wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this work , we propose a REPresentation-And-INstance Transfer algorithm (REPAINT) for deep actor-critic reinforcement learningparadigm. In representation transfer, we adopt a kickstarted training method using a pre-trained teacher policy by introducing an auxiliary cross-entropy loss.
Revised:  In this work , we propose a REPresentation-And-INstance Transfer algorithm (REPAINT) algorithm for knowledge transfer in deep reinforcement learning. REPAINT not only transfers the representation of a pre-trained teacher policy by introducing an auxiliary cross-entropy loss.
Edit intent: clarity
Original:  In representation transfer, we adopt a kickstarted training method using a pre-trained teacher policy by introducing an auxiliary cross-entropy loss. In instance transfer, we develop a sampling approach, i.e., advantage-based experience replay, on transitions collected following the teacher policy , where only the samples with high advantage estimates are retained for policy update.
Revised:  In representation transfer, we adopt a kickstarted training method using a pre-trained teacher policy in the on-policy learning, but also uses an advantage-based experience replay, on transitions collected following the teacher policy , where only the samples with high advantage estimates are retained for policy update.
Edit intent: meaning-changed
Original:  In instance transfer, we develop a sampling approach, i.e., advantage-based experience replay, on transitions collected following the teacher policy , where only the samples with high advantage estimates are retained for policy update.
Revised:  In instance transfer, we develop a sampling approach, i.e., advantage-based experience selection approach to transfer useful samples collected following the teacher policy , where only the samples with high advantage estimates are retained for policy update.
Edit intent: meaning-changed
Original:  In instance transfer, we develop a sampling approach, i.e., advantage-based experience replay, on transitions collected following the teacher policy , where only the samples with high advantage estimates are retained for policy update. We consider both learningan unseen target task by transferring from previously learned teacher tasks and learning a partially unseen task composed of multiple sub-tasks by transferring from a pre-learned teacher sub-task. In several benchmark experiments, REPAINT significantly reduces the total training time and improves the asymptotic performance compared totraining with no prior knowledge and other baselines .
Revised:  In instance transfer, we develop a sampling approach, i.e., advantage-based experience replay, on transitions collected following the teacher policy in the off-policy learning. Our experimental results on several benchmark tasks show that REPAINT significantly reduces the total training time and improves the asymptotic performance compared totraining with no prior knowledge and other baselines .
Edit intent: clarity
Original:  In several benchmark experiments, REPAINT significantly reduces the total training time and improves the asymptotic performance compared totraining with no prior knowledge and other baselines .
Revised:  In several benchmark experiments, REPAINT significantly reduces the total training time in generic cases of task similarity. In particular, when the source tasks are dissimilar to, or sub-tasks of, the target tasks, REPAINT outperforms other baselines in both training-time reduction and asymptotic performance of return scores .
Edit intent: meaning-changed
Original:  To encourage research on pretraining and transfer learning on NLG models, we make GLGE publicly available and build a leaderboard with strong baselines including MASS, BART, and ProphetNet %DIFDELCMD < \footnote{%%%  The source code and dataset will be publicly available at URL
Revised:  To encourage research on pretraining and transfer learning on NLG models, we make GLGE publicly available and build a leaderboard with strong baselines including MASS, BART, and ProphetNet %DIFDELCMD < \footnote{%%% ( The source code and dataset will be publicly available at URL
Edit intent: fluency
Original: Using novel nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018.
Revised: Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018.
Edit intent: meaning-changed
Original:  Specifically, the former predicts the proportion of time individuals will be on income support in the next four years with greater accuracy, by a magnitude of at least 22\% (14 percentage points increase in the R2), compared to the latter.
Revised:  Specifically, the former predicts the proportion of time individuals are on income support in the next four years with greater accuracy, by a magnitude of at least 22\% (14 percentage points increase in the R2), compared to the latter.
Edit intent: fluency
Original:  Specifically, the former predicts the proportion of time individuals will be on income support in the next four years with greater accuracy, by a magnitude of at least 22\% (14 percentage points increase in the R2), compared to the latter.
Revised:  Specifically, the former predicts the proportion of time individuals will be on income support in the subsequent four years with greater accuracy, by a magnitude of at least 22\% (14 percentage points increase in the R2), compared to the latter.
Edit intent: fluency
Original:  This gain can be achieved at little extra cost to practitioners since it uses data currently available to them . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system .
Revised:  This gain can be achieved at no extra cost to practitioners since it uses data currently available to them . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system .
Edit intent: clarity
Original:  This gain can be achieved at little extra cost to practitioners since it uses data currently available to them . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system .
Revised:  This gain can be achieved at little extra cost to practitioners since the algorithms use administrative data currently available to them . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system .
Edit intent: clarity
Original:  This gain can be achieved at little extra cost to practitioners since it uses data currently available to them . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system .
Revised:  This gain can be achieved at little extra cost to practitioners since it uses data currently available to caseworkers . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system .
