-crafted representation which has, by design, a high degree of geometric consistency . Our comparison of these two models demonstrates thatit is advantageous to include information about the persistence of features as the camera translates (e.g. distant features persist) .
Revised:  We test the ability of this type of representation to support geometrically consistent spatial tasks , such as interpolating between learned locations , and compare its performance to that of a hand-crafted representation which has, by design, a high degree of geometric consistency and demonstrate that, in this case, information about the persistence of features as the camera translates (e.g. distant features persist) .
Edit intent: coherence
Original:  Our comparison of these two models demonstrates thatit is advantageous to include information about the persistence of features as the camera translates (e.g. distant features persist) . It is likely that non-Cartesian representations of this sort will be increasingly important in the search for robust models of human spatial perception and navigation .
Revised:  Our comparison of these two models demonstrates thatit is advantageous to include information about the persistence of features as the camera translates (e.g. distant features persist) can improve performance on the geometric tasks. These examples avoid Cartesian (in this case, 2D) representations of space. Non-Cartesian, learned representations provide an important stimulus in neuroscience to the search for robust models of human spatial perception and navigation .
Edit intent: meaning-changed