wanyu/IteraTeR_v2 huggingface dataset:
Original: Regenerating natural language explanations for science questions is a challenging task for evaluating complex multi-hop and abductive inferencecapabilities .
Revised: Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and abductive inferencecapabilities .
Edit intent: clarity
Original: Regenerating natural language explanations for science questions is a challenging task for evaluating complex multi-hop and abductive inferencecapabilities .
Revised: Regenerating natural language explanations for science questions is a challenging task for evaluating complex multi-hop and explainable inference .
Edit intent: clarity
Original:  In this setting, Transformers trained on human-annotated explanations achieve state-of-the-art performance when adopted as cross-encoder architectures  .
Revised:  In this context, large language models can achieve state-of-the-art performance when adopted as cross-encoder architectures .
Edit intent: clarity
Original:  In this setting, Transformers trained on human-annotated explanations achieve state-of-the-art performance when adopted as cross-encoder architectures  .
Revised:  In this setting, Transformers trained on human-annotated explanations achieve state-of-the-art performance when employed as cross-encoder architectures .
Edit intent: clarity
Original:  However, while much attention has been devoted to the quality of the constructed explanations, the problem of performing abductive inference at scale is still under-studied.
Revised:  However, while much attention has been devoted to the quality of the explanations, the problem of performing abductive inference at scale is still under-studied.
Edit intent: clarity
Original:  However, while much attention has been devoted to the quality of the constructed explanations, the problem of performing abductive inference at scale is still under-studied.
Revised:  However, while much attention has been devoted to the quality of the constructed explanations, the problem of performing inference efficiently is largely under-studied.
Edit intent: clarity
Original:  As intrinsically not scalable, the cross-encoder architectural paradigm is not suitable for efficient multi-hop inference on massive facts banks.
Revised:  As intrinsically not scalable, possessing limited applicability to real-world scenarios that require inference on massive facts banks.
Edit intent: clarity
Original:  To maximise both accuracy and inference time, we propose a hybrid abductive solver that autoregressively combines a dense bi-encoder  with a sparse model of explanatory power, computed leveraging explicit patterns in the explanations.
Revised:  To enable complex multi-hop reasoning at scale, this paper focuses on bi-encoder with a sparse model of explanatory power, computed leveraging explicit patterns in the explanations.
Edit intent: clarity
Original:  To maximise both accuracy and inference time, we propose a hybrid abductive solver that autoregressively combines a dense bi-encoder  with a sparse model of explanatory power, computed leveraging explicit patterns in the explanations.
Revised:  To maximise both accuracy and inference time, we propose a hybrid abductive solver that autoregressively combines a dense bi-encoder with a sparse model of explanatory power, designed to leverage explicit inference patterns in the explanations.
Edit intent: clarity
Original:  Our experiments demonstrate that the proposed framework can achieve performance comparable with the state-of-the-art cross-encoder while being \approx 50 times faster and scalable to corpora of millions of facts.
Revised:  Our experiments demonstrate that the proposed framework can achieve performance comparable with that of state-of-the-art cross-encoder while being \approx 50 times faster and scalable to corpora of millions of facts.
Edit intent: clarity
Original:  Our experiments demonstrate that the proposed framework can achieve performance comparable with the state-of-the-art cross-encoder while being \approx 50 times faster and scalable to corpora of millions of facts.
Revised:  Our experiments demonstrate that the proposed framework can achieve performance comparable with the state-of-the-art cross-encoders while being approx 50 times faster and scalable to corpora of millions of facts.
Edit intent: fluency
Original:  Moreover, we study the impact of the hybridisation on semantic drift and science question answering without additional training, showing that it boosts the quality of the explanations and contributes to improved downstream inference performance .
Revised:  Further analyses on semantic drift and science question answering without additional training, showing that it boosts the quality of the explanations and contributes to improved downstream inference performance .
Edit intent: clarity
Original:  Moreover, we study the impact of the hybridisation on semantic drift and science question answering without additional training, showing that it boosts the quality of the explanations and contributes to improved downstream inference performance .
Revised:  Moreover, we study the impact of the hybridisation on semantic drift and science question answering without additional training, showing that it boosts the quality of the most challenging explanations, contributing to improved performance on downstream inference tasks .
Edit intent: clarity
Original: Multiplier, which has a key role in many different applications, is a time-consuming, energy-intensive computation block.
