wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Specifically, we recently demonstrated that seizures themselves change in terms of their evolution. However, the underpinning modulators of seizure evolution remain unclear.
Revised:  Specifically, we recently found variable seizure dynamics within individual patients. Additionally, the variability appeared to follow subject-specific circadian or longer timescale modulations. However, whether signatures of these modulations over different timescales can be captured on continuous (interictal) EEG remains unclear.
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Original:  In this work, we analyse continuous (interictal ) intracranial Electroencephalographic (iEEG) recordings , and elucidate fluctuations in iEEG band power over different timescales ( ranging from minutes to days) .
Revised:  In this work, we analyse continuous interictal intracranial electroencephalographic (iEEG) recordings , and elucidate fluctuations in iEEG band power over different timescales ( ranging from minutes to days) .
Edit intent: fluency
Original:  In this work, we analyse continuous (interictal ) intracranial Electroencephalographic (iEEG) recordings , and elucidate fluctuations in iEEG band power over different timescales ( ranging from minutes to days) .
Revised:  In this work, we analyse continuous (interictal ) intracranial Electroencephalographic (iEEG) recordings from video-telemetry units and find fluctuations in iEEG band power over different timescales ( ranging from minutes to days) .
Edit intent: meaning-changed
Original:  In this work, we analyse continuous (interictal ) intracranial Electroencephalographic (iEEG) recordings , and elucidate fluctuations in iEEG band power over different timescales ( ranging from minutes to days) .
Revised:  In this work, we analyse continuous (interictal ) intracranial Electroencephalographic (iEEG) recordings , and elucidate fluctuations in iEEG band power over different timescales ranging from minutes to days) .
Edit intent: fluency
Original:  In this work, we analyse continuous (interictal ) intracranial Electroencephalographic (iEEG) recordings , and elucidate fluctuations in iEEG band power over different timescales ( ranging from minutes to days) .
Revised:  In this work, we analyse continuous (interictal ) intracranial Electroencephalographic (iEEG) recordings , and elucidate fluctuations in iEEG band power over different timescales ( ranging from minutes up to twelve days .
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Original:  Importantly, we find that a combination of fluctuations on different timescales can explain the changes in seizure evolution in most patients above chance-level. We interpret these results as evidence that seizure modulating factors exist, and they vary over time (patient-specifically). These time-varying modulating factors can be captured in fluctuations of EEG band power , and future work should link them to the exact biological time-varying processes  .
Revised:  Importantly, we find that a combination of fluctuations on different timescales can explain the changes in seizure network evolution in a regression model in most subjects above chance level. These results suggest that subject-specific fluctuations in iEEG band power over timescales of minutes to days are associated with how seizures are modulated over time. Future work is needed to link the detected fluctuations to the exact biological time-varying processes .
Edit intent: clarity
Original:  These time-varying modulating factors can be captured in fluctuations of EEG band power , and future work should link them to the exact biological time-varying processes  .
Revised:  These time-varying modulating factors can be captured in fluctuations of EEG band power , and future work should link them to the exact biological time-varying processes . Understanding seizure modulating factors enables development of novel treatment strategies that minimise the seizure spread, duration, or severity and therefore clinical impact of seizures .
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Original: Neural word alignmentmethods have received increasing attention recently. These methods usually extract word alignment from a machine translation model. However, there is a gap between translation and alignment tasks, since the target future context is available in the latter .
Revised: Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence .
Edit intent: clarity
Original:  Our model parallelly masks and predicts each target token , and extracts high-quality alignments without any supervised loss. In addition, we introduce leaky attention to alleviate the problem of unexpected high attention weights on special tokens  . Experiments on four language pairs show that our model significantly outperforms all existing unsupervised neural baselines and obtains new state-of-the-art results.
Revised:  Our model parallelly masks and predicts each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high attention weights on special tokens . Experiments on four language pairs show that our model significantly outperforms all existing unsupervised neural baselines and obtains new state-of-the-art results.
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Original:  In addition, we introduce leaky attention to alleviate the problem of unexpected high attention weights on special tokens  . Experiments on four language pairs show that our model significantly outperforms all existing unsupervised neural baselines and obtains new state-of-the-art results.
