wanyu/IteraTeR_full_sent huggingface dataset:
Original: The COVID-19 pandemic has sparked unprecedented mobilization of scientists, already generating thousands of new papers that join a litany of previous biomedical work in related areas. This deluge of information makes it hard for researchers to keep track of their own research area, let alone explore new directions.
Revised: The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track of their own research area, let alone explore new directions.
Edit intent: clarity
Original:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics);
Revised:  In this paper, we present SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics);
Edit intent: clarity
Original:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics);
Revised:  In this paper, we present our ongoing work on SciSight, a system for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics);
Edit intent: clarity
Original:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics);
Revised:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research integrating two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics);
Edit intent: coherence
Original:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics);
Revised:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring associations between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics);
Edit intent: clarity
Original:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics);
Revised:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets automatically extracted from papers (e.g., proteins, genes, drugs, diseases, patient characteristics);
Edit intent: meaning-changed
Original:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics);
Revised:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets (e.g., genes, drugs, diseases, patient characteristics);
Edit intent: meaning-changed
Original:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics); and second, discovering groups of researchers and how they are connected. We extract entities using a language model pre-trained on several biomedical information extraction tasks, and enrich them with data from the Microsoft Academic Graph (MAG). To find research groups automatically, we use hierarchical clustering with overlap to allow authors, as they do, to belong to multiple groups. Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 30K page views and 13 \% returning users.
Revised:  In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties . SciSight has thus far served over 10K users with over 30K page views and 13 \% returning users.
Edit intent: coherence
Original:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 30K page views and 13 \% returning users.
Revised:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has so far served over 10K users with over 30K page views and 13 \% returning users.
Edit intent: fluency
Original:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 30K page views and 13 \% returning users.
Revised:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 15K users with over 30K page views and 13 \% returning users.
Edit intent: meaning-changed
Original:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 30K page views and 13 \% returning users.
Revised:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 42K page views and 13 \% returning users.
Edit intent: meaning-changed
Original:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 30K page views and 13 \% returning users. Preliminary user interviews with biomedical researchers suggest that SciSight complements current approaches and helps find new and relevant knowledge.
Revised:  Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 30K page views and 13 
Edit intent: coherence
Original:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Revised:  However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Edit intent: clarity
Original:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Revised:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Edit intent: coherence
Original:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Revised:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Edit intent: coherence
Original:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Revised:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the observed samples , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Edit intent: clarity
Original:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
Revised:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items .
Edit intent: coherence
Original:  In this paper, we introduce CLRec, a Contrastive Learning paradigm  successfully deployed in a real-world massive recommender system, for alleviating exposure unfairness in DCG.
Revised:  In this paper, we introduce CLRec, a Contrastive Learning paradigm that has been successfully deployed in a real-world massive recommender system, for alleviating exposure unfairness in DCG.
Edit intent: clarity
Original:  We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store items (and reuse the computed representations ) from previous batches, where the queue serves as the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Revised:  We further employ a fix-sized queue to store items (and reuse the computed representations ) from previous batches, where the queue serves as the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Edit intent: coherence
Original:  We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store items (and reuse the computed representations ) from previous batches, where the queue serves as the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Revised:  We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Edit intent: coherence
Original:  We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store items (and reuse the computed representations ) from previous batches, where the queue serves as the negative sampler . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Revised:  We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store items (and reuse the computed representations ) from previous batches, where the queue serves as the items' representations computed in previously processed batches, and use the queue to serve as an effective sampler of negative examples. This queue-based design provides great efficiency in incorporating rich features of the thousand negative items per batch thanks to computation reuse . Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Edit intent: meaning-changed
Original: We iterate Dorfman's pool testing algorithm \mbox{%DIFAUXCMD dorfman previous stage.
Revised: In order to identify the infected individuals of a population, their samples are divided in equally sized groups called pools and a single laboratory test is applied to each pool. Individuals whose samples belong to pools that test negative are declared healthy, while each pool that tests positive is divided into smaller, equally sized pools which are tested in the next stage. This scheme is called adaptive, because the composition of the pools at each stage depends on results from previous stages, and nested because each pool is a subset of a pool of the previous stage.
Edit intent: meaning-changed
Original:  We compute the mean and variance of the number of tests per individual as a function of the pool sizes m=(m_1,\dots,m_k) in the first k stages;
Revised:  Is the infection probability p is not smaller than 1-3^{-1/3 compute the mean and variance of the number of tests per individual as a function of the pool sizes m=(m_1,\dots,m_k) in the first k stages;
Edit intent: meaning-changed
Original:  We compute the mean and variance of the number of tests per individual as a function of the pool sizes m=(m_1,\dots,m_k) in the first k stages;
Revised:  We compute the mean D_k(m,p) and the variance of the number of tests per individual as a function of the pool sizes m=(m_1,\dots,m_k) in the first k stages;
Edit intent: meaning-changed
Original:  We show that the cost of the strategy (3^k,%DIFDELCMD < \dots%%% ,3) with k\approx \log_3(1/p) is of order p\log(1/p), and differs from the optimal cost by a fraction of this value.
Revised:  We show that %DIFDELCMD < \dots%%% ,3) with k\approx \log_3(1/p) is of order p\log(1/p), and differs from the optimal cost by a fraction of this value.
Edit intent: clarity
Original:  We show that the cost of the strategy (3^k,%DIFDELCMD < \dots%%% ,3) with k\approx \log_3(1/p) is of order p\log(1/p), and differs from the optimal cost by a fraction of this value. To prove this result we bound the difference between the cost of this strategy with the minimal cost when pool sizes take real values. We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1}  \dots,3  ), with a precise description for k.
Revised:  We show that the cost of the strategy (3^k,%DIFDELCMD < \dots%%% for p\in (0, 1-3^{-1/3 (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1} \dots,3 ), with a precise description for k.
Edit intent: meaning-changed
Original:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1}  \dots,3  ), with a precise description for k.
Revised:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k %DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1} \dots,3 ), with a precise description for k.
Edit intent: fluency
Original:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1}  \dots,3  ), with a precise description for k.
Revised:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% \text{ or 3 ) or of the form (3 ^{k-1}4,3^{k-1} \dots,3 ), with a precise description for k.
Edit intent: meaning-changed
Original:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1}  \dots,3  ), with a precise description for k.
Revised:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ^{k-1}4,3^{k-1} \dots,3 ), with a precise description for k.
Edit intent: coherence
Original:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1}  \dots,3  ), with a precise description for k.
Revised:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1} , \dots,3 ), with a precise description for k.
Edit intent: fluency
Original:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1}  \dots,3  ), with a precise description for k.
Revised:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1} \dots,3 ^2,3 ), with a precise description for k.
Edit intent: meaning-changed
Original:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1}  \dots,3  ), with a precise description for k. This conjecture is supported by inspection of a family of values of p. Finally, we observe that for these values of p and the best strategy of the form (3^k,\dots,3), \big(\big) the standard deviation of the number of tests per individual is of the same order as the cost .
Revised:  We conjecture that the optimal strategy, depending on the value of p, is indeed of the form (3^k ,%DIFDELCMD < \dots%%% , 3 ) or of the form (3 ^{k-1}4,3^{k-1} \dots,3 ), with a precise description of the range of p's where each holds. We then focus on schemes of the type (3^k,\dots,3), \big(\big) the standard deviation of the number of tests per individual is of the same order as the cost .
Edit intent: clarity