Revised: Multiplier, as a key role in many different applications, is a time-consuming, energy-intensive computation block.
Edit intent: clarity
Original:  Then, another ultra-fast, high-efficient approximate multiplier is achieved by means a systematic truncation strategy.
Revised:  Then, another ultra-fast, high-efficient approximate multiplier is achieved utilizing a systematic truncation strategy.
Edit intent: clarity
Original: Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled samples per class are given.
Revised: Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled examples per class are given.
Edit intent: clarity
Original:  We introduce Transductive Maximum Margin Classifier (TMMC) for few-shot learning  .
Revised:  We introduce a transductive maximum margin classifier for few-shot learning .
Edit intent: clarity
Original:  The basic idea of the classical maximum margin classifier is to solve an optimal prediction function that the corresponding separating hyperplane can correctly divide the training data and the resulting classifier has the largest geometric margin.
Revised:  The basic idea of the classical maximum margin classifier is to solve an optimal prediction function so that the training data can be correctly classified by the resulting classifer with the corresponding separating hyperplane can correctly divide the training data and the resulting classifier has the largest geometric margin.
Edit intent: clarity
Original:  The basic idea of the classical maximum margin classifier is to solve an optimal prediction function that the corresponding separating hyperplane can correctly divide the training data and the resulting classifier has the largest geometric margin.
Revised:  The basic idea of the classical maximum margin classifier is to solve an optimal prediction function that the largest geometric margin.
Edit intent: coherence
Original:  In few-shot learning scenarios, the training samples are scarce, not enough to find a separating hyperplane with good generalization ability on unseen data.
Revised:  In few-shot learning , it is challenging to find such classifiers with good generalization ability on unseen data.
Edit intent: clarity
Original:  TMMC is constructed using a mixture of the labeled support setand the unlabeled query set in a given task.
Revised:  TMMC is constructed using a mixture of the unlabeled query set in a given task.
Edit intent: fluency
Original:  The unlabeled samples in the query set can adjust the separating hyperplane so that the prediction function is optimal on both the labeled and unlabeled samples .
Revised:  The unlabeled samples in the query set can adjust the separating hyperplane so that the prediction function is optimal on both the support and query sets .
Edit intent: clarity
Original:  Furthermore, we leverage an efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize TMMC . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves state-of-the-art accuracies .
Revised:  Furthermore, we use an efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize TMMC . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves state-of-the-art accuracies .
Edit intent: clarity
Original:  Furthermore, we leverage an efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize TMMC . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves state-of-the-art accuracies .
Revised:  Furthermore, we leverage an efficient and effective quasi-Newton algorithm, the L-BFGS method for optimization . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves state-of-the-art accuracies .
Edit intent: clarity
Original:  Furthermore, we leverage an efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize TMMC . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves state-of-the-art accuracies .
Revised:  Furthermore, we leverage an efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize TMMC . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB show that our method achieves state-of-the-art accuracies .
Edit intent: clarity
Original:  Furthermore, we leverage an efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize TMMC . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves state-of-the-art accuracies .
Revised:  Furthermore, we leverage an efficient and effective quasi-Newton algorithm, the L-BFGS method to optimize TMMC . Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB suggest that our TMMC achieves state-of-the-art performance .
Edit intent: clarity
Original:  Despite their apparent differences  , these methods are theoretically equivalent as they represent the synthesis and analysis sides of the same problem  .
Revised:  Despite their apparent differences , these methods are theoretically equivalent as they represent the synthesis and analysis sides of the same problem , respectively .
Edit intent: clarity
Original:  We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental data of motor task and resting-state fMRI . We evaluate the parameter settings that lead to equivalent results, and showcase the potential of these algorithms compared to other widely-used approaches.
Revised:  We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental fMRI data acquired during a motor task and resting-state fMRI . We evaluate the parameter settings that lead to equivalent results, and showcase the potential of these algorithms compared to other widely-used approaches.
Edit intent: clarity
Original:  We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental data of motor task and resting-state fMRI . We evaluate the parameter settings that lead to equivalent results, and showcase the potential of these algorithms compared to other widely-used approaches.
