wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Using this to compare the relative decay of the number of deaths per day between different regions in Italy, Spain and England, each applying in principle the same social distancing procedures across the whole country, I obtain an estimate of the total fraction of the population which has already become infected  .
Revised:  Using this to compare the relative decay of the number of deaths per day between different regions in Italy, Spain and England, each applying in principle the same social distancing procedures across the whole country, I obtain an estimate of the total fraction of the population which had already become infected .
Edit intent: fluency
Original:  Using this to compare the relative decay of the number of deaths per day between different regions in Italy, Spain and England, each applying in principle the same social distancing procedures across the whole country, I obtain an estimate of the total fraction of the population which has already become infected  .
Revised:  Using this to compare the relative decay of the number of deaths per day between different regions in Italy, Spain and England, each applying in principle the same social distancing procedures across the whole country, I obtain an estimate of the total fraction of the population which has already become infected by April 10th .
Edit intent: meaning-changed
Original:  This result can then be converted to a determination of the infection fatality rate ifr, which appears to be ifr \approx 0.0025-0.005, and even smaller in London, somewhat lower than usually assumed  .
Revised:  This result can then be converted to a determination of the infection fatality rate ifr, which appears to be ifr \approx 0.0025-0.005, and even smaller in London, somewhat lower than usually assumed . Alternatively, the result can also be interpreted as an effectively larger fraction of the population than simple counting would suggest if there is a variation in susceptibility to infection with a variance of up to a value of about 2. The implications are very similar for either interpretation or for a combination of effects .
Edit intent: meaning-changed
Original:  The same law applies to the analysis of Machine Learning (ML) models. One method to explain the model is not enough because different questions and different stakeholders need different approaches. Most of the proposed methods for eXplainable Artificial Intelligence (XAI) focus on a single aspect of model behaviour. However, we cannot sufficiently explain a complex model using a single method that gives only one perspective.
Revised:  The same law applies to the explanatory analysis of machine learning models. We cannot sufficiently explain a complex model using a single method that gives only one perspective.
Edit intent: clarity
Original:   In this paper, we present the problem of model explainability as an interactive and sequential explanatory analysis of a model (IEMA).
Revised:  Surprisingly, the majority of methods developed for Explainable Artificial Intelligence (XAI) focus on a single aspect of the model behaviour. In this paper, we present the problem of model explainability as an interactive and sequential explanatory analysis of a model (IEMA).
Edit intent: meaning-changed
Original:   In this paper, we present the problem of model explainability as an interactive and sequential explanatory analysis of a model (IEMA). We introduce the grammar of such interactive explanations. We show how different XAI methods complement each other and why it is essential to juxtapose them together.
Revised:  In this paper, we present the problem of model explainability as an interactive and sequential explanatory analysis of a model . We show how different XAI methods complement each other and why it is essential to juxtapose them together.
Edit intent: coherence
Original: When analysing a complex system, very often an answer to one question raises new questions. This also applies to the explanatory analysis of machine learning models. We cannot sufficiently explain a complex model using a single method that gives only one perspective.
Revised: The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a complex model using a single method that gives only one perspective.
Edit intent: meaning-changed
Original:  Surprisingly, the majority of methods developed for Explainable Artificial Intelligence (XAI) focus on a single aspect of the model behaviour. In this paper, we show the problem of model explainability as an interactive and sequential analysis of a model.
Revised:  Surprisingly, the majority of methods developed for Explainable Artificial Intelligence (XAI) focus on a single aspect of the model behavior. In contrast, we showcase the problem of model explainability as an interactive and sequential analysis of a model.
Edit intent: coherence
Original: We introduce a persistent random walk model with finite velocity and self-reinforcing directionality, which explains how exponentially distributed runs URLanize into superdiffusive L\'evy walks observed in active intracellular transport by Chen et. al. [ Nat. mat. , 2015].
Revised: We introduce a persistent random walk model with finite velocity and self-reinforcing directionality, which explains how exponentially distributed runs URLanize into truncated L\'evy walks observed in active intracellular transport by Chen et. al. [ Nat. mat. , 2015].
Edit intent: meaning-changed
Original: We introduce a persistent random walk model with finite velocity and self-reinforcing directionality, which explains how exponentially distributed runs URLanize into superdiffusive L\'evy walks observed in active intracellular transport by Chen et. al. [ Nat. mat. , 2015].
Revised: We introduce a persistent random walk model with finite velocity and self-reinforcing directionality, which explains how exponentially distributed runs URLanize into superdiffusive L\'evy walks observed in active intracellular transport by Chen et. al. [ Nat. mat. , 2015].
Edit intent: fluency
Original: We propose a novel graph-based ranking model for unsupervised extractive summarization of long documents. Graph-based ranking models typically represent documents as undirected fully-connected graphs, where a node is a sentence, an edge is weighted based on sentence-pair similarity, and sentence importance is measured via node centrality. Our method leverages positional and hierarchical information grounded in discourse structure to augment a document's graph representation with hierarchy and directionality. Experimental results on PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins and performs comparably to some of the state-of-the-art supervised models that are trained on hundreds of thousands of examples.
