wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Schema2QA has several advantages: (1) Schema2QA handles compositional queries involving multiple fields automatically, such as "find the Italian restaurant around here with the most reviews", or "what W3C employees on LinkedIn went to Oxford" ;
Revised:  Schema2QA has several advantages: (1) Schema2QA is more accurate than commercial assistants in answering compositional queries involving multiple fields automatically, such as "find the Italian restaurant around here with the most reviews", or "what W3C employees on LinkedIn went to Oxford" ;
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Original:  (2)  Schema2QA translates natural language into executable queries on the up-to-date data from the website; (3) natural language training can be applied to one domain at a time to handle multiple websites using the same URL representations .
Revised:  (2) Schema2QA uses a novel neural model that combines the BERT pretrained model with an LSTM; the model can generalize a training set containing only synthesized and paraphrase data to understand real-world crowdsourced questions .
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Original:  This paper proposes Schema2QA, an open-source toolkit that leverages the URL markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schema2QA is more accurate than commercial assistants in answering compositional queries involving multiple properties; (2) it has a low-cost training data acquisition methodology that requires only writing a small number of annotations per domain and paraphrasing a small number of sentences. Schema2QA uses  a  novel neural model that combines the BERT pretrained model with an LSTM;
Revised:  This paper proposes Schema2QA, an open-source toolkit that can build a Q a novel neural model that combines the BERT pretrained model with an LSTM;
Edit intent: clarity
Original:  Schema2QA uses  a  novel neural model that combines the BERT pretrained model with an LSTM;
Revised:  Schema2QA uses A skill from a database schema, requiring just a few manual annotations on each field. The key concept is to cover the space of possible compound queries on the database with millions of domain-specific questions synthesized with the help of a corpus of generic query templates. The synthesized data and a novel neural model that combines the BERT pretrained model with an LSTM;
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Original:  Our skills achieve an overall accuracy between 73\% and 81\% . With transfer learning , we show that a new domain can achieve a 59\% accuracy without manual effort.
Revised:  Our skills achieve an overall accuracy between 74\% and 80\% on crowdsourced questions for the domains. Any websites with URL metadata in these domains can use Schema2QA to build their website-specific skill automatically. We show that with transfer learning from the restaurant to the hotel domain, we can achieve a 59\% accuracy without manual effort.
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Original:  We apply Schema2QA to two different  URL domains, restaurants and people, and show that the skills we built can answer complex queries accurately. Our skills achieve an overall accuracy between 74\% and 78 \% on crowdsourced questions for the domains.
Revised:  We apply Schema2QA to two different URL domains, restaurants , people, movies, books and music, and obtain an overall accuracy between 74\% and 78 \% on crowdsourced questions for the domains.
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Original:  Our skills achieve an overall accuracy between 74\% and 78 \% on crowdsourced questions for the domains. Any websites with URL metadata in these domains can use Schema2QA to build their website-specific skill automatically. We show that with transfer learning  from the restaurant to the hotel domain, we can achieve a 64\% accuracy on crowdsourced questions with no manual effort.
Revised:  Our skills achieve an overall accuracy between 74\% and 78 \% on crowdsourced questions for these domains. Once annotations and paraphrases are obtained for a URL schema, no additional manual effort is needed to create a Q from the restaurant to the hotel domain, we can achieve a 64\% accuracy on crowdsourced questions with no manual effort.
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Original:  We show that with transfer learning  from the restaurant to the hotel domain, we can achieve a 64\% accuracy on crowdsourced questions with no manual effort.
Revised:  We show that with transfer learning A agent for any website that uses the same schema. Furthermore, we demonstrate that learning can be transferred from the restaurant to the hotel domain, we can achieve a 64\% accuracy on crowdsourced questions with no manual effort.
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Original:  On the restaurant domain, Schema2QA outperforms the best commercial assistant by 21\% on a set of diverse crowdsourced questions, while achieving equally good accuracy on questionscommonly asked to existing virtual assistants .
Revised:  Schema2QA achieves an accuracy of 60\% on popular restaurant questions that can be answered using URL. Its performance is comparable to Google Assistant, 7\% lower than Siri, and 15\% higher than Alexa. It outperforms all these assistants by at least 18\% on more complex, long-tail questions .
