wanyu/IteraTeR_full_sent huggingface dataset:
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised:  }\big) and sample complexity of\tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: clarity
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised:  }\big) \tilde{O}\big(\frac{s{\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: meaning-changed
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big). As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: fluency
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: clarity
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the }\hspace{0pt}%DIFAUXCMD } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: clarity
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD } agnostic model where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: fluency
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD } where no assumption is made on the noise rate \nu , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: coherence
Original:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace. Our algorithm builds upon the celebrated Perceptron while leveraging novel localized sampling and semi-random gradient update to tolerate the adversarial noise. We believe that our algorithmic design and analysis are of independent interest, and may shed light on learning halfspaces with broader noise models .
Revised:  }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{%DIFAUXCMD \cite{kearns1992toward }\hspace{0pt}%DIFAUXCMD } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous halfspace .
Edit intent: meaning-changed
Original: We study the question of when we can answer a Conjunctive Query (CQ) with an ordering over the answers by constructing a structure for direct (random) access to the sorted list of answers, without actually materializing this list, so that the construction time is linear (or quasilinear ) in the size of the database.
Revised: We study the question of when we can provide logarithmic-time direct access to the k-th answer to a Conjunctive Query (CQ) with an ordering over the answers by constructing a structure for direct (random) access to the sorted list of answers, without actually materializing this list, so that the construction time is linear (or quasilinear ) in the size of the database.
Edit intent: meaning-changed
Original: We study the question of when we can answer a Conjunctive Query (CQ) with an ordering over the answers by constructing a structure for direct (random) access to the sorted list of answers, without actually materializing this list, so that the construction time is linear (or quasilinear ) in the size of the database.
Revised: We study the question of when we can answer a Conjunctive Query (CQ) with a specified ordering over the answers by constructing a structure for direct (random) access to the sorted list of answers, without actually materializing this list, so that the construction time is linear (or quasilinear ) in the size of the database.
Edit intent: clarity
Original: We study the question of when we can answer a Conjunctive Query (CQ) with an ordering over the answers by constructing a structure for direct (random) access to the sorted list of answers, without actually materializing this list, so that the construction time is linear (or quasilinear ) in the size of the database.
Revised: We study the question of when we can answer a Conjunctive Query (CQ) with an ordering over the answers , following a preprocessing step that constructs a data structure in time quasilinear in the size of the database.
Edit intent: meaning-changed
Original:  In the absence of answer ordering, such a construction has been devised for the task of enumerating query answers of free-connex acyclic CQs, so that the access time is logarithmic. Moreover, it follows from past results that within the class of CQs without self-joins, being free-connex acyclic is necessary for the existence of such a construction (under conventional assumptions in fine-grained complexity). In this work, we embark on the challenge of identifying the  answer orderings that allow for ranked direct access with the above complexity guarantees.
Revised:  Specifically, we embark on the challenge of identifying the answer orderings that allow for ranked direct access with the above complexity guarantees.
Edit intent: coherence
Original:  Hence, to better understand the computational challenge at hand, we consider the more modest task of providing access to only one single answer (i.e., finding the answer at a given position)  .
Revised:  Hence, to better understand the computational challenge at hand, we consider the more modest task of providing access to only one single answer (i.e., finding the answer at a given position) - a task that we refer to as the selection problem .
Edit intent: meaning-changed
Original: Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [EFMRTT19] demonstrates that random shuffling of input data amplifies differential privacy guarantees  .
Revised: Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [EFMRTT19] demonstrates that random shuffling amplifies differential privacy guarantees .
Edit intent: clarity
Original: Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [EFMRTT19] demonstrates that random shuffling of input data amplifies differential privacy guarantees  .
Revised: Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [EFMRTT19] demonstrates that random shuffling of input data amplifies differential privacy guarantees of locally randomized data .
Edit intent: meaning-changed
Original:  Such amplification leads to substantially stronger privacy guarantees for systems in which data is contributed anonymously [BEMMRLRKTS17] and for the analysis of noisy stochastic gradient descent  .
Revised:  Such amplification leads to substantially stronger privacy guarantees for systems in which data is contributed anonymously [BEMMRLRKTS17] and for the analysis of noisy stochastic gradient descent CSUZZ19,EFMRTT19 .
Edit intent: meaning-changed
Original:  We show that an \varepsilon_0-locally differentially private algorithm, under shuffling with n users, amplifies to a (\Theta ((1-e^{-\varepsilon_0})\frac{e^{\varepsilon_0\log(1/\delta)}{n}}), \delta) -central differential privacy guarantee .
Revised:  We show that random shuffling of n data records that are input to \varepsilon_0-differentially private local randomizers results in an (O ((1-e^{-\varepsilon_0})\frac{e^{\varepsilon_0\log(1/\delta)}{n}}), \delta) -central differential privacy guarantee .
