wanyu/IteraTeR_full_sent huggingface dataset:
Original: In recent years, Natural Language Processing (NLP) models have achieved phenomenal success in linguistical and semantical tasks like machine translation, cognitive dialogue systems, information retrieval via Natural Language Understanding (NLU), and Natural Language Generation (NLG).
Revised: In recent years, Natural Language Processing (NLP) models have achieved phenomenal success in linguistic and semantic tasks like text classification, machine translation, cognitive dialogue systems, information retrieval via Natural Language Understanding (NLU), and Natural Language Generation (NLG).
Edit intent: clarity
Original:  We show that the three widely-used systemic risk measures  conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Revised:  The systemic risk measures conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Edit intent: clarity
Original:  We show that the three widely-used systemic risk measures  conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Revised:  We show that the three widely-used systemic risk measures CoVaR ( conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Edit intent: meaning-changed
Original:  We show that the three widely-used systemic risk measures  conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Revised:  We show that the three widely-used systemic risk measures conditional value-at-risk ), CoES ( conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Edit intent: meaning-changed
Original:  We show that the three widely-used systemic risk measures  conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Revised:  We show that the three widely-used systemic risk measures conditional value-at-risk (CoVaR), conditional expected shortfall ) and MES ( marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Edit intent: meaning-changed
Original:  We show that the three widely-used systemic risk measures  conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Revised:  We show that the three widely-used systemic risk measures conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall ), measuring the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own.
Edit intent: clarity
Original:  We show that the three widely-used systemic risk measures  conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable on their own. As a remedy, we establish the joint identifiability of CoVaR, MES and (CoVaR, CoES) together with the value-at-risk (VaR) of the reference position X . While this resembles the situation of the classical risk measures expected shortfall (ES) and VaR concerning identifiability, a joint elicitability result fails.
Revised:  We show that the three widely-used systemic risk measures conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position Y given that a reference position X is in distress, fail to be identifiable and elicitable . We establish the joint identifiability of CoVaR, MES and (CoVaR, CoES) together with the value-at-risk (VaR) of the reference position X . While this resembles the situation of the classical risk measures expected shortfall (ES) and VaR concerning identifiability, a joint elicitability result fails.
Edit intent: clarity
Original:  As a remedy, we establish the joint identifiability of CoVaR, MES and (CoVaR, CoES) together with the value-at-risk (VaR) of the reference position X . While this resembles the situation of the classical risk measures expected shortfall (ES) and VaR concerning identifiability, a joint elicitability result fails. Therefore, we introduce a completely novel notion of multivariate scoring functions equipped with some order, which are therefore called multi-objective scores.
Revised:  As a remedy, we establish the joint identifiability of CoVaR, MES and (CoVaR, CoES) together with the value-at-risk (VaR) of the reference position X , but show that an analogue result for elicitability fails. The novel notion of multivariate scoring functions equipped with some order, which are therefore called multi-objective scores.
Edit intent: coherence
Original:  Therefore, we introduce a completely novel notion of multivariate scoring functions equipped with some order, which are therefore called multi-objective scores. We introduce and investigate corresponding notions of multi-objective elicitability, which may prove beneficial in various applications beyond finance. In particular, we prove that conditional elicitability of two functionals implies joint multi-objective elicitability with respect to the lexicographic order on \mathbb{R}^2 , which makes it applicable in the context of CoVaR, MES or (CoVaR, CoES), together with VaR.
Revised:  Therefore, we introduce a completely novel notion of multivariate scoring functions equipped with some order, which are therefore called multi-objective elicitability however, relying on multivariate scores equipped with an order, leads to a positive result when using the lexicographic order on \mathbb{R}^2 , which makes it applicable in the context of CoVaR, MES or (CoVaR, CoES), together with VaR.
Edit intent: coherence
Original:  In particular, we prove that conditional elicitability of two functionals implies joint multi-objective elicitability with respect to the lexicographic order on \mathbb{R}^2 , which makes it applicable in the context of CoVaR, MES or (CoVaR, CoES), together with VaR. We describe corresponding comparative backtests of Diebold-Mariano type , for two-sided and 'one and a half'-sided hypotheses, which respect the particularities of the lexicographic order and which can be used in a regulatory setting .
Revised:  In particular, we prove that conditional elicitability of two functionals implies joint multi-objective elicitability with respect to the lexicographic order on \mathbb{R}^2 . We establish comparative backtests of Diebold-Mariano type , for two-sided and 'one and a half'-sided hypotheses, which respect the particularities of the lexicographic order and which can be used in a regulatory setting .
Edit intent: coherence
Original:  We describe corresponding comparative backtests of Diebold-Mariano type , for two-sided and 'one and a half'-sided hypotheses, which respect the particularities of the lexicographic order and which can be used in a regulatory setting .
Revised:  We describe corresponding comparative backtests of Diebold--Mariano type for superior systemic risk forecasts and comparable VaR forecasts, accompanied by a traffic-light approach .
Edit intent: meaning-changed
Original:  Our model consists of an attribute-aware modulation network and an attribute-augmented generative network  . Given unseen classes, the modulation network adaptively modulates the generator by applying task-specific transformations so that the generative network can adapt to highly diverse tasks.
Revised:  Our model consists of an attribute-aware modulation network , an attribute-augmented generative network . Given unseen classes, the modulation network adaptively modulates the generator by applying task-specific transformations so that the generative network can adapt to highly diverse tasks.