Revised:  We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental data of motor task and resting-state . We evaluate the parameter settings that lead to equivalent results, and showcase the potential of these algorithms compared to other widely-used approaches.
Edit intent: clarity
Original:  We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental data of motor task and resting-state fMRI . We evaluate the parameter settings that lead to equivalent results, and showcase the potential of these algorithms compared to other widely-used approaches.
Revised:  We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental data of motor task and resting-state fMRI . We evaluate the parameter settings that lead to equivalent results, and showcase the potential of these algorithms compared to other common approaches.
Edit intent: clarity
Original:  Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalance learning.
Revised:  Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalanced learning.
Edit intent: fluency
Original: We present a neural video compression method based on generative adversarial networks (GANs) that outperforms previous neural  video compression methods and is comparable to HEVC in a user study .
Revised: We present a neural video compression method based on generative adversarial networks (GANs) . Our approach significantly outperforms previous neural video compression methods and is comparable to HEVC in a user study .
Edit intent: clarity
Original: We present a neural video compression method based on generative adversarial networks (GANs) that outperforms previous neural  video compression methods and is comparable to HEVC in a user study .
Revised: We present a neural video compression method based on generative adversarial networks (GANs) that outperforms previous neural video compression methods in a user study .
Edit intent: coherence
Original: We present in detail the network design choices , their relative importance, and elaborate on the challenges of evaluating video compression methods in user studies.
Revised: We present in detail the network design choices in detail, and ablate them with user studies.
Edit intent: clarity
Original:  Based on these categories, we identify three parameters that are relevant to take into account when constructing a corruption benchmark : number of represented categories, balance among categories andsize of benchmarks.
Revised:  Based on these categories, we identify three relevant parameters to take into account when constructing a corruption benchmark : number of represented categories, balance among categories andsize of benchmarks.
Edit intent: coherence
Original:  Applying the proposed methodology , we build a new benchmark called ImageNet-Syn2Nat to predict image classifier robustness .
Revised:  Applying the proposed methodology , we build new synthetic corruption selections that are more predictive of robustness to natural corruptions than existing synthetic corruption benchmarks .
Edit intent: clarity
Original:  We propose a general-purpose dialogue system architecture that leverages computational argumentation and state-of-the-art language technologies .
Revised:  We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers .
Edit intent: clarity
Original:  We illustrate and evaluate the system using a COVID-19 vaccine information case study.
Revised:  We illustrate the system using a COVID-19 vaccine information case study.
Edit intent: clarity
Original:  With this synthetic method, we propose \textit{optimization-based ABA (OP-ABA) by iteratively optimizing an adversarial objective function against the tracking w.r.t. the motion and light accumulation parameters.
Revised:  With this synthetic method, we propose \textit{optimization-based ABA (OP-ABA) by iteratively optimizing an adversarial objective function against the tracking w.r.t. the motion and light accumulation parameters.
Edit intent: fluency
Original:  To alleviate this issue, we further propose one-step ABA (OS-ABA) where we design and train a joint adversarial motion and accumulation predictive network (JAMANet) with the guidance of OP-ABA, which is able to efficiently estimate the adversarial motion and accumulation parameters in a one-step way.
Revised:  To alleviate this issue, we further propose one-step ABA (OS-ABA) where we design and train a joint adversarial motion and accumulation predictive network (JAMANet) with the guidance of OP-ABA, which is able to efficiently estimate the adversarial motion and accumulation parameters in a one-step way.
Edit intent: fluency
Original: Recent advances in linguistic steganalysis have successively applied CNNs, RNNs, GNNs and other deep learning models for detecting secret information in generative texts.
Revised: Recent advances in linguistic steganalysis have successively applied CNN, RNN, GNN and other efficient deep models for detecting secret information in generative texts.
Edit intent: clarity
Original:  However, we have found through experiments that there actually exists significant difference between automatically generated steganographic texts and carrier texts in terms of the conditional probability distribution of individual words.
Revised:  However, we have found through experiments that there actually exists significant difference between automatically generated stego texts and carrier texts in terms of the conditional probability distribution of individual words.
Edit intent: clarity
Original:  Such kind of statistical difference can be naturally captured by the language model used for generating steganographic texts, which drives us to give the classifier a priori knowledge of the language model to enhance the steganalysis ability.
