wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We model this goal with a regularized edge-based  clustering objective,  design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions .
Revised:  We model this goal with a regularized edge-based clustering objective, and we also show how naive objectives actually have no diversity-experience tradeoff. Although our objective function is NP-hard to optimize, we design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions .
Edit intent: meaning-changed
Original:  We model this goal with a regularized edge-based  clustering objective,  design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions .
Revised:  We model this goal with a regularized edge-based clustering objective, design an efficient 2-approximation algorithm and also show how to compute bounds for the regularization hyperparameter that lead to meaningful diversity-experience tradeoffs .
Edit intent: meaning-changed
Original:  On average, the proposed approach surpasses benchmark procurement strategies and achieves a reduction in costs of 1.65 \% with respect to the perfectly uniform reference procurement strategy achieving the mean electricity price. Moreover, in addition to automating the electricity procurement task, this algorithm demonstrates more consistent results throughout the years compared to the benchmark strategies.
Revised:  On average, the proposed approach surpasses benchmark procurement strategies and achieves a reduction in costs of 1.65 
Edit intent: coherence
Original: Event detection in social media refers to automatic identification of important information shared in social media platforms on a certain time. Considering the dynamic nature and high volume of data production in  data streams, it is impractical to filter the events manually .
Revised: Event detection in social media refers to automatic identification of important information shared in social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in data streams, it is impractical to filter the events manually .
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Original:  Considering the dynamic nature and high volume of data production in  data streams, it is impractical to filter the events manually . Therefore, it is important to have an automated mechanism to detect events in order to utilise social media data effectively. Analysing the available literature, most of the existing event detection methods are only focused on statistical and syntactical features in data , even though the underlying semantics are also important for an effective information retrieval from text , because they describe the connections between words and their meanings.
Revised:  Considering the dynamic nature and high volume of data production in data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data , even though the underlying semantics are also important for an effective information retrieval from text , because they describe the connections between words and their meanings.
Edit intent: clarity
Original:  Analysing the available literature, most of the existing event detection methods are only focused on statistical and syntactical features in data , even though the underlying semantics are also important for an effective information retrieval from text , because they describe the connections between words and their meanings.
Revised:  Analysing the available literature, most of the existing event detection methods are only focused on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text , because they describe the connections between words and their meanings.
Edit intent: coherence
Original:  Analysing the available literature, most of the existing event detection methods are only focused on statistical and syntactical features in data , even though the underlying semantics are also important for an effective information retrieval from text , because they describe the connections between words and their meanings.
Revised:  Analysing the available literature, most of the existing event detection methods are only focused on statistical and syntactical features in data , even though the underlying semantics are also important for an effective information retrieval from text since they represent the connections between words and their meanings.
Edit intent: clarity
Original:  This method is experimented on two recent  social media data sets which represent the sports and politics domains. The results obtained from the experiments reveal that our approach is capable of effective and efficient event detection with the proof of significant improvements over baselines.
Revised:  This method is experimented on two recent social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection with the proof of significant improvements over baselines.
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Original:  The results obtained from the experiments reveal that our approach is capable of effective and efficient event detection with the proof of significant improvements over baselines. For sports data set, Embed2Detect achieved 27\% higher F-measure than the best performed baseline method and for political data set, it was an increase by 29\%.
Revised:  The results obtained from the experiments reveal that our approach is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27\% higher F-measure than the best performed baseline method and for political data set, it was an increase by 29\%.
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Original: We study the problem of few-sample fine-tuning of BERT contextual representations, and identify three sub-optimal choices in current, broadly adopted practices. First, we observe that the omission of the gradient bias correction in the BERTAdam optimizer results in fine-tuning instability. We also find that parts of the BERT network provide a detrimental starting point for fine-tuning, and simply re-initializing these layers speeds up learning and improves performance.
Revised: We study the problem of few-sample fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cause this instability: the common use of a non-standard optimization method with biased gradient estimation; the limited applicability of significant parts of the BERT network provide a detrimental starting point for fine-tuning, and simply re-initializing these layers speeds up learning and improves performance.
Edit intent: clarity
Original:  We also find that parts of the BERT network provide a detrimental starting point for fine-tuning, and simply re-initializing these layers speeds up learning and improves performance. Finally, we study the effect of training time, and observe that commonly used recipes often do not allocate sufficient time for training .
