wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices ,  achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time.
Revised:  This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices , achieves comparable results to several state-of-the-art algorithms in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computational time.
Edit intent: meaning-changed
Original:  This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices ,  achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time. Finally, theoretical results concerning the convergence of the training are derived .
Revised:  This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices , achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computation time .
Edit intent: coherence
Original:  The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, the algorithm must re-run each time a new matrixshould be decomposed . Since these algorithms are computationally expensive, it is preferable to learn and store a function that instantaneously performs this decomposition when evaluated.
Revised:  The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, those algorithms must re-run each time a new matrixshould be decomposed . Since these algorithms are computationally expensive, it is preferable to learn and store a function that instantaneously performs this decomposition when evaluated.
Edit intent: clarity
Original:  The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, the algorithm must re-run each time a new matrixshould be decomposed . Since these algorithms are computationally expensive, it is preferable to learn and store a function that instantaneously performs this decomposition when evaluated.
Revised:  The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, the algorithm must re-run for every new matrix . Since these algorithms are computationally expensive, it is preferable to learn and store a function that instantaneously performs this decomposition when evaluated.
Edit intent: clarity
Original:  Theoretical guarantees that Denise 's architecture can approximate the decomposition function, to arbitrary precision and with arbitrarily high probability, are obtained. The training scheme is also shown to convergence to a stationary point of the robust PCA's loss-function. We train Denise on a randomly generated dataset, and evaluate the performance of the DNN on synthetic and real-world covariance matrices. Denise achieves comparable results to several state-of-the-art algorithms in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computation time .
Revised:  Theoretical guarantees for Denise are provided. These include a novel universal approximation theorem adapted to our geometric deep learning problem, convergence to an optimal solution of the learning problem and convergence of the training scheme. Our experiments show that Denise matches state-of-the-art algorithms in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computation time .
Edit intent: clarity
Original:  Denise achieves comparable results to several state-of-the-art algorithms in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computation time .
Revised:  Denise achieves comparable results to several state-of-the-art performance in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computation time .
Edit intent: clarity
Original:  Denise achieves comparable results to several state-of-the-art algorithms in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computation time .
Revised:  Denise achieves comparable results to several state-of-the-art algorithms in terms of decomposition quality, while being approximately 2000x faster than the state-of-the-art, PCP, and 200x faster than the current speed optimized method, fast PCP .
Edit intent: meaning-changed
Original:  Recent studies reveal that jointly modeling of these two graphs could improve the understanding of each one. The completion processes on the concept graph and the entity graph can be further regarded as processes of knowledge abstractionand concretization. However, concept graphs in existing datasets are usually small and the links between concepts and entities are usually sparse, which cannot provide sufficient information for knowledge transfer between the two graphs. In this paper ,  we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links  . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Revised:  The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization , we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Edit intent: clarity
Original:  In this paper ,  we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links  . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Revised:  In this paper , and completion (KACC), which are crucial for human to recognize the world and manage learned knowledge. Existing studies mainly focus on partial aspects of KACC. In order to promote thorough analyses for KACC abilities of models, we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Edit intent: meaning-changed
Original:  In this paper ,  we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links  . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Revised:  In this paper , we propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty. Specifically, we collect new datasets that contain larger concept graphs, abundant cross-view links . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Edit intent: meaning-changed
Original:  In this paper ,  we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links  . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Revised:  In this paper , we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links as well as dense entity graphs . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Edit intent: meaning-changed
Original:  In this paper ,  we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links  . Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction , concretization and completion (KACC) . Our dataset is available at URL
Revised:  In this paper , we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphsand abundant cross-view links . Based on the datasets, we propose novel tasks such as multi-hop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark. For MKA and MKC tasks, we further annotate multi-hop hierarchical triples as harder samples. The experimental results of existing methods demonstrate the challenges of our benchmark. The resource is available at URL
Edit intent: meaning-changed
Original: This paper modifies single assumption in the base of classical option pricing model and derives further extensions for the Black-Scholes-Merton equation. We regard the price as the ratio of the cost and the volume of market transaction and apply classical assumptions on stochastic Brownian motion not to the price but to the cost and the volume.
