wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  Here, we studied a semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Edit intent: coherence
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  In our semi-analytical model and identified two tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Edit intent: clarity
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spread: (1) the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Edit intent: coherence
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden chains becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Edit intent: style
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Edit intent: clarity
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and (2) the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Edit intent: meaning-changed
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections exceeds the tracing capacity , causing a self-accelerating spread.
Edit intent: coherence
Original:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity , causing a self-accelerating spread.
Revised:  In our semi-analytical model, we identified two distinct tipping points between controlled and uncontrolled spreading: one, at which the behavior-driven reproduction number of the hidden infections becomes too large to be compensated by the available TTI capabilities, and one at which the number of new infections starts to exceed the tracing capacity . Both trigger a self-accelerating spread.
Edit intent: coherence
Original:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Revised:  We investigated how these tipping points depend on challenges like limited cooperation , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Edit intent: clarity
Original:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Revised:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation . Our model results suggest that TTI alone is insufficient to contain an otherwise unhindered spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Edit intent: clarity
Original:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Revised:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 , implying that complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Edit intent: coherence
Original:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Revised:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like social distancing and improved hygiene probably remain necessary.
Edit intent: clarity
Original:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene probably remain necessary.
Revised:  We investigated how these tipping points depend on realistic limitations like limited cooperativity , missing contacts, and imperfect isolation , finding that TTI is likely not sufficient to contain the natural spread of SARS-CoV-2 . Therefore, complementary measures like reduced physical contacts and improved hygiene remain necessary.
Edit intent: coherence
Original:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from  large volumes of data .
Revised:  As a result , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from large volumes of data .
Edit intent: coherence
Original:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from  large volumes of data .
Revised:  Because of this , more agencies are interested in programmatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from large volumes of data .
Edit intent: fluency
Original:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from  large volumes of data .
Revised:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) . Therefore, recognizing the informativeness of a tweet can help filter noise from large volumes of data .
Edit intent: coherence
Original:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from  large volumes of data .
Revised:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a Tweet can help filter noise from large volumes of data .
Edit intent: fluency
Original:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from  large volumes of data .
Revised:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from the large volumes of data .
Edit intent: fluency
Original:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from  large volumes of data .
Revised:  Because of this , more agencies are interested in programatically monitoring Twitter (disaster URLanizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from large volumes of Tweets .
Edit intent: clarity
Original:  The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) , and shows significant gains in performance compared to a baseline system using fasttext embeddings.
Revised:  The proposed system achieves an F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) , and shows significant gains in performance compared to a baseline system using fasttext embeddings.
Edit intent: fluency
Original:  The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) , and shows significant gains in performance compared to a baseline system using fasttext embeddings.
Revised:  The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) and shows significant gains in performance compared to a baseline system using fasttext embeddings.
Edit intent: fluency
Original:  The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) , and shows significant gains in performance compared to a baseline system using fasttext embeddings.
Revised:  The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) , and shows significant gains in performance compared to a baseline system using FastText embeddings.
Edit intent: fluency
Original:  The second alternative , the generalised multinomial robit (Gen-MNR) model, has not been studied in the literature before and is more flexible than MNR, as it allows for alternative-specific marginal heavy-tailedness  of the kernel error distribution.
Revised:  The second model , the generalised multinomial robit (Gen-MNR) model, has not been studied in the literature before and is more flexible than MNR, as it allows for alternative-specific marginal heavy-tailedness of the kernel error distribution.
Edit intent: clarity
Original:  The second alternative , the generalised multinomial robit (Gen-MNR) model, has not been studied in the literature before and is more flexible than MNR, as it allows for alternative-specific marginal heavy-tailedness  of the kernel error distribution.
Revised:  The second alternative , the generalised multinomial robit (Gen-MNR) model, is more flexible than MNR, as it allows for alternative-specific marginal heavy-tailedness of the kernel error distribution.
Edit intent: coherence
Original:  The second alternative , the generalised multinomial robit (Gen-MNR) model, has not been studied in the literature before and is more flexible than MNR, as it allows for alternative-specific marginal heavy-tailedness  of the kernel error distribution.
Revised:  The second alternative , the generalised multinomial robit (Gen-MNR) model, has not been studied in the literature before and is more flexible than MNR, as it allows for distinct heavy-tailedness of the kernel error distribution.
Edit intent: clarity
Original:  The second alternative , the generalised multinomial robit (Gen-MNR) model, has not been studied in the literature before and is more flexible than MNR, as it allows for alternative-specific marginal heavy-tailedness  of the kernel error distribution.
Revised:  The second alternative , the generalised multinomial robit (Gen-MNR) model, has not been studied in the literature before and is more flexible than MNR, as it allows for alternative-specific marginal heavy-tailedness in each dimension of the kernel error distribution.
Edit intent: meaning-changed
Original:  For both models, we devise scalable and gradient-free Bayes estimators. We compare MNP, MNR and Gen-MNR in a simulation study and a case study on transport mode choice behaviour .
