wanyu/IteraTeR_v2 huggingface dataset:
Original:  To overcome these challenges, we propose X-BERT, the first feasible attempt to finetune BERT models for a scalable solution to the XMC problem.
Revised:  To overcome these challenges, we propose X-BERT, the first scalable solution to finetune BERT models for a scalable solution to the XMC problem.
Edit intent: clarity
Original:  To overcome these challenges, we propose X-BERT, the first feasible attempt to finetune BERT models for a scalable solution to the XMC problem.
Revised:  To overcome these challenges, we propose X-BERT, the first feasible attempt to finetune BERT models on the XMC problem.
Edit intent: clarity
Original:  Specifically, X-BERT leverages both the label and document text to build label representations, which induces semantic label clusters in order to better model label dependencies.
Revised:  Specifically, X-BERT leverages both the label and input text to build label representations, which induces semantic label clusters in order to better model label dependencies.
Edit intent: clarity
Original:  Specifically, X-BERT leverages both the label and document text to build label representations, which induces semantic label clusters in order to better model label dependencies.
Revised:  Specifically, X-BERT leverages both the label and document text to build label representations, which induces semantic label clusters to better model label dependencies.
Edit intent: coherence
Original:  At the heart of X-BERT is finetuning BERT models to capture the contextual relations between input text and the induced label clusters.
Revised:  At the heart of X-BERT is a procedure to finetune BERT models to capture the contextual relations between input text and the induced label clusters.
Edit intent: clarity
Original:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Revised:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model , which leads to a state-of-the-art XMC method. In particular , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Edit intent: clarity
Original:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Revised:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Edit intent: coherence
Original:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Revised:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 of X-BERT is 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Edit intent: clarity
Original:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Revised:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over the neural baseline fastText and competing XMC approach Parabel, respectively.
Edit intent: clarity
Original:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and competing XMC approach Parabel, respectively.
Revised:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model . Empirically , on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67: 80 \%, a substantial improvement over 32.58\%/60.91\% of deep learning baseline fastText and a state-of-the-art XMC approach Parabel, respectively.
Edit intent: clarity
Original: Extreme multi-label text classification (XMC) concerns tagging input textwith the most relevant labels from an extremely large set.
Revised: We consider the extreme multi-label text classification (XMC) concerns tagging input textwith the most relevant labels from an extremely large set.
Edit intent: coherence
Original: Extreme multi-label text classification (XMC) concerns tagging input textwith the most relevant labels from an extremely large set.
Revised: Extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from an extremely large set.
Edit intent: clarity
Original:  Recently, pre-trained language representation models such as BERT (Bidirectional Encoder Representations from Transformers) have been shown to achieve outstanding performance on many NLP tasks including sentence classification  with small label sets (typically fewer than thousands) .
Revised:  Recently, pre-trained language representation models such as BERT (Bidirectional Encoder Representations from Transformers) have been shown to achieve outstanding performance on many NLP tasks including sentence classification , albeit with small label sets (typically fewer than thousands) .
Edit intent: clarity
Original:  Recently, pre-trained language representation models such as BERT (Bidirectional Encoder Representations from Transformers) have been shown to achieve outstanding performance on many NLP tasks including sentence classification  with small label sets (typically fewer than thousands) .
Revised:  Recently, pre-trained language representation models such as BERT (Bidirectional Encoder Representations from Transformers) have been shown to achieve outstanding performance on many NLP tasks including sentence classification with small label sets .
Edit intent: clarity
Original:  To overcome these challenges , we propose X-BERT , the first scalable solution to finetune BERT models on the XMC problem.
Revised:  To overcome these challenges , we propose X-BERT , the first scalable approach to fine-tuning deep transformer models for the XMC problem.
Edit intent: clarity
Original:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model, which leads to a state-of-the-art XMC method .
Revised:  Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model, which leads to a state-of-the-art results on four XMC benchmark datasets .
Edit intent: clarity
Original:  In particular, on a Wiki dataset with around 0.5 million labels, the precision @1 of X-BERT is 67:87 \%, a substantial improvement over the neural baseline fastText and a state-of-the-art XMC approach Parabel , which achieves 32:58\% and 60:91 \% precision@1, respectively  .
