wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Experiments and ablation studies demonstrate that Universal ASR not only simplifies the workflow of training and deploying streaming and full-context ASR models, but also significantly improves both emission latency and recognition accuracy of streaming ASR.
Revised:  Experiments and ablation studies demonstrate that Dual-mode ASR not only simplifies the workflow of training and deploying streaming and full-context ASR models, but also significantly improves both emission latency and recognition accuracy of streaming ASR.
Edit intent: meaning-changed
Original: To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories  . A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper;
Revised: To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison . A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper;
Edit intent: meaning-changed
Original:  Human assessment by domain experts shows that 41.7\%-70.5\% of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones  20\% of the time.
Revised:  Human assessment by domain experts shows that 41.7\%-70.5\% of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones for 20\% of the time.
Edit intent: clarity
Original:  Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the state-of-the-art models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e  , equation relevance, topic relevance, and language coherence. 
Revised:  Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the state-of-the-art models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e . , equation relevance, topic relevance, and language coherence. 
Edit intent: fluency
Original:  Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the state-of-the-art models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e  , equation relevance, topic relevance, and language coherence. 
Revised:  Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the state-of-the-art models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e , equation relevance, topic relevance, and language coherence. To encourage reproducible results, we make our code and MWP dataset public available at URL
Edit intent: meaning-changed
Original:  By integrating genetics, biophysics, deep learning, and algebraic topology, we deduce that some of the mutations such as M153I, S254F, and S255F may weaken the binding of S protein and antibodies , and potentially disrupt the efficacy and reliability of antibody therapies and vaccines in the development.
Revised:  By integrating genetics, biophysics, deep learning, and algebraic topology, we reveal that most of 462 mutations on the receptor-binding domain (RBD) will weaken the binding of S protein and antibodies , and potentially disrupt the efficacy and reliability of antibody therapies and vaccines in the development.
Edit intent: meaning-changed
Original:  By integrating genetics, biophysics, deep learning, and algebraic topology, we deduce that some of the mutations such as M153I, S254F, and S255F may weaken the binding of S protein and antibodies , and potentially disrupt the efficacy and reliability of antibody therapies and vaccines in the development.
Revised:  By integrating genetics, biophysics, deep learning, and algebraic topology, we deduce that some of the mutations such as M153I, S254F, and S255F may weaken the binding of S protein and antibodies and disrupt the efficacy and reliability of antibody therapies and vaccines in the development.
Edit intent: clarity
Original:  By integrating genetics, biophysics, deep learning, and algebraic topology, we deduce that some of the mutations such as M153I, S254F, and S255F may weaken the binding of S protein and antibodies , and potentially disrupt the efficacy and reliability of antibody therapies and vaccines in the development. We provide a strategy to prioritize the selection of mutations for designing vaccines or antibody cocktails.
Revised:  By integrating genetics, biophysics, deep learning, and algebraic topology, we deduce that some of the mutations such as M153I, S254F, and S255F may weaken the binding of S protein and antibodies , and potentially disrupt the efficacy and reliability of antibody therapies and vaccines 
Edit intent: coherence
Original: %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > existing RBD mutations, including those variants recently found in the United Kingdom (UK) and South Africa, are binding-strengthen mutations, resulting in more infectious COVID-19 variants.
Revised: %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% . A list of 31 vaccine escape mutants is identified, while many other disruptive mutations are detailed as well. We also unveil that about 65\\%DIF > existing RBD mutations, including those variants recently found in the United Kingdom (UK) and South Africa, are binding-strengthen mutations, resulting in more infectious COVID-19 variants.
Edit intent: meaning-changed
Original:  The utility of the proposed likelihood model is demonstrated in a visual odometry ego-motion simulation study, which results in 30-83\% reduction in position drift rate compared to traditional methodsemploying a Gaussian error assumption .
Revised:  The utility of the proposed likelihood model is demonstrated in a visual odometry ego-motion study, which results in 30-83\% reduction in position drift rate compared to traditional methodsemploying a Gaussian error assumption .
Edit intent: clarity
Original:  We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking.
Revised:  We define underranking based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking.
Edit intent: meaning-changed
Original:  We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking.