Revised:  For both models, we derive efficient Gibbs sampling schemes, which also allow for a straightforward inclusion of random parameters. In a simulation study, we illustrate the excellent finite sample properties of the proposed Bayes estimators and show that MNR and Gen-MNR in a simulation study and a case study on transport mode choice behaviour .
Edit intent: meaning-changed
Original:  We compare MNP, MNR and Gen-MNR in a simulation study and a case study on transport mode choice behaviour . We find that both MNR and Gen-MNR deliver significantly better in-sample fit and out-of-sample predictive accuracy than MNP. Gen-MNR outperforms MNR due to its more flexible kernel error distribution.
Revised:  We compare MNP, MNR and Gen-MNR in a simulation study and a case study on transport mode choice behaviour , MNR and Gen-MNR deliver significantly better in-sample fit and out-of-sample predictive accuracy than MNP. Gen-MNR outperforms MNR due to its more flexible kernel error distribution.
Edit intent: coherence
Original:  We find that both MNR and Gen-MNR deliver significantly better in-sample fit and out-of-sample predictive accuracy than MNP. Gen-MNR outperforms MNR due to its more flexible kernel error distribution.
Revised:  We find that both MNR and Gen-MNR outperform MNP by substantial margins in terms of in-sample fit and out-of-sample predictive accuracy than MNP. Gen-MNR outperforms MNR due to its more flexible kernel error distribution.
Edit intent: clarity
Original:  We find that both MNR and Gen-MNR deliver significantly better in-sample fit and out-of-sample predictive accuracy than MNP. Gen-MNR outperforms MNR due to its more flexible kernel error distribution.
Revised:  We find that both MNR and Gen-MNR deliver significantly better in-sample fit and out-of-sample predictive accuracy . We also find that the benefits of the more flexible kernel error distribution.
Edit intent: coherence
Original:  We find that both MNR and Gen-MNR deliver significantly better in-sample fit and out-of-sample predictive accuracy than MNP. Gen-MNR outperforms MNR due to its more flexible kernel error distribution. Also, Gen-MNR gives more reasonable elasticity estimates than MNP and MNR , in particular regarding the demand for under-represented alternatives in a class-imbalanced dataset .
Revised:  We find that both MNR and Gen-MNR deliver significantly better in-sample fit and out-of-sample predictive accuracy than MNP. Gen-MNR outperforms MNR due to its more flexible kernel error distributions underlying MNR and Gen-MNR gives more reasonable elasticity estimates than MNP and MNR , in particular regarding the demand for under-represented alternatives in a class-imbalanced dataset .
Edit intent: meaning-changed
Original:  However, existing datasets  do not provide annotations for the gold evidence pieces, which is a critical aspect for improving the explainability of fact checking systems. The only exception is the FEVER dataset , which is artificially constructed based on Wikipedia and does not use naturally occurring political claims and evidence pages, which is more challenging. Most claims in FEVER only have one evidence sentence associated with them and require no reasoning to make label predictions -- the small number of instances with two evidence sentences only require simple reasoning . In this paper , we study how to perform more complex claim verification on naturally occurring claims with multiple hops over  evidence chunks.
Revised:  However, existing datasets do not provide annotations for gold evidence pages, or the only dataset which does (FEVER) mostly consists of claims which can be fact-checked with simple reasoning and is constructed artificially. Here , we study how to perform more complex claim verification on naturally occurring claims with multiple hops over evidence chunks.
Edit intent: clarity
Original:  We first construct a small annotated dataset, PolitiHop, of reasoning chains for claim verification . We then compare the dataset to other existing multi-hop datasets and study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop.
Revised:  We first construct a small annotated dataset, PolitiHop, of reasoning chains for claim verification ; 2) compare it to existing multi-hop datasets and study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop.
Edit intent: coherence
Original: Inspired by Google's Neural Machine Translation (NMT) \mbox{%DIFAUXCMD Wu2016Google that models the one-to-one alignment in translation tasks with an optimal uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source paragraphs/sentences with the optimal coverage.
Revised: Inspired by Google's Neural Machine Translation (NMT) that models the one-to-one alignment in translation tasks with an optimal uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source paragraphs/sentences with the optimal coverage.
Edit intent: coherence
Original: Inspired by Google's Neural Machine Translation (NMT) \mbox{%DIFAUXCMD Wu2016Google that models the one-to-one alignment in translation tasks with an optimal uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source paragraphs/sentences with the optimal coverage.
Revised: Inspired by Google's Neural Machine Translation (NMT) \mbox{%DIFAUXCMD Wu2016Google that models the one-to-one alignment in translation tasks with an uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source paragraphs/sentences with the optimal coverage.
Edit intent: clarity
Original: Inspired by Google's Neural Machine Translation (NMT) \mbox{%DIFAUXCMD Wu2016Google that models the one-to-one alignment in translation tasks with an optimal uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source paragraphs/sentences with the optimal coverage.
Revised: Inspired by Google's Neural Machine Translation (NMT) \mbox{%DIFAUXCMD Wu2016Google that models the one-to-one alignment in translation tasks with an optimal uniform attention distribution during the inference, this study proposes an attention-aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source contents with the optimal coverage.
Edit intent: clarity