Edit intent: clarity
Original:  This gain can be achieved at little extra cost to practitioners since it uses data currently available to them . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system .
Revised:  This gain can be achieved at little extra cost to practitioners since it uses data currently available to them . Consequently, our machine learning algorithms can improve the detection of long-term income support recipients , which can potentially provide governments with large savings in accrued welfare costs .
Edit intent: clarity
Original:  This many-query  task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling  gas transport, are a challenging application for model reduction algorithms.
Revised:  This many-query gas network simulation task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling gas transport, are a challenging application for model reduction algorithms.
Edit intent: meaning-changed
Original:  This many-query  task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling  gas transport, are a challenging application for model reduction algorithms.
Revised:  This many-query task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling natural gas transport, are a challenging application for model reduction algorithms.
Edit intent: meaning-changed
Original:  This many-query gas network simulation task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling natural gas transport, are a challenging application for model  reduction algorithms.
Revised:  This many-query gas network simulation task can be accelerated by model reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling natural gas transport, are a challenging application for model reduction algorithms.
Edit intent: clarity
Original:  This many-query gas network simulation task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling natural gas transport, are a challenging application for model  reduction algorithms.
Revised:  This many-query gas network simulation task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling natural gas transport, are a challenging application for model order reduction algorithms.
Edit intent: fluency
Original:  For this industrial application, we bring together the scientific computing topics of: mathematical modeling of gas transport networks, numerical simulation of hyperbolic partial differential equation, and  model reduction for nonlinear parametric systems.
Revised:  For this industrial application, we bring together the scientific computing topics of: mathematical modeling of gas transport networks, numerical simulation of hyperbolic partial differential equation, and parametric model reduction for nonlinear parametric systems.
Edit intent: meaning-changed
Original:  Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the controllers into stages of exploration, Q-value estimation as well as policy update .
Revised:  Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the controllers into stages of exploration, policy update, and most importantly, learning a heuristic value function that naturally interpolates along task trajectories .
Edit intent: meaning-changed
Original: Navigation policies are commonly learned on idealized cylinder agents in simulation , without modelling complex dynamics, like contact dynamics, arising from the interaction between the robot and the environment. Such policies perform poorly when deployed on complex and dynamic robots , such as legged robots .
Revised: Navigation policies are commonly learned on idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation policies on legged robots can be challenging due to their complex dynamics, and the large dynamical difference between cylinder agents and legged systems .
Edit intent: clarity
Original:  Once such a policy is learned on one legged robot, it does not directly generalize to a different robot due to dynamical differences, which increases the cost of learning such a policy on new robots. To overcome this challenge , we learn dynamics-aware navigation policies across multiple robots with robot-specific embeddings , which enable generalization to new unseen robots .
Revised:  To enable transfer of policies learned in simulation to new legged robots and hardware , we learn dynamics-aware navigation policies across multiple robots with robot-specific embeddings , which enable generalization to new unseen robots .
Edit intent: meaning-changed
Original:  At test time, we study the performance of our learned policy on two new legged robots  (Laikago, 4-legged Daisy) and show that our learned policy can sample-efficiently generalize to previously unseen  robots.
Revised:  At test time, we study the performance of our learned policy on two new legged robots in simulation (Laikago, 4-legged Daisy) and show that our learned policy can sample-efficiently generalize to previously unseen robots.
Edit intent: meaning-changed
Original:  At test time, we study the performance of our learned policy on two new legged robots  (Laikago, 4-legged Daisy) and show that our learned policy can sample-efficiently generalize to previously unseen  robots.
Revised:  At test time, we study the performance of our learned policy on two new legged robots (Laikago, 4-legged Daisy) , and one real-world quadrupedal robot (A1). Our experiments show that our learned policy can sample-efficiently generalize to previously unseen robots.
Edit intent: meaning-changed
Original:  At test time, we study the performance of our learned policy on two new legged robots  (Laikago, 4-legged Daisy) and show that our learned policy can sample-efficiently generalize to previously unseen  robots.
Revised:  At test time, we study the performance of our learned policy on two new legged robots (Laikago, 4-legged Daisy) and show that our learned policy can sample-efficiently generalize to previously unseen robots, and enable sim-to-real transfer of navigation policies for legged robots.
Edit intent: meaning-changed
Original:  Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, when is DA obviously strategyproof Li17  ? We answer this question by completely characterizing those priorities which make DA obviously strategyproof (OSP).
Revised:  Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, Li17 ? We answer this question by completely characterizing those priorities which make DA obviously strategyproof (OSP).
Edit intent: coherence
Original:  Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, when is DA obviously strategyproof Li17  ? We answer this question by completely characterizing those priorities which make DA obviously strategyproof (OSP).
Revised:  Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, when is DA obviously strategyproof when is DA obviously strategyproof ? We answer this question by completely characterizing those priorities which make DA obviously strategyproof (OSP).