Revised:  In particular, on a Wiki dataset with around 0.5 million labels, the prec @1 of X-BERT is 67:87 \%, a substantial improvement over the neural baseline fastText and a state-of-the-art XMC approach Parabel , which achieves 32:58\% and 60:91 \% precision@1, respectively .
Edit intent: fluency
Original: Almost every known turnstile streaming algorithm is implementable as a linear sketch .
Revised: Almost every known turnstile streaming algorithm can be implemented as a linear sketch .
Edit intent: clarity
Original:  It was shown in ~ LNW14 that, if a turnstile algorithm works for arbitrarily long streams with arbitrarily large coordinates at intermediate stages of the stream, then the turnstile algorithm can be implemented as a linear sketch.
Revised:  It was shown in LNW14 that, if a turnstile algorithm works for arbitrarily long streams with arbitrarily large coordinates at intermediate stages of the stream, then the turnstile algorithm can be implemented as a linear sketch.
Edit intent: fluency
Original:  It was shown in ~ LNW14 that, if a turnstile algorithm works for arbitrarily long streams with arbitrarily large coordinates at intermediate stages of the stream, then the turnstile algorithm can be implemented as a linear sketch.
Revised:  It was shown in ~ LNW14 that, if a turnstile algorithm works for arbitrarily long streams with arbitrarily large coordinates at intermediate stages of the stream, then the turnstile algorithm is equivalent to a linear sketch.
Edit intent: clarity
Original:  Our results have the opposite form: if either the stream length or the maximum value of the stream are substantially restricted, there exist problems where linear sketching is exponentially harder than turnstile streaming  .
Revised:  We show separations of the opposite form: if either the stream length or the maximum value of the stream are substantially restricted, there exist problems where linear sketching is exponentially harder than turnstile streaming .
Edit intent: clarity
Original:  We propose an alternative relaxation of differential privacy, which we term " f-differential privacy ", which has a number of appealing properties and avoids some of the difficulties associated with divergence based relaxations.
Revised:  We propose an alternative relaxation of differential privacy, which we term ` f-differential privacy ", which has a number of appealing properties and avoids some of the difficulties associated with divergence based relaxations.
Edit intent: fluency
Original:  We propose an alternative relaxation of differential privacy, which we term " f-differential privacy ", which has a number of appealing properties and avoids some of the difficulties associated with divergence based relaxations.
Revised:  We propose an alternative relaxation of differential privacy, which we term " f-differential privacy ", which has a number of appealing properties and , in particular, avoids difficulties associated with divergence based relaxations.
Edit intent: coherence
Original:  We show that this family is focal by proving a central limit theorem , which shows that the privacy guarantees of any hypothesis-testing based definition of privacy (including differential privacy ) converges to Gaussian differential privacy in the limit under composition.
Revised:  We show that this family is focal by proving a central limit theorem , which shows that the privacy guarantees of any hypothesis testing based definition of privacy (including differential privacy ) converges to Gaussian differential privacy in the limit under composition.
Edit intent: fluency
Original:  We demonstrate the use of the tools we develop by giving an improved analysis of the privacy guarantees of noisy stochastic gradient descent.
Revised:  We demonstrate the use of the tools we develop by giving an improved privacy analysis of noisy stochastic gradient descent.
Edit intent: clarity
Original:  This model makes a parameter-free prediction for the average number of memory items recalled (RC) out of M items in memory: RC = 3\pi M/2.
Revised:  This model makes a parameter-free prediction for the average number of memory items recalled (RC) out of M items in memory: R = 3\pi M/2.
Edit intent: fluency
Original: We consider efficiency in  deep neural networks.
Revised: We consider efficiency in the implementation of deep neural networks.
Edit intent: clarity
Original:  In many situations, these interactions are not observed and have to be inferred from species abundance data.
Revised:  We consider the problem of inferring the species interaction network from abundance data.
Edit intent: clarity
Original:  To be relevant, any reconstruction network methodology needs to handle count data and to account for possible environmental effects.
Revised:  To be relevant, any network inference methodology needs to handle count data and to account for possible environmental effects.
Edit intent: clarity
Original:  It also needs to distinguish between direct  and indirect interactions and graphical models provide a convenient framework for this purpose.