Revised:  We also find that parts of the BERT network for down-stream tasks; and the prevalent practice of using a pre-determined, and small number of training iterations. We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process .
Edit intent: coherence
Original: This paper uses a mathematical model with appropriate assumptions, to model and to determine the optimal group size for microfinance loans.
Revised: Microlending, where a bank lends to a small group of people without credit histories, began with the Grameen Bank in Bangladesh, and is widely seen as the creation of Muhammad Yunus, who received the Nobel Peace Prize in recognition of his largely successful efforts. Since that time the model of microlending has received a fair amount of academic attention. One of the issues not yet addressed in detail, however, is the issue of the size of the group. (Some attention has nevertheless been paid, see the appropriate references in this paper.) Instead of a game theory approach, we take a mathematical approach to the issue of an optimal group size for microfinance loans.
Edit intent: meaning-changed
Original:  An optimal size is defined to be the size which maximizes the probability of no default of the group 
Revised:  An optimal size is defined to be the size which maximizes the probability of no default of the group . To do this, one has to create a model with interacting forces, and to make precise the hypotheses of the model. (In previous work we have addressed the issue of what is a fair rate of interest to charge for such a loan.) We show that the original choice of Muhammad Yunus, of a group size of five people, is, under the right (and, we believe, reasonable) hypotheses, either close to optimal, or even at times exactly optimal, i.e., the optimal group size is indeed five people.
Edit intent: meaning-changed
Original:  ( Some attention has nevertheless been paid , see the appropriate references in this paper.) Instead of a game theory approach , we take a mathematical approach to the issue of an optimal group size, where the goal is to minimize the probability of default of the group.
Revised:  ( Some attention has nevertheless been paid using an experimental and game theory approach , we take a mathematical approach to the issue of an optimal group size, where the goal is to minimize the probability of default of the group.
Edit intent: meaning-changed
Original:  We show that the original choice of Muhammad Yunus, of a group size of five people, is, under the right ( and, we believe, reasonable ) hypotheses, either close to optimal, or even at times exactly optimal, i.e., the optimal group size is indeed five people.
Revised:  We show that the original choice of Muhammad Yunus, of a group size of five people, is, under the right , and, we believe, reasonable ) hypotheses, either close to optimal, or even at times exactly optimal, i.e., the optimal group size is indeed five people.
Edit intent: fluency
Original:  We show that the original choice of Muhammad Yunus, of a group size of five people, is, under the right ( and, we believe, reasonable ) hypotheses, either close to optimal, or even at times exactly optimal, i.e., the optimal group size is indeed five people.
Revised:  We show that the original choice of Muhammad Yunus, of a group size of five people, is, under the right ( and, we believe, reasonable hypotheses, either close to optimal, or even at times exactly optimal, i.e., the optimal group size is indeed five people.
Edit intent: fluency
Original:  This methodology is primarily based on the approach introduced by Drori and Teboulle ( Mathematical Programming, 2014) and on convex interpolation results, and allows producing non-improvable worst-case analyzes.
Revised:  This methodology is primarily based on the approach introduced by Drori and Teboulle ( 2014) and on convex interpolation results, and allows producing non-improvable worst-case analyzes.
Edit intent: clarity
Original:  Relying on this methodology, we provide three new methods with conceptually simple proofs: (i) an optimized relatively inexact proximal point method, (ii) an extension of the hybrid proximal extragradient method of Monteiro and Svaiter (SIAM Journal on Optimization, 2013), and (iii) an inexact accelerated forward-backward splitting supporting backtracking line-search, and both (ii) and (iii) supporting possibly strongly convex objectives .
Revised:  Relying on this methodology, we study numerical worst-case performances of a few basic methods relying on inexact proximal operations including accelerated variants, and design a variant with optimized worst-case behaviour. We further illustrate how to extend the approach to support strongly convex objectives .
