wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As a side benefit, our techniques are also applicable to unranked enumeration (where answers can be returned in any order) for joins with inequalities, returning k answers in \mathcal{O(n }%DIFDELCMD < \operatorname{polylog} %%% n + k). This guarantee improves over the state of the art for large values of k. In an experimental study , we show that our ranked-enumeration approach is not only theoretically interesting, but also fast and memory-efficient in practice .
Revised:  As a side benefit, our techniques are also applicable to unranked enumeration (where answers can be returned in any order) for joins with inequalities, returning k answers in \mathcal{O(n }%DIFDELCMD < \operatorname{polylog} %%% In addition to providing the first non-trivial theoretical guarantees beyond equi-joins, we show in an experimental study , we show that our ranked-enumeration approach is not only theoretically interesting, but also fast and memory-efficient in practice .
Edit intent: meaning-changed
Original: A new class of measures of bivariate tail dependence  is proposed, which is defined as a limit of a measure of concordance of the underlying copula restricted to the tail region of interest.
Revised: A new class of measures of bivariate tail dependence called tail concordance measures (TCMs) is proposed, which is defined as a limit of a measure of concordance of the underlying copula restricted to the tail region of interest.
Edit intent: meaning-changed
Original:  The proposed tail dependence measures include tail dependence coefficients as special cases, but capture the extremal relationship between random variables not only along the diagonal but also along all the angles weighted by the so-called tail generating measure.
Revised:  The proposed tail dependence measures include tail dependence coefficients as special cases, but capture the extremal relationship between random variables not only along the diagonal but also along all angles weighted by the so-called tail generating measure.
Edit intent: fluency
Original:  The proposed tail dependence measures include tail dependence coefficients as special cases, but capture the extremal relationship between random variables not only along the diagonal but also along all the angles weighted by the so-called tail generating measure.
Revised:  The proposed tail dependence measures include tail dependence coefficients as special cases, but capture the extremal relationship between random variables not only along the diagonal but also along all the angles weighted by a tail generating measure.
Edit intent: clarity
Original:  As a result, the proposed tail dependence measures overcome the issue that the tail dependence coefficients underestimate the extent of extreme co-movements. We also consider the so-called maximal and minimal tail dependence measures , defined as the maximum and minimum of the tail dependence measures among all tail generating measures for a given copula.
Revised:  Axioms of tail dependence measures , defined as the maximum and minimum of the tail dependence measures among all tail generating measures for a given copula.
Edit intent: clarity
Original:  We also consider the so-called maximal and minimal tail dependence measures , defined as the maximum and minimum of the tail dependence measures among all tail generating measures for a given copula. It turns out that the minimal tail dependence measure coincides with the tail dependence coefficient, and the maximal tail dependencemeasure overestimates the degree of extreme co-movements.
Revised:  We also consider the so-called maximal and minimal tail dependence measures , defined as the maximum and minimum of the tail dependence measures . The infimum and supremum of TCMs over all generating measures are considered to investigate the issue of under- and overestimation of the degree of extreme co-movements. The infimum is shown to be attained by the classical tail dependence coefficient, and the maximal tail dependencemeasure overestimates the degree of extreme co-movements.
Edit intent: meaning-changed
Original:  It turns out that the minimal tail dependence measure coincides with the tail dependence coefficient, and the maximal tail dependencemeasure overestimates the degree of extreme co-movements.
Revised:  It turns out that the minimal tail dependence measure coincides with the tail dependence coefficient, and thus the classical notion always underestimates the degree of tail dependence. A formula for the supremum TCM is derived and shown to overestimate the degree of extreme co-movements.
Edit intent: meaning-changed
Original:  For  fair assessment of tail dependence and stability of  estimation under small sample size, we support the use of tail dependence measures weighted over all angles compared with maximal and minimal ones .
Revised:  For fair assessment of tail dependence and stability of estimation under small sample size, we support the use of tail dependence measures weighted over all angles are suggested, with tail Spearman's rho and tail Gini's gamma being interesting novel special cases of TCMs .
Edit intent: meaning-changed
Original:  The key components of Triple M are: 1) A seq2seq model with multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms . Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss or online service modification.