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Original:  It is demonstrated in the experiments on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets that our model has achieved the best results evaluated by BLEU, CIDEr, METEOR and ROUGE-L metrics with significant gains of up to 11.7 \% on MSVD and 5 \% on MSR-VTT compared with the previous state-of-the-art models.
Revised:  It is demonstrated in the experiments on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets that our model has achieved the best results evaluated by BLEU, CIDEr, METEOR and ROUGE-L metrics with significant gains of up to 18 \% on MSVD and 5 \% on MSR-VTT compared with the previous state-of-the-art models.
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Original:  It is demonstrated in the experiments on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets that our model has achieved the best results evaluated by BLEU, CIDEr, METEOR and ROUGE-L metrics with significant gains of up to 11.7 \% on MSVD and 5 \% on MSR-VTT compared with the previous state-of-the-art models.
Revised:  It is demonstrated in the experiments on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets that our model has achieved the best results evaluated by BLEU, CIDEr, METEOR and ROUGE-L metrics with significant gains of up to 11.7 \% on MSVD and 3.5 \% on MSR-VTT compared with the previous state-of-the-art models.
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Original:  OralCam allows a user to annotate additional information (e.g. living habits, pain, and bleeding) to augment the input image, and presents the output hierarchically, probabilistically and with visual explanations to help a laymen user understand examination results.
Revised:  OralCam allows a user to annotate additional information (e.g. living habits, pain, and bleeding) to augment the input image, and presents the output hierarchically, probabilistically and with visual explanations to help a laymen user understand examination results.
Edit intent: fluency
Original:  Herein, we show that excitation-inhibition (E-I) balanced network with synaptic kinetics can maintain irregular spiking dynamics with different levels of synchrony and critical avalanches emerge near the synchronous transition point.
Revised:  Herein, we study excitation-inhibition (E-I) balanced network with synaptic kinetics can maintain irregular spiking dynamics with different levels of synchrony and critical avalanches emerge near the synchronous transition point.
Edit intent: clarity
Original:  Herein, we show that excitation-inhibition (E-I) balanced network with synaptic kinetics can maintain irregular spiking dynamics with different levels of synchrony and critical avalanches emerge near the synchronous transition point.
Revised:  Herein, we show that excitation-inhibition (E-I) balanced neuronal network with biologically realistic synaptic kinetics. It can maintain irregular spiking dynamics with different levels of synchrony and critical avalanches emerge near the synchronous transition point.
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Original:  It also provides a theoretical framework for analyzing the macroscopic dynamics of E-I balanced networks and its relationship to the microscopic counterparts, which can be useful for large-scale modeling and computation of cortical dynamics.
Revised:  It also provides a novel tool for analyzing the macroscopic dynamics of E-I balanced networks and its relationship to the microscopic counterparts, which can be useful for large-scale modeling and computation of cortical dynamics.
Edit intent: clarity
Original: Machine Reading Comprehension (MRC) is the task of natural languageprocessing that studies the ability to read and understand unstructured texts and then find the correct answers for questions. Until now, we have not yet had any MRC dataset for such a low-resource language as Vietnamese. In this paper, we introduce ViMMRC, a challenging machine comprehension corpus with multiple-choice questions, intended for research on the machine comprehension of Vietnamesetext. This corpus includes 2,783  multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.
Revised: Although over 95 million people worldwide speak the Vietnamese language, limited study and efforts have been made to carry out machine reading comprehension and create language resources for the Vietnamese language. This article proposes the lexical-based reading comprehension approach utilizing semantic similarity measurement and external knowledge sources to analyze questions and extract answers from reading texts in Vietnamese. This method is evaluated on our proposed dataset including 2,783 multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.
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Original:  Answers may be extracted from the contents of single or multiple sentences in the corresponding reading text. A thorough analysis of the corpus and experimental results in this paper illustrate that our corpus ViMMRC demands reasoning abilities beyond simple word matching. We proposed the method of Boosted Sliding Window (BSW) that improves 5.51\% in accuracy over the best baseline method.
Revised:  The objectives of this research are two main contributions: (1) to develop a human-generated benchmark dataset for the low-resourced Vietnamese language for the machine reading comprehension; and (2) to evaluate machine reading comprehension techniques using lexical-based approaches, neural-based approaches, and our proposed method. Finally, the results of our proposed model are analyzed by comparing it with the outcome of the lexical-based and neural-based approaches. Our experiments show that our proposed method outperforms baseline models in terms of accuracy of 61.81\%, higher 5.51\% in accuracy over the best baseline method.