Edit intent: coherence
Original:  Our model consists of an attribute-aware modulation network and an attribute-augmented generative network  . Given unseen classes, the modulation network adaptively modulates the generator by applying task-specific transformations so that the generative network can adapt to highly diverse tasks.
Revised:  Our model consists of an attribute-aware modulation network and an attribute-augmented generative network , and an attribute-weighted classifier . Given unseen classes, the modulation network adaptively modulates the generator by applying task-specific transformations so that the generative network can adapt to highly diverse tasks.
Edit intent: meaning-changed
Original:   Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8\% and 5.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method  .
Revised:  The weighted classifier utilizes the data quality to enhance the training procedure, further improving the model performance. Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8\% and 5.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method .
Edit intent: meaning-changed
Original:   Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8\% and 5.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method  .
Revised:  Our empirical evaluations on four widely-used benchmarks show that AMAZ outperforms state-of-the-art methods by 3.8\% and 5.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method .
Edit intent: clarity
Original:   Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8\% and 5.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method  .
Revised:  Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8\% and 3.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method .
Edit intent: meaning-changed
Original:   Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8\% and 5.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method  .
Revised:  Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8\% and 5.1 \% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method . Our experiments on a zero-shot image retrieval task show AMAZ's ability to synthesize instances that portray real visual characteristics .
Edit intent: meaning-changed
Original:  The results showed that listening to the breakup songs, compared to the rebellious songs, enhanced the coupling between the medial prefrontal cortex and several emotion-related regions, including the thalamus, caudate, amygdala, hippocampus  , and right inferior frontal gyrus.
Revised:  The results showed that listening to the breakup songs, compared to the rebellious songs, enhanced the coupling between the medial prefrontal cortex and several emotion-related regions, including the thalamus, caudate, amygdala, hippocampus , middle orbitofrontal cortex , and right inferior frontal gyrus.
Edit intent: meaning-changed
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A P seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentive seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentiveP R elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: clarity
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentiveP seudo elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentiveP seudoR F eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevance eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF Net work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedback work (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: fluency
Original:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet work (APRF-Net) to enhance the representation of rare queries for query categorization.
Revised:  To this end, we propose a novel deep neural model named A ttentiveP seudoR elevanceF eedbackNet Attentive Pseudo Relevance Feedback Network (APRF-Net) to enhance the representation of rare queries for query categorization.
Edit intent: meaning-changed
Original:  The findings of this paper can be leveraged for further improvements in search query representation and understanding.abstract 
Revised:  The findings of this paper can be leveraged for further improvements in search query representation and understanding.abstract \% on F1@1 score over the baselines, which increases to 8.2\% improvement for the rare (tail) queries. The findings of this paper can be leveraged for further improvements in search query representation and understanding.
Edit intent: meaning-changed
Original:  In this work, we propose a software framework Knodle that provides a modularization for separating weak data annotations, powerful deep learning models, and methods for improving weakly supervised training . This modularization gives the training process access to fine-grained information such as data set characteristics, matches of heuristic rules, or elements of the deep learning model ultimately used for prediction.
Revised:  In this work, we propose a software framework Knodle that provides a modularization for separating weak data annotations, powerful deep learning models, and methods for improving weakly supervised training as separate, modular components. The standardized interfaces between these independent parts account for data- and model-agnostic weak supervision method development, but still allow the training process access to fine-grained information such as data set characteristics, matches of heuristic rules, or elements of the deep learning model ultimately used for prediction.
Edit intent: meaning-changed
Original:  Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at the correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those methods that harness the interplay of neural networks and weakly labeled data  .
Revised:  Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those methods that harness the interplay of neural networks and weakly labeled data .
Edit intent: fluency
Original:  Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at the correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those methods that harness the interplay of neural networks and weakly labeled data  .
Revised:  Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at the correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those that harness the interplay of neural networks and weakly labeled data .
Edit intent: fluency
Original:  Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at the correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those methods that harness the interplay of neural networks and weakly labeled data  .
Revised:  Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at the correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those methods that harness the interplay of neural networks and weakly labeled data . We illustrate the benchmarking potential of the framework with a performance comparison of several reference implementations on a selection of datasets that are already available in Knodle .
Edit intent: meaning-changed
Original: Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e.g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding.
Revised: Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e.g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding.
Edit intent: fluency
Original:  To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% minority categories and%DIFDELCMD < {\em %%% tail queries, we conduct two sets of experiments.
Revised:  To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% categories and%DIFDELCMD < {\em %%% tail queries, we conduct two sets of experiments.
Edit intent: fluency
Original:  To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% minority categories and%DIFDELCMD < {\em %%% tail queries, we conduct two sets of experiments.
Revised:  To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% minority %DIFDELCMD < {\em %%% tail queries, we conduct two sets of experiments.
Edit intent: clarity
Original:  To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% minority categories and%DIFDELCMD < {\em %%% tail queries, we conduct two sets of experiments.
Revised:  To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% minority categories and%DIFDELCMD < {\em %%% minority categories and tail queries, we conduct two sets of experiments.
Edit intent: clarity
Original:  Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.abstract 
Revised:  Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.abstract \% improvement on minority classes and a 7.1\% improvement on tail queries over a state-of-the-art label embedding model. Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.
Edit intent: meaning-changed
Original:  Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of formatgiven company-specific nature , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information .
Revised:  Due to the massive volume of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of formatgiven company-specific nature , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information .
Edit intent: clarity