Revised:  We define individual fairness based on how close the group-fair rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking.
Edit intent: clarity
Original:  We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking.
Revised:  We define individual fairness based on how close the predicted rank of each item is to its original rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking.
Edit intent: clarity
Original:  We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking.
Revised:  We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous underranking and group fairness in ranking.
Edit intent: clarity
Original:  We applied Weight Squeezing combined with Knowledge Distillation to a pre-trained text classification model , and compared it to various knowledge transfer and model compression methods on several downstream text classification tasks  .
Revised:  We applied Weight Squeezing combined with Knowledge Distillation to a pre-trained text classification model , and compared it to various knowledge transfer and model compression methods on several downstream text classification tasks based on the GLUE dataset .
Edit intent: meaning-changed
Original:  The applications of TAMGI are illustrated by the study of hidden periodic motifs in the genomes of the severe acute respiratory syndrome coronaviruses SARS-CoV and SARS-CoV-2 (the latter coronavirus SARS-CoV-2 being responsible for the COVID-19 pandemia ) packaged within filament-like helical capsid.
Revised:  The applications of TAMGI are illustrated by the study of motifs in the genomes of the severe acute respiratory syndrome coronaviruses SARS-CoV and SARS-CoV-2 (the latter coronavirus SARS-CoV-2 being responsible for the COVID-19 pandemia ) packaged within filament-like helical capsid.
Edit intent: clarity
Original:  The applications of TAMGI are illustrated by the study of hidden periodic motifs in the genomes of the severe acute respiratory syndrome coronaviruses SARS-CoV and SARS-CoV-2 (the latter coronavirus SARS-CoV-2 being responsible for the COVID-19 pandemia ) packaged within filament-like helical capsid.
Revised:  The applications of TAMGI are illustrated by the study of hidden periodic motifs in the genomes of the severe acute respiratory syndrome coronaviruses SARS-CoV and SARS-CoV-2 (the latter coronavirus SARS-CoV-2 being responsible for the COVID-19 pandemic ) packaged within filament-like helical capsid.
Edit intent: fluency
Original:  In our previous paper "Prevalence of deficiency zero reaction networks in an Erdos-Renyi framework, " we provided a framework to quantify the prevalence of deficiency zero among randomly generated reaction networks.
Revised:  In our previous paper Prevalence of deficiency zero reaction networks in an Erd\H os-R\'enyi framework we provided a framework to quantify the prevalence of deficiency zero among randomly generated reaction networks.
Edit intent: fluency
Original:  In our previous paper "Prevalence of deficiency zero reaction networks in an Erdos-Renyi framework, " we provided a framework to quantify the prevalence of deficiency zero among randomly generated reaction networks.
Revised:  In our previous paper "Prevalence of deficiency zero reaction networks in an Erdos-Renyi framework, ", we provided a framework to quantify the prevalence of deficiency zero among randomly generated reaction networks.
Edit intent: fluency
Original:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n){r(n)}}\to  and converges to 0 if p_n \gg r(n){r(n)}}\to , as n \to \infty.
Revised:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if \frac{p_n{r(n)}}\to and converges to 0 if p_n \gg r(n){r(n)}}\to , as n \to \infty.
Edit intent: meaning-changed
Original:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n){r(n)}}\to  and converges to 0 if p_n \gg r(n){r(n)}}\to , as n \to \infty.
Revised:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n){r(n)}}\to 0 and converges to 0 if p_n \gg r(n){r(n)}}\to , as n \to \infty.
Edit intent: fluency
Original:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n){r(n)}}\to  and converges to 0 if p_n \gg r(n){r(n)}}\to , as n \to \infty.
Revised:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n){r(n)}}\to and converges to 0 if \frac{p_n{r(n)}}\to , as n \to \infty.
Edit intent: meaning-changed
Original:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n){r(n)}}\to  and converges to 0 if p_n \gg r(n){r(n)}}\to , as n \to \infty.
Revised:  Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability p_n, we established the threshold function r(n)=1{n^3} such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n){r(n)}}\to and converges to 0 if p_n \gg r(n){r(n)}}\to\infty , as n \to \infty.