Revised:  In addition, we introduce leaky attention to alleviate the problem of unexpected high cross-attention weights on special tokens . Experiments on four language pairs show that our model significantly outperforms all existing unsupervised neural baselines and obtains new state-of-the-art results.
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Original:  In addition, we introduce leaky attention to alleviate the problem of unexpected high attention weights on special tokens  . Experiments on four language pairs show that our model significantly outperforms all existing unsupervised neural baselines and obtains new state-of-the-art results.
Revised:  In addition, we introduce leaky attention to alleviate the problem of unexpected high attention weights on special tokens such as periods . Experiments on four language pairs show that our model significantly outperforms all existing unsupervised neural baselines and obtains new state-of-the-art results.
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Original:  In addition, we introduce leaky attention to alleviate the problem of unexpected high attention weights on special tokens  . Experiments on four language pairs show that our model significantly outperforms all existing unsupervised neural baselines and obtains new state-of-the-art results.
Revised:  In addition, we introduce leaky attention to alleviate the problem of unexpected high attention weights on special tokens . Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.
Edit intent: clarity
Original:  However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the `` exposure bias" problem.
Revised:  However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the " exposure bias" problem.
Edit intent: fluency
Original: The generation of graph-structured data is an emerging problem in the field of deep learning. Various solutions have been proposed in the last few years, yet the exploration of this branch is still in an early phase. In sequential approaches, the construction of a graph is the result of a sequence of decisions, in which, at each step, a node or a group of nodes is added to the graph, along with its connections. A very relevant application of graph generation methods is the discovery of new drug molecules, which are naturally represented as graphs.
Revised: Drug Discovery is a fundamental and ever-evolving field of research. The design of new candidate molecules requires large amounts of time and money, and computational methods are being increasingly employed to cut these costs. Machine learning methods are ideal for the design of large amounts of potential new candidate molecules, which are naturally represented as graphs.
Edit intent: clarity
Original:   In this paper, we introduce a sequential molecular graph generator based on a set of graph neural network modules, which we call MG^2N^2.
Revised:  Graph generation is being revolutionized by deep learning methods, and molecular generation is one of its most promising applications. In this paper, we introduce a sequential molecular graph generator based on a set of graph neural network modules, which we call MG^2N^2.
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Original:  To alleviate these drawbacks, we propose a LiDAR-based 3D MOT framework named FlowMOT, which integrates point-wise motion information into the traditional matching algorithm, enhancing the robustness of the data association .
Revised:  To alleviate these drawbacks, we propose a LiDAR-based 3D MOT framework named FlowMOT, which integrates point-wise motion information with the traditional matching algorithm, enhancing the robustness of the data association .
Edit intent: clarity
Original:  To alleviate these drawbacks, we propose a LiDAR-based 3D MOT framework named FlowMOT, which integrates point-wise motion information into the traditional matching algorithm, enhancing the robustness of the data association .
Revised:  To alleviate these drawbacks, we propose a LiDAR-based 3D MOT framework named FlowMOT, which integrates point-wise motion information into the traditional matching algorithm, enhancing the robustness of the motion prediction .
Edit intent: clarity
Original: While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, static word vectors continue to play an important role in tasks where word meaning needs to be modelled in the absence of linguistic context.
Revised: While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where word meaning needs to be modelled in the absence of linguistic context.
Edit intent: clarity
Original: While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, static word vectors continue to play an important role in tasks where word meaning needs to be modelled in the absence of linguistic context.
Revised: While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, static word vectors continue to play an important role in tasks where words need to be modelled in the absence of linguistic context.
Edit intent: clarity
Original:  Does the current focus fully adhere to users' desires or should we expand or change our focus? We investigate this question empirically by conducting a survey amongst heavy users of pre-made summaries.
Revised:  Does the current focus fully adhere to users' needs. Importantly, these needs are dependent on the envisioned target group of the generated summaries. One such important target group is formed by students, due to their usage of summaries in their study activities. For this reason, we investigate students' needs regarding automatically generated summaries by means of a survey amongst heavy users of pre-made summaries.
Edit intent: clarity
Original:  First, we argue that it is important to adopt a broader perspective on automatic summarization . Based on our findings, we illustrate how we can expand our view when it comes to the types of input materialthat is to be summarized , the purpose of the summaries and their potential formats.