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Original:  We proposed the method of Boosted Sliding Window (BSW) that improves 5.51\% in accuracy over the best baseline method. We also measured human performance on the corpus and compared it to our MRC models.
Revised:  We proposed the method of Boosted Sliding Window (BSW) that improves 5.51\% in accuracy over the best baseline model. Besides, we measure human performance on the corpus and compared it to our MRC models.
Edit intent: coherence
Original: Although over 95 million people worldwide speak the Vietnamese language, limited study and efforts have been made to carry out machine reading comprehension and create language resources for the Vietnamese language. This article proposes the lexical-based reading comprehension approach utilizing semantic similarity measurement and external knowledge sources to analyze questions and extract answers from reading texts in Vietnamese. This method is evaluated on our proposed dataset including 2,783 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.
Revised: Although over 95 million people worldwide speak the Vietnamese language, limited study and efforts have been made to carry out machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 417 Vietnamese texts and 2,783 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.
Edit intent: clarity
Original:  This method is evaluated on our proposed dataset including 2,783 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.
Revised:  This method is evaluated on our proposed dataset including 2,783 pairs of multiple-choice questions and answers . The texts are commonly used for teaching reading comprehension for 1st to 5th graders.
Edit intent: clarity
Original:  This method is evaluated on our proposed dataset including 2,783 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders. The objectives of this research are two main contributions: (1) to develop a human-generated benchmark dataset for the low-resourced Vietnamese language for the machine reading comprehension; and (2) to evaluate machine reading comprehension techniques using lexical-based approaches, neural-based approaches, and our proposed method.
Revised:  This method is evaluated on our proposed dataset including 2,783 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexical-based approaches, neural-based approaches, and our proposed method.
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Original:  and (2) to evaluate machine reading comprehension techniques using lexical-based approaches, neural-based approaches, and our proposed method. Finally, the results of our proposed model are analyzed by comparing it with the outcome of the lexical-based and neural-based approaches.
Revised:  and (2) to evaluate machine reading comprehension techniques using lexical-based MRC technique that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several lexical-based and neural-based approaches.
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Original:  Finally, the results of our proposed model are analyzed by comparing it with the outcome of the lexical-based and neural-based approaches. Our experiments show that our proposed method outperforms baseline modelsin terms of accuracy of 61.81 \%, higher 5.51\% than the best baseline model.
Revised:  Finally, the results of our proposed model are analyzed by comparing it with the outcome of the lexical-based and neural network-based baseline models. Our proposed technique achieves 61.81 \%, higher 5.51\% than the best baseline model.
Edit intent: clarity
Original:  Our experiments show that our proposed method outperforms baseline modelsin terms of accuracy of 61.81 \%, higher 5.51\% than the best baseline model. Besides, we measure human performance on our dataset and compared it to our MRC models. The performance gap between humans and our best experimental model indicates that significant progress can be made on Vietnamese machine reading comprehension in further research. Our dataset is freely available for research purposes.
Revised:  Our experiments show that our proposed method outperforms baseline modelsin terms of accuracy of 61.81 
Edit intent: coherence
Original: Although over 95 million people worldwide speak the Vietnamese language , there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it.
Revised: Although Vietnamese is the 17th most popular native-speaker language in the world , there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it.
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Original:  In this work, we construct a dataset which consists of 417 Vietnamese texts and 2,783 pairs of multiple-choice questions and answers . The texts are commonly used for teaching reading comprehension for elementary school pupils.
Revised:  In this work, we construct a dataset which consists of 417 Vietnamese texts and 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils.
Edit intent: clarity
Original:  The dataset is freely available at our website for research purposes.abstract 
Revised:  The dataset is freely available at our website for research purposes.abstract \% by accuracy, which is 5.51\% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website for research purposes.
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Original:  Our algorithm combines the free energy principle from neuroscience, rooted in variational inference, with deep convolutional decoders to scale the algorithm to directly deal with images input and provide online adaptive inference.
Revised:  Our algorithm combines the free-energy principle from neuroscience, rooted in variational inference, with deep convolutional decoders to scale the algorithm to directly deal with images input and provide online adaptive inference.
Edit intent: fluency
Original:  The approach enables the robot to perform 1) dynamical body estimation of  arm using only raw monocular camera images and 2) autonomous reaching to "imagined" arm poses in the visual space.