Edit intent: clarity
Original:  Relying on this methodology, we provide three new methods with conceptually simple proofs: (i) an optimized relatively inexact proximal point method, (ii) an extension of the hybrid proximal extragradient method of Monteiro and Svaiter (SIAM Journal on Optimization, 2013), and (iii) an inexact accelerated forward-backward splitting supporting backtracking line-search, and both (ii) and (iii) supporting possibly strongly convex objectives . Finally, we use the methodology for studying a recent inexact variant of the Douglas-Rachford splitting due to Eckstein and Yao (Mathematical Programming, 2018). We showcase and compare the different variants of the accelerated inexact forward-backward methodon a factorization and a total variation problem .
Revised:  Relying on this methodology, we provide three new methods with conceptually simple proofs: (i) an optimized relatively inexact proximal point method, (ii) an extension of the hybrid proximal extragradient method of Monteiro and Svaiter (SIAM Journal on Optimization, 2013), and (iii) an inexact accelerated forward-backward splitting supporting backtracking line-search, and both (ii) and (iii) supporting possibly strongly convex objectives by studying a simple relatively inexact proximal minimization method .
Edit intent: clarity
Original:  It is possible to switch to block values, or use a mixed precision solution, which results in up to 4 times speedup, and reduces the memory footprint of the algorithm by about 40\\%.
Revised:  It is possible to switch the system matrix to use small statically sized blocks to store the nonzero values, or use a mixed precision solution, which results in up to 4 times speedup, and reduces the memory footprint of the algorithm by about 40\\%.
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Original:  Our results  imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, answering a question of Bresar, Hartinger, Kos  and Milanic from 2018.
Revised:  Our results yield an infinite family of \chi-bounded induced-minor-closed graph classes and imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, answering a question of Bresar, Hartinger, Kos and Milanic from 2018.
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Original:  Our results  imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, answering a question of Bresar, Hartinger, Kos  and Milanic from 2018.
Revised:  Our results imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, leading to linear-time algorithms for k-coloring 1-perfectly orientable graphs for every fixed k. This answers a question of Bresar, Hartinger, Kos and Milanic from 2018.
Edit intent: meaning-changed
Original:  Our results  imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, answering a question of Bresar, Hartinger, Kos  and Milanic from 2018.
Revised:  Our results imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, answering a question of Bresar, Hartinger, Kos , and Milanic from 2018.
Edit intent: fluency
Original:  Our results  imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, answering a question of Bresar, Hartinger, Kos  and Milanic from 2018.
Revised:  Our results imply that the class of 1-perfectly orientable graphs is (tw,\omega)-bounded, answering a question of Bresar, Hartinger, Kos and Milanic from 2018, and one of Beisegel, Chudnovsky, Gurvich, Milani\v{c
Edit intent: meaning-changed
Original:  We also reveal some further algorithmic implications of (tw,\omega)-boundedness related to list k-coloring and clique problems  .
Revised:  We also reveal some further algorithmic implications of (tw,\omega)-boundedness related to list k-coloring and clique problems . In addition, we propose a question about the complexity of the Maximum Weight Independent Set problem in (tw,\omega)-bounded graph classes and prove that the problem is polynomial-time solvable in every class of graphs excluding a fixed star as an induced minor .
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Original:  In the present study we investigate how common a neuroreductionist inclination is in the popular mind.
Revised:  In the present study we investigate how common a neuroreductionist inclination is among highly educated people. In particular, we shed light on implicit presuppositions of mental disorders little is known about in the public, exemplified here by the case of Body Integrity Dysphoria (BID) that is considered a mental disorder for the first time in ICD-11. Methods.
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Original:  738 highly-educated laymen rated plausibility of causality attribution from brain to mind and from mind to brain for correlations between brain structural properties and mental phenomena in the context of commonly perceived brain-lesion induced behavior (aphasia), behavior-induced training effects (piano playing), and a newly described mental disorder ( Body Integrity Dysphoria).
Revised:  738 highly-educated laymen rated plausibility of causality attribution from brain to mind and from mind to brain for correlations between brain structural properties and mental phenomena . We contrasted participants' plausibility ratings of causality in the contexts of commonly perceived brain-lesion induced behavior (aphasia), behavior-induced training effects (piano playing), and a newly described mental disorder ( Body Integrity Dysphoria).
Edit intent: clarity
Original:  738 highly-educated laymen rated plausibility of causality attribution from brain to mind and from mind to brain for correlations between brain structural properties and mental phenomena in the context of commonly perceived brain-lesion induced behavior (aphasia), behavior-induced training effects (piano playing), and a newly described mental disorder ( Body Integrity Dysphoria).