Revised: We propose a novel graph-based ranking model for unsupervised extractive summarization of long scientific documents. Our method assumes a two-level hierarchical graph representation of the source document, and exploits asymmetrical positional cues to determine sentence importance. Results on the PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins and performs comparably to some of the state-of-the-art supervised models that are trained on hundreds of thousands of examples.
Edit intent: clarity
Original:  Experimental results on PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins and performs comparably to some of the state-of-the-art supervised models that are trained on hundreds of thousands of examples.
Revised:  Experimental results on PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins in automatic metrics and human evaluation. In addition, it achieves performance comparable to many state-of-the-art supervised models that are trained on hundreds of thousands of examples.
Edit intent: meaning-changed
Original:  Experimental results on PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins and performs comparably to some of the state-of-the-art supervised models that are trained on hundreds of thousands of examples.
Revised:  Experimental results on PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins and performs comparably to some of the state-of-the-art supervised approaches which are trained on hundreds of thousands of examples.
Edit intent: clarity
Original:   There are no known efficient algorithms to compute rotation distance between trees , where rotations are permitted at any node . Limiting the allowed locations of where rotations are permitted gives rise to a number of notions of restricted rotation distances.
Revised:  The one-dimensional skeleta of associahedra are rotation graphs, where two vertices representing trees are connected by an edge if they differ by a single rotation. There are no known efficient algorithms to compute rotation distance between trees , where rotations are permitted at any node . Limiting the allowed locations of where rotations are permitted gives rise to a number of notions of restricted rotation distances.
Edit intent: meaning-changed
Original:   There are no known efficient algorithms to compute rotation distance between trees , where rotations are permitted at any node . Limiting the allowed locations of where rotations are permitted gives rise to a number of notions of restricted rotation distances.
Revised:  There are no known efficient algorithms to compute rotation distance between trees and thus distances in rotation graphs . Limiting the allowed locations of where rotations are permitted gives rise to a number of notions of restricted rotation distances.
Edit intent: meaning-changed
Original: Much of the recent success in NLP is due to the large Transformer-based models such as BERT (Devlin et al, 2019). However, these models have been shown to be reducible to a smaller number of self-attention heads and layers.
Revised: Much of the recent success in NLP is due to the large Transformer-based models were shown to be reducible to a smaller number of self-attention heads and layers.
Edit intent: clarity
Original:  For fine-tuned BERT, we show that (a) it is possible to find a subnetwork of elements that achieves performance comparable with that of the full model, and (b) similarly-sized subnetworks sampled from the rest of the model perform worse.
Revised:  For fine-tuned BERT, we show that (a) it is possible to find subnetworks achieving performance that is comparable with that of the full model, and (b) similarly-sized subnetworks sampled from the rest of the model perform worse.
Edit intent: clarity
Original:  However, the "bad" subnetworks can be fine-tuned separately to achieve only slightly worse performance than the "good" ones , indicating that most weights in the pre-trained BERT  are potentially useful.
Revised:  However, the "bad" subnetworks can be fine-tuned separately to achieve only slightly worse performance than the "good" ones , indicating that most weights in the pre-trained BERT weights are potentially useful.
Edit intent: meaning-changed
Original:  In some cases, even worst-case splits  under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.
Revised:  We can also split data in biased or adversarial ways, e.g., training on short sentences and evaluating on long ones. Biased sampling has been used in domain adaptation to simulate real-world drift; this is known as the covariate shift assumption. In NLP, however, even worst-case splits under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.
Edit intent: meaning-changed
Original:  In some cases, even worst-case splits  under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.
Revised:  In some cases, even worst-case splits , maximizing bias, often under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.
Edit intent: meaning-changed
Original: Despite their large-scale coverage, existing cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity , necessitating link prediction that requires inferring a target entity, given a source entity and a query relation.
Revised: Despite their large-scale coverage, cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity , necessitating link prediction that requires inferring a target entity, given a source entity and a query relation.
Edit intent: clarity
Original: Despite their large-scale coverage, existing cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity , necessitating link prediction that requires inferring a target entity, given a source entity and a query relation.
Revised: Despite their large-scale coverage, existing cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity . Link prediction can alleviate this by inferring a target entity, given a source entity and a query relation.
Edit intent: clarity
Original:  Recent approaches can broadly be classified into two categories: embedding-based approaches and path-based approaches. In contrast to embedding-based approaches, which operate in an uninterpretable latent semantic vector space of entities and relations,  path-based approaches operate in the symbolic space, making the inference process explainable.
Revised:  Recent approaches can broadly be classified into two categories: embedding-based approaches operate in an uninterpretable latent semantic vector space of entities and relations, path-based approaches operate in the symbolic space, making the inference process explainable.
Edit intent: coherence
Original:  In contrast to embedding-based approaches, which operate in an uninterpretable latent semantic vector space of entities and relations,  path-based approaches operate in the symbolic space, making the inference process explainable.
