wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes. To further reduce the dimensionality, we also postulate that the stationary part of the series is a nonsingular linear transformation of certain common factors and idiosyncratic white noise components as in Gao and Tsay (2019a, b)
Revised:  For a p-dimensional unit-root process , we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary common factors, which are dynamically dependent, and some idiosyncratic white noise components. For the stationary components, we assume that the factor process captures the temporal-dependence and the idiosyncratic white noise components as in Gao and Tsay (2019a, b)
Edit intent: meaning-changed
Original:  To further reduce the dimensionality, we also postulate that the stationary part of the series is a nonsingular linear transformation of certain common factors and idiosyncratic white noise components as in Gao and Tsay (2019a, b)
Revised:  To further reduce the dimensionality, we also postulate that the stationary part of the series is a nonsingular linear transformation of certain common factors and idiosyncratic white noise series explains, jointly with the factors, the cross-sectional dependence
Edit intent: meaning-changed
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Revised:  . The estimation of nonsingular linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Edit intent: meaning-changed
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Revised:  . The estimation of linear loading spaces is carried out in two steps. First, we use an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Edit intent: clarity
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Revised:  . The estimation of linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of a nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Edit intent: clarity
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Revised:  . The estimation of linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix of the data to separate the unit-root processes from the stationary ones and a modified method to specify the number of unit roots. We then employ another eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Edit intent: meaning-changed
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Revised:  . The estimation of linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis to identify the stationary common factors and the white noise series. We propose a new procedure to specify the number of white noise series and, hence, the number of stationary common factors, establish asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Edit intent: meaning-changed
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Revised:  . The estimation of linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method for both fixed p and diverging p as the sample size n tends to infinity.
Edit intent: clarity
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity.
Revised:  . The estimation of linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed and diverging p as the sample size n tends to infinity.
Edit intent: fluency
Original:  . The estimation of  linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n tends to infinity. Both simulated and real examples are used to demonstrate the performance of the proposed method in finite samples  .
Revised:  . The estimation of linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix , and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis . Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n increases, and use simulation and a real example to demonstrate the performance of the proposed method in finite samples .
Edit intent: clarity
Original:  Both simulated and real examples are used to demonstrate the performance of the proposed method in finite samples  .
Revised:  Both simulated and real examples are used to demonstrate the performance of the proposed method in finite samples . We also compare our method with some commonly used ones in the literature regarding the forecast ability of the extracted factors and find that the proposed method performs well in out-of-sample forecasting of a 508-dimensional PM_{2.5 .
Edit intent: meaning-changed
Original:  We also show that capital gain sharing features, through prepayment penalties in high house price states, are ineffective , as they virtually eliminate prepayment in such states. For typical foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed rate mortgage at contract rate spreads of approximately 50-100 basis points , depending on how far prices must fall before adjustments are made.
Revised:  We also show that capital gain sharing features, through prepayment penalties in high house price states, are ineffective , as they virtually eliminate prepayment . For observed foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed rate mortgage at contract rate spreads of approximately 50-100 basis points , depending on how far prices must fall before adjustments are made.
Edit intent: clarity
Original:  For typical foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed rate mortgage at contract rate spreads of approximately 50-100 basis points , depending on how far prices must fall before adjustments are made.
Revised:  For typical foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed rate contracts at mortgage rate spreads between 50-100 basis points , depending on how far prices must fall before adjustments are made.
Edit intent: clarity
Original:  For typical foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed rate mortgage at contract rate spreads of approximately 50-100 basis points , depending on how far prices must fall before adjustments are made. Furthermore, these spreads rapidly decrease with the borrower utility from home ownership. Our results are obtained using American options pricing methods , in a  model with diffusive home prices , and either diffusive or constant interest rates.
Revised:  For typical foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed rate mortgage at contract rate spreads of approximately 50-100 basis points . Results are obtained using American options pricing methods , in a model with diffusive home prices , and either diffusive or constant interest rates.
Edit intent: coherence
Original: We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.
Revised: We propose a new task of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.
Edit intent: clarity
Original: We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.
Revised: We focus on the study of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.
Edit intent: clarity
Original:  To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot).
Revised:  To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot).
Edit intent: fluency
Original: The outbreak of the novel coronavirus (COVID-19) is considered an exogenous risk, which has caused unprecedented disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines in history.
Revised: Markets are subjected to both endogenous and exogenous risks that have caused disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines in history.