Revised: This paper considers the asset price p as relations C=pV between the value C and the volume of market transaction and apply classical assumptions on stochastic Brownian motion not to the price but to the cost and the volume.
Edit intent: meaning-changed
Original:  We regard the price as the ratio of the cost and the volume of market transaction and apply classical assumptions on stochastic Brownian motion not to the price but to the cost and the volume. This simple replacement leads to 2-dimensional BSM-like equation with two constant volatilities.
Revised:  We regard the price as the ratio of the cost and the volume V of the executed transactions and studies the consequences of this definition for the option pricing equations. We show that the classical BSM model implicitly assumes that value C and volume V of transactions follow identical Brownian processes. Violation of this identity leads to 2-dimensional BSM-like equation with two constant volatilities.
Edit intent: meaning-changed
Original:  We argue that decisions on the cost and the volume of market transactions are made under agents expectations . Random perturbations of expectations impact the market transactions and through them induce stochastic behavior of the underlying price. We derive BSM-like equation driven by Brownian motion of agents expectations . Agents expectations can be based on option trading data . We show how such expectations  can lead to  nonlinear BSM-like equations.
Revised:  We show that agents expectations can lead to nonlinear BSM-like equations.
Edit intent: coherence
Original:  Further we show that the Heston stochastic volatility option pricing model can be applied to our approximations and as example derive 3-dimensional BSM-like equation that describes option pricing with stochastic cost volatility and constant volume volatility.
Revised:  We reconsider the Heston stochastic volatility option pricing model can be applied to our approximations and as example derive 3-dimensional BSM-like equation that describes option pricing with stochastic cost volatility and constant volume volatility.
Edit intent: coherence
Original:  Further we show that the Heston stochastic volatility option pricing model can be applied to our approximations and as example derive 3-dimensional BSM-like equation that describes option pricing with stochastic cost volatility and constant volume volatility.
Revised:  Further we show that the Heston stochastic volatility model for the price determined by the value and the volume and derive 3-dimensional BSM-like equation that describes option pricing with stochastic cost volatility and constant volume volatility.
Edit intent: meaning-changed
Original:  Further we show that the Heston stochastic volatility option pricing model can be applied to our approximations and as example derive 3-dimensional BSM-like equation that describes option pricing with stochastic cost volatility and constant volume volatility.
Revised:  Further we show that the Heston stochastic volatility option pricing model can be applied to our approximations and as example derive 3-dimensional BSM-like model with stochastic value volatility and constant volume volatility.
Edit intent: clarity
Original: Several countries  have been successful in constraining the severity of COVID-19 pandemic via "lockdown" , but itcannot be used as a longtime measure because of enormous socio-economic side-effects.
Revised: Several countries that have been successful in constraining the severity of COVID-19 pandemic via "lockdown" , but itcannot be used as a longtime measure because of enormous socio-economic side-effects.
Edit intent: fluency
Original: Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and various variants have been proposed to further improve the performance of the pre-trained  models.
Revised: Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained models.
Edit intent: clarity
Original: Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and various variants have been proposed to further improve the performance of the pre-trained  models.
Revised: Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and various variants have been proposed to further improve the performance of the pre-trained language models.
Edit intent: meaning-changed
Original:  The goal of ConronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity  and to model the human emotions during the COVID-19 pandemic.
Revised: Due to the nature of the data and public interaction, twitter is becoming more and more useful to understand and model various events. The goal of ConronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity and to model the human emotions during the COVID-19 pandemic.
Edit intent: meaning-changed
Original:  The goal of ConronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity  and to model the human emotions during the COVID-19 pandemic.
Revised:  The goal of CoronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity and to model the human emotions during the COVID-19 pandemic.
Edit intent: fluency
Original:  The goal of ConronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity  and to model the human emotions during the COVID-19 pandemic.
Revised:  The goal of ConronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity , and to model the human emotions during the COVID-19 pandemic.
Edit intent: fluency
Original:  In this  paper, we describe the CoronaVis Twitter dataset (focused on the United States) that we have been collecting from early March 2020.
Revised:  In this work, we develop a live application to observe the tweets on COVID-19 generated from the USA. In this paper, we describe the CoronaVis Twitter dataset (focused on the United States) that we have been collecting from early March 2020.