Revised:  In contrast to embedding-based approaches, which operate in an uninterpretable latent semantic vector space of entities and relations, while path-based approaches operate in the symbolic space, making the inference process explainable.
Edit intent: coherence
Original: Information Extraction (IE) for  document images is often approached as a BIO tagging problem , where the model sequentially goes through and classifies each recognized input token into one of the information categories.
Revised: Information Extraction (IE) for document images is often approached as a BIO tagging problem , where the model sequentially goes through and classifies each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories.
Edit intent: meaning-changed
Original:  However, such problem setup has two inherent limitations that (1) it can only extract a flat list of information and (2) it assumes that the input data is serialized, often by a simple rule-based script.
Revised:  However, such problem setup has two inherent limitations that (1) it cannot easily handle complex spatial relationships and (2) it assumes that the input data is serialized, often by a simple rule-based script.
Edit intent: meaning-changed
Original:  However, such problem setup has two inherent limitations that (1) it can only extract a flat list of information and (2) it assumes that the input data is serialized, often by a simple rule-based script. Nevertheless, real-world documents often contain hierarchical informationin the form of two-dimensional language data in which the serialization can be highly non-trivial .
Revised:  However, such problem setup has two inherent limitations that (1) it can only extract a flat list of information and (2) it is not suitable for highly structured information, which are nevertheless frequently observed in real-world documents often contain hierarchical informationin the form of two-dimensional language data in which the serialization can be highly non-trivial .
Edit intent: meaning-changed
Original:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards.
Revised:  To tackle these issues, we first formulate the IE task as spatial dependency parsing problem that focuses on the relationship among text segment nodes in the documents. Under this setup, we then propose SPADE (SPAtial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards.
Edit intent: meaning-changed
Original:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards.
Revised:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) that models highly complex spatial relationships and an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards.
Edit intent: meaning-changed
Original:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards.
Revised:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers in the documents in an end-to-end manner. We evaluate it on various kinds of documents such as receipts and multimodal documents such as name cards.
Edit intent: clarity
Original:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards. We show that SPADE\spadesuit outperforms the previous BIO tagging-based approach on name card parsing task and achieves comparable performance on receipt parsing task. Especially, when the receipt images have non-flat manifold representing physical distortion of receipt paper in real-world, SPADE\spadesuit outperforms the tagging-based method by a large margin of 25.8\% highlighting the strong performance of SPADE\spadesuit over spatially complex document .
Revised:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts , name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger, with up to 37.7\% improvement .
Edit intent: meaning-changed
Original: While fine-tuning pre-trained language models (PTLMs) has yielded strong results on a range of question answering (QA) benchmarks, these methods still suffer in cases when external knowledge are needed to infer the right answer. Existing work on augmenting QA models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.
Revised: While fine-tuning pre-trained language models (PTLMs) has yielded strong results on a range of question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.
Edit intent: clarity
Original:  In this paper, we propose a novel knowledge-aware approach that equips PTLMs with a multi-hop relational reasoning module, named multi-hop graph relation networks (MHGRN).
Revised:  In this paper, we propose a novel knowledge-aware approach that equips pre-trained language models (PTLMs) with a multi-hop relational reasoning module, named multi-hop graph relation networks (MHGRN).
Edit intent: meaning-changed
Original: Exploring complex social contagion, especially the social reinforcement dynamics when exposing to multiple sources , is of vital importance for understanding the spread of complicated collective behaviors nowadays. While previous works exclusively focus on single-layer networks , the detailed impacts of multilayer reinforcement which characterizes the promoting effects among multiple social circles remain largely unknown .
Revised: Exploring complex social contagion, especially the social reinforcement dynamics when exposing to multiple sources in social contagion process, is ubiquitous in information-technology ecosystem and has aroused great attention in recent years. While the impacts of social reinforcement on single-layer networks , the detailed impacts of multilayer reinforcement which characterizes the promoting effects among multiple social circles remain largely unknown .
Edit intent: meaning-changed
Original:   Further, we show that the final state of bistable regions depends on the initial density of adopters, the critical value of which decreases as the contagion transmissibility or the multilayer reinforcement increases.
Revised:  In particular, the hysteresis loop of stationary prevalence occurs in this bistable region, explaining why the fight towards the spread of rumors is protracted and difficult in modern society. Further, we show that the final state of bistable regions depends on the initial density of adopters, the critical value of which decreases as the contagion transmissibility or the multilayer reinforcement increases.
Edit intent: meaning-changed
Original:  We present the first study investigating this question . We find that BERTwith a derivational classification layer outperforms an LSTM-based model .
Revised:  We present the first study investigating this question , taking BERT as the example PLM. We examine BERT's derivational capabilities in different settings, ranging from using the unmodified pretrained model to full finetuning. Our best model, DagoBERT (Derivationally and generatively optimized BERT), clearly outperforms the previous state of the art in derivation generation (DG).
Edit intent: meaning-changed