wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we present the novel task of Schema-Guided Natural Language Generation , in which we repurpose an existing dataset for another task: dialog state tracking . Dialog state tracking data includes a large and rich schema spanning multiple different attributes, including information about the domain, user intent, and slot descriptions.
Revised:  In this paper, we present the novel task of Schema-Guided Natural Language Generation , in which we repurpose an existing dataset for another task: dialog state tracking , which includes a large and rich schema spanning multiple different attributes, including information about the domain, user intent, and slot descriptions.
Edit intent: coherence
Original:  We train different state-of-the-art models for neural natural language generation on this data and show that inclusion of the rich schema allows our models to produce higher quality outputs both in terms of semantics and diversity.
Revised:  We train different state-of-the-art models for neural natural language generation on this dataset and show that inclusion of the rich schema allows our models to produce higher quality outputs both in terms of semantics and diversity.
Edit intent: clarity
Original:  We train different state-of-the-art models for neural natural language generation on this data and show that inclusion of the rich schema allows our models to produce higher quality outputs both in terms of semantics and diversity.
Revised:  We train different state-of-the-art models for neural natural language generation on this data and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity.
Edit intent: clarity
Original:  I introduce three extensions to these models to fit the daily arrivals in French hospitals, as well in critical care , releases and deaths . These models involve ratios of evolutionary timescales to branching fractions, assumed uniform throughout a country, and the basic reproduction number, R_0, before and during the national lockdown, for each region of France.
Revised:  I introduce three extensions to these models to fit the daily arrivals in French hospitals, as well in critical care , releases and deaths , using extended SEIR models. These involve ratios of evolutionary timescales to branching fractions, assumed uniform throughout a country, and the basic reproduction number, R_0, before and during the national lockdown, for each region of France.
Edit intent: meaning-changed
Original:  The  hospital data are well fit by the models with the exception of the arrivals in critical care, which are found to decrease in time faster than predicted by all models.
Revised:  The joint-region Bayesian analysis allows precise evaluations of the time/fraction ratios and pre-hospitalized fractions. The hospital data are well fit by the models with the exception of the arrivals in critical care, which are found to decrease in time faster than predicted by all models.
Edit intent: meaning-changed
Original:  The  hospital data are well fit by the models with the exception of the arrivals in critical care, which are found to decrease in time faster than predicted by all models. This suggests that hospitals have learnt over timeto better treat COVID-19 patients without resorting to critical care. The basic reproductive factor, averaged over France, was R_0= 3.4\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during it, both with small regional variations.
Revised:  The hospital data are well fit by the models with the exception of the arrivals in critical care, which are found to decrease in time faster than predicted , indicating better treatment over time. Averaged over France, was R_0= 3.4\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during it, both with small regional variations.
Edit intent: clarity
Original:  The basic reproductive factor, averaged over France, was R_0= 3.4\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during it, both with small regional variations.
Revised:  The basic reproductive factor, averaged over France, the analysis yields R_0= 3.4\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during it, both with small regional variations.
Edit intent: clarity
Original:  The basic reproductive factor, averaged over France, was R_0= 3.4\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during it, both with small regional variations.
Revised:  The basic reproductive factor, averaged over France, was R_0= 3.4\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during the lockdown, with small regional variations.
Edit intent: clarity
Original:  On 11 May 2020, the Infection Fatality Rate in France is 4 \pm1\% (90\% c.l.) and constant , while the Feverish vastly outnumber the Asymptomatic, contrary to the early phases.
Revised:  On 11 May 2020, the Infection Fatality Rate in France was 4 \pm1\% (90\% c.l.) and constant , while the Feverish vastly outnumber the Asymptomatic, contrary to the early phases.
Edit intent: fluency
Original:  On 11 May 2020, the Infection Fatality Rate in France is 4 \pm1\% (90\% c.l.) and constant , while the Feverish vastly outnumber the Asymptomatic, contrary to the early phases.
Revised:  On 11 May 2020, the Infection Fatality Rate in France is 4 \pm1\% (90\% c.l.) , while the Feverish vastly outnumber the Asymptomatic, contrary to the early phases.
Edit intent: clarity
Original:  The fraction of immunized people reached a plateau below 1\% throughout France (3\% in Paris) by late April 2020 (95\% c.l.), suggesting a lack of herd immunity and that a second wave of the pandemic is possible during the partial lifting of the national lockdown. After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 is 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
Revised:  The fraction of immunized people reached a plateau below 1\% throughout France (3\% in Paris) by late April 2020 (95\% c.l.), suggesting a lack of herd immunity . The widespread availability of face masks on 11 May, when the lockdown was partially lifted, should keep R_0 below unity if at least 46\% of the population wear them outside their home. Otherwise, without enhanced other social distancing, a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 is 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
Edit intent: meaning-changed
Original:  After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 is 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
Revised:  After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave is inevitable and cause the number of deaths to triple between early May and October ( if R_0 is 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
Edit intent: meaning-changed
Original:  After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 is 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
Revised:  After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 = 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
Edit intent: clarity
Original:  After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 is 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
Revised:  After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 is 1.2 ) or even late June (if R_0=2) .