Edit intent: meaning-changed
Original:  In this  paper, we describe the CoronaVis Twitter dataset (focused on the United States) that we have been collecting from early March 2020.
Revised:  In this paper, we have generated various data analytics over a period of time to study the changes in topics, subjectivity, and human emotions. We also share a cleaned and processed dataset named CoronaVis Twitter dataset (focused on the United States) that we have been collecting from early March 2020.
Edit intent: meaning-changed
Original:  In this  paper, we describe the CoronaVis Twitter dataset (focused on the United States) that we have been collecting from early March 2020. The dataset is available to the research community at URL We would like to share this data with the hope that it will enable the community to find out more useful insights and create different applications and models to fight with COVID-19 pandemic and the future pandemics as well.
Revised:  In this paper, we describe the CoronaVis Twitter dataset (focused on the United States) available to the research community at URL We would like to share this data with the hope that it will enable the community to find out more useful insights and create different applications and models to fight with COVID-19 pandemic and the future pandemics as well.
Edit intent: coherence
Original:  The dataset is available to the research community at URL We would like to share this data with the hope that it will enable the community to find out more useful insights and create different applications and models to fight with COVID-19 pandemic and the future pandemics as well.
Revised:  The dataset is available to the research community at URL This will enable the community to find out more useful insights and create different applications and models to fight with COVID-19 pandemic and the future pandemics as well.
Edit intent: coherence
Original:  The dataset is available to the research community at URL We would like to share this data with the hope that it will enable the community to find out more useful insights and create different applications and models to fight with COVID-19 pandemic and the future pandemics as well.
Revised:  The dataset is available to the research community at URL We would like to share this data with the hope that it will enable the community to find more useful insights and create different applications and models to fight with COVID-19 pandemic and the future pandemics as well.
Edit intent: coherence
Original:  The dataset is available to the research community at URL We would like to share this data with the hope that it will enable the community to find out more useful insights and create different applications and models to fight with COVID-19 pandemic and the future pandemics as well.
Revised:  The dataset is available to the research community at URL We would like to share this data with the hope that it will enable the community to find out more useful insights and create different applications and models to fight with COVID-19 pandemic and future pandemics as well.
Edit intent: fluency
Original:  However, the effective representations offered by the models target at each token inside a sequence rather than each sequence and the fine-tuning step involves the input of both sequences at one time, leading to unsatisfying representation of each individual sequence. Besides , as sentence-level representations taken as the full training context in these models, there comes inferior performance on lower-level linguistic units (phrases and words).
Revised:  However, the effective representations offered by the models target at each token inside a sequence rather than each sequence and the fine-tuning step involves the input of both sequences at one time, leading to unsatisfying representations of various sequences with different granularities. Especially , as sentence-level representations taken as the full training context in these models, there comes inferior performance on lower-level linguistic units (phrases and words).
Edit intent: clarity
Original:  In this work, we present a novel framework on BERT that is capable of generating universal, fixed-size representations for input sequences of any lengths , i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives.
Revised:  In this work, we present BURT (BERT inspired Universal Representation from Twin Structure) that is capable of generating universal, fixed-size representations for input sequences of any lengths , i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives.
Edit intent: meaning-changed
Original:  In this work, we present a novel framework on BERT that is capable of generating universal, fixed-size representations for input sequences of any lengths , i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives.
Revised:  In this work, we present a novel framework on BERT that is capable of generating universal, fixed-size representations for input sequences of any granularity , i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives.
Edit intent: clarity
Original:  We evaluate our model across different granularity of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where our model substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word-level and phrase-level representation.
Revised:  We evaluate BURT across different granularities of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where our model substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word-level and phrase-level representation.
Edit intent: meaning-changed
Original:  We evaluate our model across different granularity of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where our model substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word-level and phrase-level representation.
Revised:  We evaluate our model across different granularity of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where BURT substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word-level and phrase-level representation.
Edit intent: meaning-changed
Original:  We evaluate our model across different granularity of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where our model substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word-level and phrase-level representation.
Revised:  We evaluate our model across different granularity of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where our model substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word/ phrase-level representation.
Edit intent: clarity