Edit intent: meaning-changed
Original:  Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, when is DA obviously strategyproof Li17  ? We answer this question by completely characterizing those priorities which make DA obviously strategyproof (OSP).
Revised:  Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, when is DA obviously strategyproof Li17 (Li, 2017) ? We answer this question by completely characterizing those priorities which make DA obviously strategyproof (OSP).
Edit intent: meaning-changed
Original:  This answers an open question of AG18%DIFDELCMD < ]%%% . We find that when DA is OSP, priorities are either acyclic Erg02 , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Revised:  This AG18%DIFDELCMD < ]%%% . We find that when DA is OSP, priorities are either acyclic Erg02 , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Edit intent: coherence
Original:  This answers an open question of AG18%DIFDELCMD < ]%%% . We find that when DA is OSP, priorities are either acyclic Erg02 , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Revised:  This answers an open question of %DIFDELCMD < ]%%% . We find that when DA is OSP, priorities are either acyclic Erg02 , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Edit intent: meaning-changed
Original:  This answers an open question of AG18%DIFDELCMD < ]%%% . We find that when DA is OSP, priorities are either acyclic Erg02 , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Revised:  This answers an open question of AG18%DIFDELCMD < ]%%% solves an open problem of Ashlagi and Gonczarowski, 2018. We find that when DA is OSP, priorities are either acyclic Erg02 , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Edit intent: meaning-changed
Original:  This answers an open question of AG18%DIFDELCMD < ]%%% . We find that when DA is OSP, priorities are either acyclic Erg02 , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Revised:  This answers an open question of AG18%DIFDELCMD < ]%%% . We find that when DA is OSP, priorities are either acyclic (Ergin, 2002), a restrictive condition which allows priorities to only differ on only two agents at a time , or contain an extremely limited cyclic pattern where all priority lists are identical except for exactly two.
Edit intent: meaning-changed
Original: Safety is an important topic in autonomous driving since any collision may cause serious damage to people and the environment . Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance.
Revised: Safety is an important topic in autonomous driving since any collision may cause serious injury to people and the environment . Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance.
Edit intent: clarity
Original: Safety is an important topic in autonomous driving since any collision may cause serious damage to people and the environment . Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance.
Revised: Safety is an important topic in autonomous driving since any collision may cause serious damage to people and damage to property . Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance.
Edit intent: clarity
Original:  Through simulations in a T-intersection and an 8-way roundabout, we demonstrate that our prediction-based reachability method largely avoids collision between two interacting cars and reduces the conservatism that the safety controller brings to the car's original operations .
Revised:  Through simulations in a T-intersection and an 8-way roundabout, we demonstrate that our prediction-based reachability method largely avoids collision between two interacting cars and reduces the conservatism that the safety controller brings to the car's original operation .
Edit intent: fluency
Original:  Natural language processing (NLP) , also called computational linguistics or human language technologies, is the sub-field of artificial intelligence (AI) focused on modeling natural languages to build applications such as speech recognition and synthesis, machine translation, optical character recognition (OCR), sentiment analysis (SA), question answering, dialogue systems, etc.
Revised:  Natural language processing (NLP) is the sub-field of artificial intelligence (AI) focused on modeling natural languages to build applications such as speech recognition and synthesis, machine translation, optical character recognition (OCR), sentiment analysis (SA), question answering, dialogue systems, etc.
Edit intent: clarity
Original:  NLP is a highly interdisciplinary field with connections to computer science, linguistics, cognitive science, psychology, mathematics and others  .
Revised:  NLP is a highly interdisciplinary field with connections to computer science, linguistics, cognitive science, psychology, mathematics and others . Some of the earliest AI applications were in NLP (e.g., machine translation); and the last decade (2010-2020) in particular has witnessed an incredible increase in quality, matched with a rise in public awareness, use, and expectations of what may have seemed like science fiction in the past. NLP researchers pride themselves on developing language independent models and tools that can be applied to all human languages, e.g. machine translation systems can be built for a variety of languages using the same basic mechanisms and models. However, the reality is that some languages do get more attention (e.g., English and Chinese) than others (e.g., Hindi and Swahili). Arabic, the primary language of the Arab world and the religious language of millions of non-Arab Muslims is somewhere in the middle of this continuum. Though Arabic NLP has many challenges, it has seen many successes and developments. Next we discuss Arabic's main challenges as a necessary background, and we present a brief history of Arabic NLP. We then survey a number of its research areas, and close with a critical discussion of the future of Arabic NLP .
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Original:  Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel KPSS-type tests for examining some essential properties of cointegrated  time series.
Revised:  Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel FPCA-based tests for examining some essential properties of cointegrated time series.
Edit intent: clarity
Original:  Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel KPSS-type tests for examining some essential properties of cointegrated  time series.
Revised:  Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel KPSS-type tests for examining some essential properties of cointegrated functional time series.
Edit intent: meaning-changed