Edit intent: fluency
Original:  With the base Erdos-Renyi framework as a starting point, the current paper provides a significantly more flexible framework by weighting the edge probabilities via control parameters i,j, with i,j\in \{0,1,2\} enumerating the types of possible vertices (zeroth, first, or second order).
Revised:  With the base Erd\H os-R\'enyi framework as a starting point, the current paper provides a significantly more flexible framework by weighting the edge probabilities via control parameters i,j, with i,j\in \{0,1,2\} enumerating the types of possible vertices (zeroth, first, or second order).
Edit intent: fluency
Original:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to  and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to  .
Revised:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if \}) } {r(n,\{\alpha_{i,j}\})}}\to and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to .
Edit intent: meaning-changed
Original:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to  and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to  .
Revised:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } \frac{p_n{r(n,\{\alpha_{i,j}\})}}\to and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to .
Edit intent: meaning-changed
Original:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to  and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to  .
Revised:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to 0 and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to .
Edit intent: fluency
Original:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to  and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to  .
Revised:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to and converges to 0 if \})} {r(n,\{\alpha_{i,j}\})}}\to .
Edit intent: meaning-changed
Original:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to  and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to  .
Revised:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} \frac{p_n{r(n,\{\alpha_{i,j}\})}}\to .
Edit intent: meaning-changed
Original:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to  and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to  .
Revised:  Under this new framework, for each choice of control parameters \{i,j\}, we establish a threshold function r(n,\{i,j\}) such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n,\{\alpha_{i,j\}) } {r(n,\{\alpha_{i,j}\})}}\to and converges to 0 if p_n \gg r(n,\{\alpha_{i,j\})} {r(n,\{\alpha_{i,j}\})}}\to \infty .
Edit intent: meaning-changed
Original:  Consequently, examples with low difficulty scores tend to be robust to multiple word substitutions. Our analysis shows that perturbations like contrast sets and counterfactual examples are not necessarily difficult for the model, and they may not be accomplishing the intended goal.
Revised:  These classifiers tend to perform poorly on the FIM test set. (generated by sampling and perturbing difficult examples, with accuracy dropping below 50\%). We replicate our experiments on 5 NLP datasets (YelpReviewPolarity, AGNEWS, SogouNews, YelpReviewFull and Yahoo Answers). On YelpReviewPolarity we observe a correlation coefficient of -0.4 between resilience to perturbations and the difficulty score. Similarly we observe a correlation of 0.35 between the difficulty score and the empirical success probability of random substitutions.
Edit intent: meaning-changed
Original:  Generating natural language has fundamentally been a human attribute and the advent of ubiquitous NLP applications and virtual agents marks the need to impart this skill to machines. There has been a colossal research effort in various frontiers of neural text generationincluding machine translation, summarization, image captioning, storytelling etc., We believe that this is an excellent juncture to retrospect on the directions of the field.
Revised:  In order to progress research in text generation, it is critical to absorb the existing research works and position ourselves in this massively growing field.
Edit intent: clarity
Original:  Specifically, this paper surveys the fundamental factors and components relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In specific , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges  .
Revised:  Specifically, this paper surveys the fundamental components of modeling approaches relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In specific , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges .
Edit intent: clarity
Original:  Specifically, this paper surveys the fundamental factors and components relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In specific , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges  .
Revised:  Specifically, this paper surveys the fundamental factors and components relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In this context , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges .
Edit intent: clarity
Original:  Specifically, this paper surveys the fundamental factors and components relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In specific , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges  .
Revised:  Specifically, this paper surveys the fundamental factors and components relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In specific , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them .
Edit intent: meaning-changed
Original:  We adapt the Reeb graph edit distance of Landi et. al. to each of our categories and prove that both functors in our pipeline are 1-Lipschitz making our pipeline stable  .
Revised:  We adapt the Reeb graph edit distance of Landi et. al. to each of our categories and prove that both functors in our pipeline are 1-Lipschitz making our pipeline stable . Our constructions generalize the classical persistence diagram and, in this setting, the bottleneck distance is strongly equivalent to the edit distance .
Edit intent: meaning-changed