Edit intent: meaning-changed
Original:  In "bidding games",{\em   in each turn, we hold an 'auction' (bidding) to determine which player moves the token . The players simultaneously submit bids and the higher bidder moves the token.
Revised:  In "bidding games",{\em in each turn, we hold an 'auction' (bidding) to determine which player moves the token : both players simultaneously submit bids and the higher bidder moves the token.
Edit intent: clarity
Original:  Several different payment schemeshave been considered. In "{\em first-price " bidding , only the higher bidder pays his bid , while in "{\em all-pay " bidding, both players pay their bids.
Revised:  The bidding mechanisms differ in their payment schemes. Bidding games were largely studied with variants of{\em first-price " bidding , only the higher bidder pays his bid , while in "{\em all-pay " bidding, both players pay their bids.
Edit intent: clarity
Original:  Bidding games were largely studied with variants of first-price bidding. In this work, we study , for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em   mathematical properties of their first-price counterparts .
Revised:  Bidding games were largely studied with variants of first-price counterparts. We study for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em mathematical properties of their first-price counterparts .
Edit intent: clarity
Original:  In this work, we study , for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em   mathematical properties of their first-price counterparts .
Revised:  In this work, we study , for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em mean-payoff mathematical properties of their first-price counterparts .
Edit intent: meaning-changed
Original:  In this work, we study , for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em   mathematical properties of their first-price counterparts .
Revised:  In this work, we study , for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em objectives: we portray a complete picture for games played on strongly-connected graphs. We study both pure (deterministic) and mixed (probabilistic) strategies and completely characterize the optimal sure and almost-sure (with probability 1) payoffs that the players can respectively guarantee. We show that mean-payoff games under all-pay bidding exhibit the intriguing mathematical properties of their first-price counterparts .
Edit intent: meaning-changed
Original:  In this work, we study , for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em   mathematical properties of their first-price counterparts . This is in stark contrast with reachability games, which are known to be much more complicated under{\em   all-pay bidding than first-price bidding.
Revised:  In this work, we study , for the first time, infinite-duration all-pay bidding games , and show that they exhibit the elegant{\em mathematical properties of their first-price counterparts ; namely, an equivalence with{\em all-pay bidding than first-price bidding.
Edit intent: meaning-changed
Original:  This is in stark contrast with reachability games, which are known to be much more complicated under{\em   all-pay bidding than first-price bidding.
Revised:  This is in stark contrast with reachability games, which are known to be much more complicated under{\em in which in each turn, the player who moves is selected according to a (biased) coin toss. The equivalences for all-pay bidding than first-price bidding.
Edit intent: meaning-changed
Original:  This is in stark contrast with reachability games, which are known to be much more complicated under{\em   all-pay bidding than first-price bidding. Another orthogonal distinction between the bidding rules is in the recipient of the payments: in "Richman" bidding, the bids are paid to the other player , and in "poorman" bidding, the bids are paid to the 'bank'. We focus on strongly-connected games with "mean-payoff" and "parity" objectives. We completely solve all-pay Richman games: a simple argument shows that deterministic strategies cannot guarantee anything in this model, and it is technically much more challenging to find optimal probabilistic strategies that achieve the same expected guarantees in a game as can be obtained with deterministic strategies under first-price bidding. Under poorman all-pay bidding , in contrast to Richman bidding, deterministic strategies are useful and guarantee a payoff that is only slightly lower than the optimal payoff under first-price poorman bidding.
Revised:  This is in stark contrast with reachability games, which are known to be much more complicated under{\em all-pay bidding , in contrast to Richman bidding, deterministic strategies are useful and guarantee a payoff that is only slightly lower than the optimal payoff under first-price poorman bidding.
Edit intent: meaning-changed
Original:  Under poorman all-pay bidding , in contrast to Richman bidding, deterministic strategies are useful and guarantee a payoff that is only slightly lower than the optimal payoff under first-price poorman bidding. Our proofs are constructive and based on new and significantly simpler constructions for first-price bidding.
Revised:  Under poorman all-pay bidding are more intricate and unexpected than for first-price bidding.
Edit intent: coherence
Original: In this work, we are interested in the large graph similarity computation problem , which is one of the most important graph-based problems. Traditional techniques to compute the exact or approximate values of Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) require at least polynomial time complexity over node numbers thus are not able to handle this problem when the numbers of nodes are large .