Edit intent: clarity
Original:  We show that an \varepsilon_0-locally differentially private algorithm, under shuffling with n users, amplifies to a (\Theta ((1-e^{-\varepsilon_0})\frac{e^{\varepsilon_0\log(1/\delta)}{n}}), \delta) -central differential privacy guarantee .
Revised:  We show that an \varepsilon_0-locally differentially private algorithm, under shuffling with n users, amplifies to a (\Theta ((1-e^{-\varepsilon_0})\frac{e^{\varepsilon_0\log(1/\delta)}{n}}), \delta) -differentially private algorithm .
Edit intent: clarity
Original:  As a direct corollary of our analysis  we derive a simple and asymptotically optimal algorithm for discrete distribution estimation in the shuffle model of privacy CSUZZ19 .
Revised:  As a direct corollary of our analysis we derive a simple and asymptotically optimal algorithm for discrete distribution estimation in the shuffle model of privacy . We also observe that our result implies the first asymptotically optimal privacy analysis of noisy stochastic gradient descent that applies to sampling without replacement .
Edit intent: meaning-changed
Original: Exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbullying, and hate speech.
Revised: The exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbullying, and hate speech.
Edit intent: fluency
Original: Exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbullying, and hate speech.
Revised: Exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expression and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbullying, and hate speech.
Edit intent: fluency
Original:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
Revised:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing the neural ensemble method of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
Edit intent: clarity
Original:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
Revised:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased / uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
Edit intent: fluency
Original:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
Revised:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) . Subsequently, important (most and least) terms are identified with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
Edit intent: meaning-changed
Original:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) to provide human-interpretable explanations.
Revised:  In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates, by employing neural ensemble of different transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased and uncased, and XLM-RoBERTa) , followed by identifying important terms with sensitivity analysis and layer-wise relevance propagation (LRP) , before providing human-interpretable explanations.
Edit intent: coherence
Original:  Evaluations against several machine learning ~ (linear and tree-based models) and deep neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1 scores of 84\%, 90\%, 88\%, and 88\%, for political, personal, geopolitical, and religious hates, respectively, during 3-fold cross-validation tests .
Revised:  Finally, to measure the quality of the explanation (i.e., faithfulness), we compute the comprehensiveness and sufficiency. Evaluations against machine learning (linear and tree-based models) and deep neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1 scores of 84\%, 90\%, 88\%, and 88\%, for political, personal, geopolitical, and religious hates, respectively, during 3-fold cross-validation tests .
Edit intent: meaning-changed
Original:  Evaluations against several machine learning ~ (linear and tree-based models) and deep neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1 scores of 84\%, 90\%, 88\%, and 88\%, for political, personal, geopolitical, and religious hates, respectively, during 3-fold cross-validation tests .
Revised:  Evaluations against several machine learning ~ (linear and tree-based models) and deep neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1 scores of 84\%, 90\%, 88\%, and 88\%, for political, personal, geopolitical, and religious hates, respectively, outperforming both ML and DNN baselines .
Edit intent: meaning-changed
Original:  However, due to expensive trial costs, most studies only conduct simple trials with dominant approaches (e.g BPE) and commonly used vocabulary sizes. In this paper, we find an exciting relation between an information-theoretic feature and BLEU scores. With thisobservation , we formulate the quest of vocabularization -- finding the best token dictionary with a proper size -- as an optimal transport  problem.
Revised:  This paper aims to figure out what is a good vocabulary and whether one can find the optimal vocabulary without trial training. To answer these questions, we first provide an alternative understanding of the role of vocabulary from the perspective of information theory. Motivated by this , we formulate the quest of vocabularization -- finding the best token dictionary with a proper size -- as an optimal transport problem.
Edit intent: coherence
Original:  To improve the  robustness, adversarial data augmentation (ADA) has been widely adopted , which attempts to cover more search space of adversarial attacks by adding the adversarial examples during training.
Revised:  To improve the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted , which attempts to cover more search space of adversarial attacks by adding the adversarial examples during training.
Edit intent: fluency
Original:  To improve the  robustness, adversarial data augmentation (ADA) has been widely adopted , which attempts to cover more search space of adversarial attacks by adding the adversarial examples during training.
Revised:  To improve the robustness, adversarial data augmentation (ADA) has been widely adopted , which attempts to cover more search space of adversarial attacks by adding textual adversarial examples during training.
Edit intent: meaning-changed
Original:  In this work, we propose a simple and effective method to cover  much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Revised:  In this work, we propose a simple and effective method to cover a much larger proportion of the attack search space, called Adversarial Data Augmentation with Mixup (MixADA ). Specifically, MixADA linearly interpolates the representations of pairs of training examples to form new virtual samples, which are more abundant and diverse than the discrete adversarial examples used in conventional ADA.
Edit intent: fluency