Revised:  738 highly-educated laymen rated plausibility of causality attribution from brain to mind and from mind to brain for correlations between brain structural properties and mental phenomena in the context of commonly perceived brain-lesion induced behavior (aphasia), behavior-induced training effects (piano playing), and a newly described mental disorder ( BID). Results.
Edit intent: clarity
Original:  The findings reveal the expected context-dependent modulation , but also a general tendency to more readily attribute causal inference from brain to mind than vice versa . We argue that this tendency has important consequences, especially in the context of Body Integrity Dysphoria, a condition where little is known about underlying causal mechanisms .
Revised:  The findings reveal the expected context-dependent modulation , but also a general tendency to more readily attribute causal inference from brain to mind than vice versa with respect to BID. Conclusion. In some contexts, exemplified here by aphasia and piano playing, unidirectional causality attributions may be justified. However, with respect to BID, we critically discuss presumably unjustified neuroreductionist inclinations under causal uncertainty. Finally, we emphasize the need for a presupposition-free approach in psychiatry .
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Original:  There is currently a disconnect between the practitioners of online social media analysis - usually social, political and communication scientists - and the accessibility to tools capable of handling large quantities of online data, and examining online users and their behavior.
Revised:  There is currently a disconnect between the practitioners of online social media analysis -- usually social, political and communication scientists - and the accessibility to tools capable of handling large quantities of online data, and examining online users and their behavior.
Edit intent: fluency
Original:  There is currently a disconnect between the practitioners of online social media analysis - usually social, political and communication scientists - and the accessibility to tools capable of handling large quantities of online data, and examining online users and their behavior.
Revised:  There is currently a disconnect between the practitioners of online social media analysis - usually social, political and communication scientists -- and the accessibility to tools capable of handling large quantities of online data, and examining online users and their behavior.
Edit intent: fluency
Original:  There is currently a disconnect between the practitioners of online social media analysis - usually social, political and communication scientists - and the accessibility to tools capable of handling large quantities of online data, and examining online users and their behavior. We present two tools,birdspotter and evently, for analyzing online users based on their involvement in retweet cascades . birdspotter provides a toolkit to measure social influence and botnets of Twitter users.
Revised:  There is currently a disconnect between the practitioners of online social media analysis - usually social, political and communication scientists - and the accessibility to tools capable of examining online discussions of users . birdspotter provides a toolkit to measure social influence and botnets of Twitter users.
Edit intent: clarity
Original:  We present two tools,birdspotter and evently, for analyzing online users based on their involvement in retweet cascades . birdspotter provides a toolkit to measure social influence and botnets of Twitter users. While it leverages the multimodal information of tweets, such as text contents, evently augments the user measurement by modeling the temporal dynamics of information diffusions using self-exciting processes.
Revised:  We present two tools,birdspotter and evently, for analyzing online users based on their involvement in retweet cascades . Here we present evently, a tool for modeling online reshare cascades, and particularly retweet cascades, using self-exciting processes. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs, modeling the temporal dynamics of information diffusions using self-exciting processes.
Edit intent: meaning-changed
Original:  While it leverages the multimodal information of tweets, such as text contents, evently augments the user measurement by modeling the temporal dynamics of information diffusions using self-exciting processes. Both tools are designed for users with a wide range of computer expertise and include tutorials and detailed documentation.
Revised:  While it leverages the multimodal information of tweets, such as text contents, evently augments the user measurement by modeling the temporal dynamics of processed retweet cascades and characterizing online users with a wide range of diffusion measures. This tool is designed for researchers with a wide range of computer expertise and include tutorials and detailed documentation.
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Original: We present a general framework for analyzing high-probability bounds for stochastic dynamics in learning algorithms.
Revised: One of the challenges in analyzing a learning algorithm is the circular entanglement between the objective value and the stochastic noise. This is also known as the "chicken and egg" phenomenon. Traditionally, people tackle this issue with the special structure of the problem and hence the analysis is difficult to generalize. In this paper, we present a general framework for analyzing high-probability bounds for stochastic dynamics in learning algorithms.
Edit intent: meaning-changed