Revised:  Our approach is validated by studying body perception and action in a simulated and a real Nao robot. Results show that our approach allows the robot to perform 1) dynamical body estimation of arm using only raw monocular camera images and 2) autonomous reaching to "imagined" arm poses in the visual space.
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Original: Hidden community is a new graph-theoretical concept recently proposed [4], in which the authors also propose a meta-approach called HICODE (Hidden Community Detection)  . HICODE is demonstrated through experiments that it is able to uncover previously overshadowed weak layers and uncover both weak and strong layers at a higher accuracy.
Revised: Hidden community is a new graph-theoretical concept recently proposed [4], in which the authors also propose a meta-approach called HICODE (Hidden Community Detection) for detecting hidden communities . HICODE is demonstrated through experiments that it is able to uncover previously overshadowed weak layers and uncover both weak and strong layers at a higher accuracy.
Edit intent: clarity
Original:  We bridge their gap through two-layer stochastic block model networks in the following aspects: 1) we show that partitions that locally optimize modularity correspond to  layers, indicating modularity-optimizing algorithms can detect strong layers;
Revised:  We bridge their gap through two-layer stochastic block model networks in the following aspects: 1) we show that partitions that locally optimize modularity correspond to grounded layers, indicating modularity-optimizing algorithms can detect strong layers;
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Original:  By analyzing data collected from two nation-wide news outlets, we demonstrate that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems.
Revised:  By analyzing data collected from two nation-wide news outlets, BBC and New York Times, where the number of user comments can be used as a proxy of news impact. We find that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems.
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Original:  In particular, we find that the news comment count follows a universal exponential distributionwhich is explained by the lack  of the otherwise omnipresent rich-get-richer mechanism . Exponential aging induces a universal dynamics of article impact.
Revised:  In particular, we find that a simple exponential distribution yields a better fit to the empirical news impact distributions than a power-law distribution. This observation is explained by the lack of the otherwise omnipresent rich-get-richer mechanism . Exponential aging induces a universal dynamics of article impact.
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Original:  In particular, we find that the news comment count follows a universal exponential distributionwhich is explained by the lack  of the otherwise omnipresent rich-get-richer mechanism . Exponential aging induces a universal dynamics of article impact.
Revised:  In particular, we find that the news comment count follows a universal exponential distributionwhich is explained by the lack or limited influence of the otherwise omnipresent rich-get-richer mechanism . Exponential aging induces a universal dynamics of article impact.
Edit intent: clarity
Original:  In particular, we find that the news comment count follows a universal exponential distributionwhich is explained by the lack  of the otherwise omnipresent rich-get-richer mechanism . Exponential aging induces a universal dynamics of article impact.
Revised:  In particular, we find that the news comment count follows a universal exponential distributionwhich is explained by the lack of the otherwise omnipresent rich-get-richer mechanism in the analyzed data. The temporal dynamics of the news impact exhibits a universal exponential decay which allows us to collapse individual news trajectories into an elementary single curve. We also show how daily variations of user activity directly influence the dynamics of the article impact.
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Original:  A spherical drawing of a graph G is a drawing in the unit sphere S^2 in which the vertices of G are represented as points---no three on a great circle---and the edges of G are shortest-arcs in S^2 connecting pairs of vertices.
Revised:  A spherical drawing of a graph G is a drawing in the unit sphere S^2 in which the vertices of G are represented as points -- no three on a great circle---and the edges of G are shortest-arcs in S^2 connecting pairs of vertices.
Edit intent: fluency
Original:  A spherical drawing of a graph G is a drawing in the unit sphere S^2 in which the vertices of G are represented as points---no three on a great circle---and the edges of G are shortest-arcs in S^2 connecting pairs of vertices.
Revised:  A spherical drawing of a graph G is a drawing in the unit sphere S^2 in which the vertices of G are represented as points---no three on a great circle -- and the edges of G are shortest-arcs in S^2 connecting pairs of vertices.
Edit intent: fluency
Original:  We use these three properties to define a pseudospherical drawing of G. Our main result is that, for the complete graph, these three properties are equivalent to the same three properties but with " exactly two crossings"  replaced by "at most two crossings".
Revised:  We use Properties (1)--(3) to define a pseudospherical drawing of G. Our main result is that, for the complete graph, these three properties are equivalent to the same three properties but with " exactly two crossings" replaced by "at most two crossings".
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