Revised:  The key components of Triple M are: 1) A sequence-to-sequence model adopts a novel multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms . Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss or online service modification.
Edit intent: meaning-changed
Original:  The key components of Triple M are: 1) A seq2seq model with multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms . Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss or online service modification.
Revised:  The key components of Triple M are: 1) A seq2seq model with multi-guidance attention to transfer complementary advantages from guiding attention mechanisms to the basic attention mechanism without in-domain performance loss or online service modification.
Edit intent: meaning-changed
Original:  The key components of Triple M are: 1) A seq2seq model with multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms . Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss or online service modification.
Revised:  The key components of Triple M are: 1) A seq2seq model with multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms . Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss and online service modification.
Edit intent: fluency
Original:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Revised:  Compared with single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Edit intent: coherence
Original:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Revised:  Compared with the our best result obtained by using single attention mechanism , multi-guidance attention not only brings better naturalness to long sentence synthesis, but also reduces the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Edit intent: meaning-changed
Original:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Revised:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate by 26.8\% . 2) A efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Edit intent: meaning-changed
Original:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Revised:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A new efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Edit intent: meaning-changed
Original:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Revised:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A efficient multi-band multi-time vocoder framework , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Edit intent: clarity
Original:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ).
Revised:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A efficient multi-band multi-time LPCNet , which reduces the computational complexity from 2.8 to 1.0 GFLOP ).
Edit intent: clarity
Original:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ). Due to these strategies, the vocoder speed is increased by 2.75x on a single CPU without much MOS degradatiaon (4.57 vs. 4.45) .
Revised:  Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP and speeds up LPCNet by 2.75x on a single CPU without much MOS degradatiaon (4.57 vs. 4.45) .
Edit intent: clarity
Original: The use of Bidirectional Encoder Representations from Transformers (BERT) models for different natural language processing (NLP) tasks, and for sentiment analysis in particular, has become very popular in recent years and not in vain. The use of social media is being constantly on the rise. Its impact on all areas of our lives is almost inconceivable. Researches show that social media nowadays serves as one of the main tools where people freely express their ideas, opinions, and emotions. During the current Covid-19 pandemic, the role of social media as a tool to resonate opinions and emotions, became even more prominent.
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Original:  HeBERT is a transformer-based model for modern Hebrew text . Hebrew is considered a Morphological Rich Language (MRL) , with unique characteristics that pose a great challenge in developing appropriate Hebrew NLP models . Analyzing multiple specifications of the BERT architecture, we come up with a language model that outperforms all existing Hebrew alternatives on multiple language tasks.
Revised:  HeBERT is a transformer-based model for modern Hebrew text , which relies on a BERT (Bidirectional Encoder Representations for Transformers) architecture. BERT has been shown to outperform alternative architectures in sentiment analysis, and is suggested to be particularly appropriate for MRLs . Analyzing multiple specifications of the BERT architecture, we come up with a language model that outperforms all existing Hebrew alternatives on multiple language tasks.
Edit intent: meaning-changed
Original:  Hebrew is considered a Morphological Rich Language (MRL) , with unique characteristics that pose a great challenge in developing appropriate Hebrew NLP models . Analyzing multiple specifications of the BERT architecture, we come up with a language model that outperforms all existing Hebrew alternatives on multiple language tasks.
Revised:  Hebrew is considered a Morphological Rich Language (MRL) , with unique characteristics that pose a great challenge in developing appropriate Hebrew NLP models . Analyzing multiple BERT specifications, we find that while model complexity correlates with high performance on language tasks that aim to understand terms in a sentence, a more-parsimonious model better captures the sentiment of entire sentence. Either way, out BERT-based language model outperforms all existing Hebrew alternatives on multiple language tasks.
Edit intent: meaning-changed
Original:  Hebrew is considered a Morphological Rich Language (MRL) , with unique characteristics that pose a great challenge in developing appropriate Hebrew NLP models . Analyzing multiple specifications of the BERT architecture, we come up with a language model that outperforms all existing Hebrew alternatives on multiple language tasks.