Revised:  Such kind of difference can be naturally captured by the language model used for generating steganographic texts, which drives us to give the classifier a priori knowledge of the language model to enhance the steganalysis ability.
Edit intent: clarity
Original:  To this end, we present two methods to efficient linguistic steganalysis in this paper .
Revised:  To this end, we present two methods to efficient linguistic steganalysis .
Edit intent: clarity
Original:  Experimental results show that the two methods have different degrees of performance improvement when compared to the randomly initialized RNN classifier , and the convergence speed is significantly accelerated.
Revised:  The results indicate that the two methods have different degrees of performance improvement when compared to the randomly initialized RNN classifier , and the convergence speed is significantly accelerated.
Edit intent: clarity
Original:  Experimental results show that the two methods have different degrees of performance improvement when compared to the randomly initialized RNN classifier , and the convergence speed is significantly accelerated.
Revised:  Experimental results show that the two methods have different degrees of performance gain compared to the randomly initialized RNN classifier , and the convergence speed is significantly accelerated.
Edit intent: clarity
Original:  Experimental results show that the two methods have different degrees of performance improvement when compared to the randomly initialized RNN classifier , and the convergence speed is significantly accelerated.
Revised:  Experimental results show that the two methods have different degrees of performance improvement when compared to the randomly initialized RNN , and the convergence speed is significantly accelerated.
Edit intent: clarity
Original:  Rephrasing these methods as directed acyclic graphs (DAGs) is a particularly effective way to study existing techniques, improve them, and eventually derive new ones.
Revised:  Rephrasing these methods as directed acyclic graphs (DAGs) is a particularly effective approach to study existing techniques, improve them, and eventually derive new ones.
Edit intent: clarity
Original:  As the accuracy of these matrix techniques is determined by the accuracy of their scalar counterparts, the design of algorithms for matrix functions can be viewed as a scalar-valued optimization problem.
Revised:  The accuracy of these matrix techniques is determined by the accuracy of their scalar counterparts, the design of algorithms for matrix functions can be viewed as a scalar-valued optimization problem.
Edit intent: fluency
Original:  As the accuracy of these matrix techniques is determined by the accuracy of their scalar counterparts, the design of algorithms for matrix functions can be viewed as a scalar-valued optimization problem.
Revised:  As the accuracy of these matrix techniques can be characterized by the accuracy of their scalar counterparts, the design of algorithms for matrix functions can be viewed as a scalar-valued optimization problem.
Edit intent: clarity
Original:  As the accuracy of these matrix techniques is determined by the accuracy of their scalar counterparts, the design of algorithms for matrix functions can be viewed as a scalar-valued optimization problem.
Revised:  As the accuracy of these matrix techniques is determined by the accuracy of their scalar counterparts, thus designing algorithms for matrix functions can be viewed as a scalar-valued optimization problem.
Edit intent: coherence
Original:  As the accuracy of these matrix techniques is determined by the accuracy of their scalar counterparts, the design of algorithms for matrix functions can be viewed as a scalar-valued optimization problem.
Revised:  As the accuracy of these matrix techniques is determined by the accuracy of their scalar counterparts, the design of algorithms for matrix functions can be regarded as a scalar-valued optimization problem.
Edit intent: clarity
Original:  The derivatives needed during the optimization can be calculated automatically by exploiting the structure of the DAG, in a fashion akin to backpropagation.
Revised:  The derivatives needed during the optimization can be calculated automatically by exploiting the structure of the DAG, in a fashion analogous to backpropagation.
Edit intent: fluency
Original:  The Julia package GraphMatFun.jl offers the tools to generate and manipulate computational graphs, to optimize their coefficients, and to generate Julia, MATLAB, and C code to evaluate them efficiently  .
Revised:  This paper describes GraphMatFun.jl offers the tools to generate and manipulate computational graphs, to optimize their coefficients, and to generate Julia, MATLAB, and C code to evaluate them efficiently .
Edit intent: coherence
Original:  The Julia package GraphMatFun.jl offers the tools to generate and manipulate computational graphs, to optimize their coefficients, and to generate Julia, MATLAB, and C code to evaluate them efficiently  .
Revised:  The Julia package GraphMatFun.jl , a Julia package that offers the means to generate and manipulate computational graphs, to optimize their coefficients, and to generate Julia, MATLAB, and C code to evaluate them efficiently .
Edit intent: clarity