Revised:  It also needs to distinguish between direct and graphical models provide a convenient framework for this purpose.
Edit intent: clarity
Original:  We introduce a generic statistical model for network reconstruction based on abundance data.
Revised:  We introduce a generic statistical model for network inference based on abundance data.
Edit intent: clarity
Original:  A simulation study shows that the proposed methodology compares well with state-of-the-art approaches, even when the underlying network strongly differs from a tree.
Revised:  A simulation study shows that the proposed methodology compares well with state-of-the-art approaches, even when the underlying graph strongly differs from a tree.
Edit intent: clarity
Original:  The analysis of two data sets highlights the influence of covariates on the inferred network.
Revised:  The analysis of two datasets highlights the influence of covariates on the inferred network.
Edit intent: clarity
Original:  Here we show that the case of equal or rationally related time-delay and clock cycle could be actively detrimental and leads to an increase of the approximation error of the reservoir.
Revised:  Here we show that the case of equal or resonant time-delay and clock cycle could be actively detrimental and leads to an increase of the approximation error of the reservoir.
Edit intent: clarity
Original:  State-of-the-art methods explore the use of end-to-end neural networks, including a shallow (up to three layers) 3D convolutional neural network (CNN) + a deep 2D CNN ( e.g. , ResNet) as the front-end to extract visual features, and a recurrent neural network ( e.g. , bidirectional LSTM) as the back-end for classification.
Revised:  State-of-the-art methods explore the use of end-to-end neural networks, including a shallow (up to three layers) 3D convolutional neural network (CNN) + a deep 2D CNN ( e.g. , ResNet) as the front-end to extract visual features, and a recurrent neural network ( e.g. , bidirectional LSTM) as the back-end for classification.
Edit intent: fluency
Original:  In this work, we propose to replace the shallow 3D CNNs + deep 2D CNNs front-end with recent successful deep 3D CNNs --- two-stream ( i.e. , grayscale video and optical flow streams) I3D.
Revised:  In this work, we propose to replace the shallow 3D CNNs + deep 2D CNNs front-end with recent successful deep 3D CNNs --- two-stream ( i.e. , grayscale video and optical flow streams) I3D.
Edit intent: fluency
Original:  The experiments show that, compared to the shallow 3D CNNs + deep 2D CNNs front-end, the deep 3D CNNs front-end with pre-training on the large-scale image and video datasets ( e.g. , ImageNet and Kinetics) can improve the classification accuracy.
Revised:  The experiments show that, compared to the shallow 3D CNNs + deep 2D CNNs front-end, the deep 3D CNNs front-end with pre-training on the large-scale image and video datasets ( e.g. , ImageNet and Kinetics) can improve the classification accuracy.
Edit intent: fluency
Original:  On the other hand , we demonstrate that using the optical flow input alone can achieve comparable performance as using the grayscale video as input.
Revised:  Also , we demonstrate that using the optical flow input alone can achieve comparable performance as using the grayscale video as input.
Edit intent: coherence
Original:  With seven input audio streams, our system achieves a word error rate (WER) of 22.3\% and comes within 3\% of the close-talking microphone WER on the non-overlapping speech segments.
Revised:  When utilizing seven input audio streams, our system achieves a word error rate (WER) of 22.3\% and comes within 3\% of the close-talking microphone WER on the non-overlapping speech segments.
Edit intent: clarity
Original:  The relative gains in SAWER over a single-device system are 14.8\%, 20.3\%, and 22.4\% for three, five, and seven microphones, respectively.
Revised:  The relative gains in SAWER over the single-device system are 14.8\%, 20.3\%, and 22.4\% for three, five, and seven microphones, respectively.
Edit intent: fluency
Original: In this paper we give an overview on the equilibrium properties of a large class of stochastic processes describing the fundamental biological process within  cells, {\em the production process of proteins}.
Revised: In this paper we analyze the equilibrium properties of a large class of stochastic processes describing the fundamental biological process within cells, {\em the production process of proteins}.
Edit intent: clarity
Original:  An extension of these models, which includes in particular elongation phases of mRNAs and proteins, is introduced.
Revised:  An extension of these models, which includes elongation phases of mRNAs and proteins, is introduced.