Revised:  Hebrew is considered a Morphological Rich Language (MRL) , with unique characteristics that pose a great challenge in developing appropriate Hebrew NLP models . Analyzing multiple specifications of the BERT architecture, we come up with a language model that outperforms all existing Hebrew alternatives on all common language tasks.
Edit intent: clarity
Original:  HebEMO is a tool that uses HeBERT to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated for this study.
Revised:  HebEMO is a tool that uses HeBERT to detect polarity and extract emotions from Hebrew UGC. HebEMO is trained on a unique Covid-19 related dataset that we collected and annotated for this study.
Edit intent: clarity
Original:  HebEMO is a tool that uses HeBERT to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated for this study.
Revised:  HebEMO is a tool that uses HeBERT to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19-related UGC dataset that we collected and annotated for this study.
Edit intent: fluency
Original:  Emotion detection reached an F1-score of 0.78-0.97  , with the exception of \textit{surprise , which the model failed to capture (F1 = 0.41).
Revised:  Emotion detection reaches F1-scores of 0.78-0.97 , with the exception of \textit{surprise , which the model failed to capture (F1 = 0.41).
Edit intent: clarity
Original:  Emotion detection reached an F1-score of 0.78-0.97  , with the exception of \textit{surprise , which the model failed to capture (F1 = 0.41).
Revised:  Emotion detection reached an F1-score of 0.78-0.97 for various target emotions , with the exception of \textit{surprise , which the model failed to capture (F1 = 0.41).
Edit intent: meaning-changed
Original:  Emotion detection reached an F1-score of 0.78-0.97  , with the exception of \textit{surprise , which the model failed to capture (F1 = 0.41).
Revised:  Emotion detection reached an F1-score of 0.78-0.97 , with the exception of \textit{surprise , which the model failed to capture (F1 = 0.41).
Edit intent: fluency
Original: To develop a high throughput multi-label annotator for body Computed Tomography (CT) reports that can be applied to a variety of URLans, and cases. First, we used a dictionary approach to develop a rule-based algorithm (RBA) for extraction of disease labels from radiology text reports.
Revised: To develop a high throughput multi-label annotator for body Computed Tomography (CT) reports that can be applied to a variety of URLans, and disease states. Approach: We used a dictionary approach to develop a rule-based algorithm (RBA) for extraction of disease labels from radiology text reports.
Edit intent: clarity
Original:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Revised:  To expand the algorithms beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Edit intent: fluency
Original:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Revised:  To expand the algorithm beyond pre-defined keywords, attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Edit intent: fluency
Original:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Revised:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural networks (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Edit intent: fluency
Original:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Revised:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) were trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Edit intent: fluency
Original:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for URLan system.
Revised:  To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for URLan system.
Edit intent: fluency
Original:  However, pre-pandemic experimental studies have produced mixed results regarding their effectiveness against respiratory viruses  , leading to confusion over whether masks protect the wearer, or only those with whom the wearer interacts .
Revised:  However, pre-pandemic experimental studies have produced mixed statistical results on the effectiveness of masks against respiratory viruses , leading to confusion over whether masks protect the wearer, or only those with whom the wearer interacts .
Edit intent: meaning-changed
Original:  However, pre-pandemic experimental studies have produced mixed results regarding their effectiveness against respiratory viruses  , leading to confusion over whether masks protect the wearer, or only those with whom the wearer interacts .
Revised:  However, pre-pandemic experimental studies have produced mixed results regarding their effectiveness against respiratory viruses . The implications of the studies' recognized limitations have not been quantitatively and statistically analyzed , leading to confusion over whether masks protect the wearer, or only those with whom the wearer interacts .
Edit intent: meaning-changed
Original:  Here , we show that studies that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100\% effective, the studies in question would still have been unlikely to find a statistically significant effect.
Revised:  Here we show that studies that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100\% effective, the studies in question would still have been unlikely to find a statistically significant effect.
Edit intent: fluency
Original:  Here , we show that studies that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100\% effective, the studies in question would still have been unlikely to find a statistically significant effect.
Revised:  Here , we show that when the adherence to mask-usage guidelines is taken into account, the empirical evidence indicates that masks prevent disease transmission: all studies we analyzed that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100\% effective, the studies in question would still have been unlikely to find a statistically significant effect.
Edit intent: meaning-changed