Edit intent: clarity
Original: The outbreak of the novel coronavirus (COVID-19) is considered an exogenous risk, which has caused unprecedented disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines in history.
Revised: The outbreak of the novel coronavirus (COVID-19) is considered an exogenous risk, which has caused unprecedented disruptions to financial and economic markets around the globe, leading eventually to fast stock market declines in history.
Edit intent: clarity
Original: The outbreak of the novel coronavirus (COVID-19) is considered an exogenous risk, which has caused unprecedented disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines in history. However, in the pastwe have seen the market recover and we can expect the market to recover again, and on this basis we assume the Standard and Poor's 500 (S  P500) index will reach a minimum before rising again in the not-too-distant future.
Revised: The outbreak of the novel coronavirus (COVID-19) is considered an exogenous risk, which has caused unprecedented disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines . In the past, markets have recovered after any economic disruption. On this basis, we focus on the outbreak of COVID-19 as a case study of an exogenous risk and analyze its impact on the Standard and Poor's 500 (S P500) index will reach a minimum before rising again in the not-too-distant future.
Edit intent: meaning-changed
Original:  However, in the pastwe have seen the market recover and we can expect the market to recover again, and on this basis we assume the Standard and Poor's 500 (S  P500) index will reach a minimum before rising again in the not-too-distant future.
Revised:  However, in the pastwe have seen the market recover and we can expect the market to recover again, and on this basis we assume the Standard and Poor's 500 (S \& P500) index will reach a minimum before rising again in the not-too-distant future.
Edit intent: fluency
Original:  However, in the pastwe have seen the market recover and we can expect the market to recover again, and on this basis we assume the Standard and Poor's 500 (S  P500) index will reach a minimum before rising again in the not-too-distant future.
Revised:  However, in the pastwe have seen the market recover and we can expect the market to recover again, and on this basis we assume the Standard and Poor's 500 (S P500) index . We assumed that the S\&P500 index reaches a minimum before rising again in the not-too-distant future.
Edit intent: coherence
Original:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Revised:  Here we present two cases to forecast the S\& P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Edit intent: meaning-changed
Original:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Revised:  Here we present four forecast models of the S P500 index. The first case uses an estimation of expected deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Edit intent: meaning-changed
Original:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Revised:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington . For the second case, it is assumed that the peak number of deaths will occur 2-months consideration since the first confirmed case occured in USA.
Edit intent: meaning-changed
Original:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Revised:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months since the first confirmed case occured in USA.
Edit intent: clarity
Original:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Revised:  Here we present four forecast models of the S P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occurred in the USA.
Edit intent: fluency
Original:  Our forecast was made on the premise that : (a ) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S P500 over the last 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > of accuracy.
Revised:  Our forecast was made on the premise that any market response can be decomposed into an overall deterministic trend and a stochastic term. The prediction was based on the deterministic part and for this case study is approximated by the extrapolation of the S\&P500 data trend in the initial stages of the outbreak. The stochastic fluctuations have the same structure as the one derived from the S P500 over the last 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > of accuracy.
Edit intent: meaning-changed
Original:  Our forecast was made on the premise that : (a ) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S P500 over the last 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > of accuracy.
Revised:  Our forecast was made on the premise that : (a ) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the P500 over the last 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > of accuracy.
Edit intent: clarity
Original:  Our forecast was made on the premise that : (a ) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S P500 over the last 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > of accuracy.
Revised:  Our forecast was made on the premise that : (a ) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S past 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > of accuracy.
Edit intent: clarity
Original:  Our forecast was made on the premise that : (a ) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S P500 over the last 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > of accuracy.
Revised:  Our forecast was made on the premise that : (a ) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S P500 over the last 24 years. %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% A reasonable forecast was achieved with 85\\%DIF > of accuracy.
Edit intent: meaning-changed
Original: Neural network based approaches to  natural language generation (NLG) have gained popularity in recent years . The goal of the task is to generate a natural language string to realize an input meaning representation , hence large datasets of paired utterances and their meaning representations are used for training the network .
Revised: Neural network based approaches to natural language generation (NLG) have gained popularity in recent years , with the goal of generating a natural language string to realize an input meaning representation , hence large datasets of paired utterances and their meaning representations are used for training the network .
Edit intent: clarity
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 (SG-NLG). Here, the goal is still to generate a natural language prompt, but in SG-NLG, the input MRs are paired with rich schemata providing contextual information. To generate a dataset for SG-NLG we re-purpose 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.
Edit intent: meaning-changed