Edit intent: clarity
Original:  The distribution of the equilibrium of this extended model is analyzed . Explicit expressions are obtained for the first two moments of the number of mRNAs and proteins at equilibrium  generalizing some classical formulas.
Revised:  The distribution of the equilibrium of this extended model is analyzed . Explicit expressions for the first two moments of the number of mRNAs and proteins at equilibrium generalizing some classical formulas.
Edit intent: clarity
Original:  In particular, several convergence results for the distribution of the number of proteins at equilibrium are  obtained under different scaling assumptions.
Revised:  Several convergence results for the distribution of the number of proteins at equilibrium are obtained under different scaling assumptions.
Edit intent: coherence
Original:  In particular, several convergence results for the distribution of the number of proteins at equilibrium are  obtained under different scaling assumptions.
Revised:  In particular, several convergence results for the distribution of the number of proteins at equilibrium are in particular obtained under different scaling assumptions.
Edit intent: fluency
Original:  It is conjectured that the joint probability distribution of every k=m-2-th (m >4 \geq4 ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3.
Revised:  It is conjectured that the joint probability distribution of every k=m-2-th (m \geq4 ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3.
Edit intent: clarity
Original:  It is also shown that the spectral fluctuations corresponding to k=m+1 (m\geq2) and k=m-3-th (m\geq5) order spacing ratio distribution is identical to that of nearest neighbor spacing ratio distribution with Dyson indices m+2 and m-4 respectively.
Revised:  It is also conjectured that the spectral fluctuations corresponding to k=m+1 (m\geq2) and k=m-3-th (m\geq5) order spacing ratio distribution is identical to that of nearest neighbor spacing ratio distribution with Dyson indices m+2 and m-4 respectively.
Edit intent: clarity
Original:  It is conjectured that the joint probability distribution of every k=m-2-th (m%DIFDELCMD < \geq4%%% ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3 . For the special case of m= 3, k = \beta=3.
Revised:  %DIFDELCMD < \geq4%%% ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3 . For the special case of m= 3, k = \beta=3.
Edit intent: coherence
Original:  It is conjectured that the joint probability distribution of every k=m-2-th (m%DIFDELCMD < \geq4%%% ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3 . For the special case of m= 3, k = \beta=3.
Revised:  It is conjectured that the joint probability distribution of every k=m-2-th (m%DIFDELCMD < \geq4%%% ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3 . For the case of m= 3, k = \beta=3.
Edit intent: clarity
Original:  It is conjectured that the joint probability distribution of every k=m-2-th (m%DIFDELCMD < \geq4%%% ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3 . For the special case of m= 3, k = \beta=3.
Revised:  It is conjectured that the joint probability distribution of every k=m-2-th (m%DIFDELCMD < \geq4%%% ) eigenvalue corresponds to that of circular \beta-ensemble with \beta=m-3 . For the special case of m= 2 (m = \beta=3.
Edit intent: clarity
Original:  Occasionally, one or more MAVs can be tasked to compute poses for another MAV through relative measurements, which is achieved through multiple view geometry concepts.
Revised:  Occasionally, one or more MAVs can be tasked to compute poses for another MAV through relative measurements, which is achieved by exploiting multiple view geometry concepts.
Edit intent: clarity
Original:  Although }  movements of many foragers and wandering animals have been shown to follow a L\'{e}vy distribution , investigation into the impact of different heavy-tailed probability distributions on CS is still insufficient up to now.
Revised:  Although } movements of many foragers and wandering animals have been shown to follow a L\'{e}vy distribution , investigation into the efficiency of other different heavy-tailed probability distributions on CS is still insufficient up to now.
Edit intent: clarity
Original:  In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution  Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS.
Revised:  In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution , Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS.
Edit intent: fluency
Original:  Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performance .
Revised:  Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performances .
Edit intent: fluency
Original: Arguably the most common network communication problem is multiple-unicasts: Distinct packets at different nodes in a network need to be delivered to a destination specific to each packet  as fast as possible.
Revised: Arguably the most common network communication problem is multiple-unicasts: Distinct packets at different nodes in a network need to be delivered to a destination specific to each packet , as fast as possible.
Edit intent: fluency