Revised: In this work, we are interested in the large graph similarity computation problem and propose a novel ``embedding-coarsening-matching'' learning framework, which outperforms state-of-the-art methods in this task and has significant improvement in time efficiency. Graph similarity computation for metrics such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) require at least polynomial time complexity over node numbers thus are not able to handle this problem when the numbers of nodes are large .
Edit intent: meaning-changed
Original:  Traditional techniques to compute the exact or approximate values of Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) require at least polynomial time complexity over node numbers thus are not able to handle this problem when the numbers of nodes are large .
Revised:  Traditional techniques to compute the exact or approximate values of Graph Edit Distance (GED) is typically NP-hard, and existing heuristics-based algorithms usually achieves a unsatisfactory trade-off between accuracy and efficiency .
Edit intent: meaning-changed
Original:  However, when we look into these techniques and classify them to embedding models and matching models , problems arise . Embedding models can be quite fast but perform poorly due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification ,
Revised:  However, when we look into these techniques and classify them to embedding models and matching models . Embedding models can be quite fast but perform poorly due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification ,
Edit intent: clarity
Original:  problems arise . Embedding models can be quite fast but perform poorly due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification ,
Revised:  problems arise . Embedding models such as GCN-Mean and GCN-Max, which directly map graphs to respective feature vectors, run faster but the performance is usually poor due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification ,
Edit intent: meaning-changed
Original:  problems arise . Embedding models can be quite fast but perform poorly due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification ,
Revised:  problems arise . Embedding models can be quite fast but perform poorly due to the lack of interactions across graphs. Matching models such as GMN, whose encoding process involves interaction across the two graphs, are more accurate but interaction between whole graphs brings a significant increase in time consumption (at least quadratic time complexity over number of nodes). Inspired by large biological molecular identification ,
Edit intent: meaning-changed
Original:  problems arise . Embedding models can be quite fast but perform poorly due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification , where we first maps the whole molecular to molecular groups and then identify them based on the "abstracted smaller molecular", the feature aggregation across two whole graphs is always redundant, especially when the number of nodes is large.
Revised:  problems arise . Embedding models can be quite fast but perform poorly due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification where the whole molecular to molecular groups and then identify them based on the "abstracted smaller molecular", the feature aggregation across two whole graphs is always redundant, especially when the number of nodes is large.
Edit intent: coherence
Original:  where we first maps the whole molecular to molecular groups and then identify them based on the "abstracted smaller molecular", the feature aggregation across two whole graphs is always redundant, especially when the number of nodes is large.
Revised:  where we first maps the whole molecular is first mapped to functional groups and then identify them based on the "abstracted smaller molecular", the feature aggregation across two whole graphs is always redundant, especially when the number of nodes is large.
Edit intent: meaning-changed
Original:  where we first maps the whole molecular to molecular groups and then identify them based on the "abstracted smaller molecular", the feature aggregation across two whole graphs is always redundant, especially when the number of nodes is large. Thus we here present a novel framework for large graph similarity computation problem. We first embed and coarsen the large graphs to "abstracted smaller graphs " with denser local topology , similar to molecular groups in biological concept.
Revised:  where we first maps the whole molecular to molecular groups and then identified based on these functional groups, our ``embedding-coarsening-matching'' learning framework first embeds and coarsens large graphs to "abstracted smaller graphs " with denser local topology , similar to molecular groups in biological concept.
Edit intent: clarity
Original:  We first embed and coarsen the large graphs to "abstracted smaller graphs " with denser local topology , similar to molecular groups in biological concept. Then we aggregate both the internal features in "abstracted smaller graphs " and external features across "abstracted smaller graph pair", leading to feature vectors for each graph, with which we calculate the final similarity score.
Revised:  We first embed and coarsen the large graphs to "abstracted smaller graphs " with denser local topology and then matching mechanism is deployed on the coarsened graphs for the final similarity score.
Edit intent: clarity
Original:  Then we aggregate both the internal features in "abstracted smaller graphs " and external features across "abstracted smaller graph pair", leading to feature vectors for each graph, with which we calculate the final similarity score. Experiments demonstrate that our proposed framework outperforms state-of-the-art methods in graph similarity computation tasks and has significant improvement in time efficiency .
Revised:  Then we aggregate both the internal features in "abstracted smaller graphs " and external features across "abstracted smaller graph pair", leading to feature vectors for each graph, with which we calculate the final similarity scores. Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework outperforms state-of-the-art methods in graph similarity computation tasks and has significant improvement in time efficiency .
Edit intent: coherence
Original: Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT  have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature .
Revised: Bidirectional Encoder Representations from Transformers (BERT) models for medical specialties, such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature .
Edit intent: fluency
Original: Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT  have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature .
Revised: Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT , have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature .
Edit intent: fluency
Original: Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT  have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature .
Revised: Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature .
Edit intent: clarity