Revised:  First, the educational domain requires a broader perspective on automatic summarization . Based on our findings, we illustrate how we can expand our view when it comes to the types of input materialthat is to be summarized , the purpose of the summaries and their potential formats.
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Original:  First, we argue that it is important to adopt a broader perspective on automatic summarization . Based on our findings, we illustrate how we can expand our view when it comes to the types of input materialthat is to be summarized , the purpose of the summaries and their potential formats.
Revised:  First, we argue that it is important to adopt a broader perspective on automatic summarization , beyond the approaches that are currently the standard. We illustrate how we can expand our view when it comes to the types of input materialthat is to be summarized , the purpose of the summaries and their potential formats.
Edit intent: meaning-changed
Original:  First, we argue that it is important to adopt a broader perspective on automatic summarization . Based on our findings, we illustrate how we can expand our view when it comes to the types of input materialthat is to be summarized , the purpose of the summaries and their potential formats.
Revised:  First, we argue that it is important to adopt a broader perspective on automatic summarization . Based on our findings, we illustrate how we can expand these approaches regarding the input material , the purpose of the summaries and their potential formats.
Edit intent: clarity
Original:  First, we argue that it is important to adopt a broader perspective on automatic summarization . Based on our findings, we illustrate how we can expand our view when it comes to the types of input materialthat is to be summarized , the purpose of the summaries and their potential formats. Second, we define requirements for datasets that can facilitate these research directions.
Revised:  First, we argue that it is important to adopt a broader perspective on automatic summarization . Based on our findings, we illustrate how we can expand our view when it comes to the types of input materialthat is to be summarized , the purpose of the summaries and their potential format and we define requirements for datasets that can facilitate these research directions.
Edit intent: coherence
Original:  we propose a methodology to evaluate the usefulness of a summary . With this work we unlock important research directions for future work on automatic summarization and we hope to initiate the development of methods in these directions .
Revised:  we propose a methodology to evaluate the usefulness of a summary based on the identified needs of a target group. Third, in more general terms, we hope that our survey will be reused to investigate the needs of different user groups of automatically generated summaries to broaden our perspective even further .
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Original: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact.
Revised: Background: The COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact.
Edit intent: clarity
Original: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact.
Revised: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact.
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Original: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact.
Revised: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas with potential negative public health impact.
Edit intent: clarity
Original: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact.
Revised: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas that have potentially negative public health impact.
Edit intent: fluency
Original: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact. Methods: Using a curated data set of COVID-19 tweets (N ~120 million tweets) spanning late January to early May 2020, we applied methods including regular expression filtering, supervised machine learning, sentiment analysis, geospatial analysis, and dynamic topic modeling to trace the spread of misinformation and to characterize novel features of COVID-19 conspiracy theories. Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857 , while more broad theories performed measurably worse, with scores of 0.654 and 0.347.
Revised: Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health . Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impacts. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered ( F1 scores of 0.804 and 0.857 , while more broad theories performed measurably worse, with scores of 0.654 and 0.347.
Edit intent: clarity
Original:  Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857 , while more broad theories performed measurably worse, with scores of 0.654 and 0.347.
Revised:  Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores between 0.347 and 0.857 , while more broad theories performed measurably worse, with scores of 0.654 and 0.347.
Edit intent: meaning-changed
Original:  Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857 , while more broad theories performed measurably worse, with scores of 0.654 and 0.347. Despite this , analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. We were able to identify distinct increases in negative sentiment , theory-specific trends in geospatial spread, and the evolution of conspiracy theory topics and subtopics over time.
Revised:  Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857 ); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time , theory-specific trends in geospatial spread, and the evolution of conspiracy theory topics and subtopics over time.
Edit intent: clarity
Original:  We were able to identify distinct increases in negative sentiment , theory-specific trends in geospatial spread, and the evolution of conspiracy theory topics and subtopics over time. Conclusions: COVID-19 related conspiracy theories show that history frequently repeats itself, with the same conspiracy theories being recycled for new situations. We use a combination of supervised learning, unsupervised learning, and natural language processing techniques to look at the evolution of theories over the first four months of the COVID-19 outbreak, how these theories intertwine, and to hypothesize on more effective public health messaging to combat misinformation in online spaces .
Revised:  We were able to identify distinct increases in negative sentiment , incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated .
Edit intent: clarity