wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We also perform ablation studies which reveal that fundamental frequency is a pertinent feature . We also find that a model trained on the data of one speaker only also works for the other speaker of the same conversation.
Revised:  We also perform ablation studies which reveal that fundamental frequency is a relevant feature for gesture prediction task. In another sub-experiment, we find that including eyebrow movements as acting as beat gesture improves the performance. Besides, we also find that a model trained on the data of one speaker only also works for the other speaker of the same conversation.
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Original:  In this work we propose residual learning from demonstration (rLfD) , a framework that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy.
Revised:  In this work we study ways for adapting dynamic movement primitives (DMP) to improve their performance in the context of contact rich insertion. We propose a framework we refer to as residual learning from demonstration (rLfD) , a framework that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy.
Edit intent: meaning-changed
Original:  In this work we propose residual learning from demonstration (rLfD) , a framework that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy.
Revised:  In this work we propose residual learning from demonstration (rLfD) that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy.
Edit intent: clarity
Original:  We attribute the accuracy gaps to two disadvantages of non-autoregressive models : a) learning simultaneous generation under the overly strong conditional independence assumption; b) lacking explicit target language modeling. In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Revised:  We attribute the accuracy gaps to two disadvantages of non-autoregressive models . Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Edit intent: meaning-changed
Original:  In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Revised:  In this paper , we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Edit intent: meaning-changed
Original:  In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Revised:  In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on three benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Edit intent: clarity
Original:  In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Revised:  In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Edit intent: clarity
Original:  In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency .
Revised:  In this paper , we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time . Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without multiple decoding iterations .
Edit intent: clarity
Original:  In particular, GLAT achieves 30.91 BLEU on WMT 2014 German-English, which narrows the gap between autoregressive models and non-autoregressive models to less than 0.5 BLEU score .
Revised:  In particular, GLAT achieves state-of-the-art results among non-iterative models and even outperforms top iterative counterparts in some specific benchmarks .
Edit intent: clarity
Original: Although non-autoregressive models with one-iteration generation achieve remarkable inference speed-up, they still fall behind their autoregressive counterparts in prediction accuracy. The non-autoregressive models with the best accuracy currently rely on multiple decoding iterations, which largely sacrifice the inference speed of non-autoregressive models. Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations.
Revised: Although non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Model (GLM), a method to learn word interdependency for single-pass parallel generation models. With GLM, we develop Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations.
Edit intent: meaning-changed
Original:  Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations.
Revised:  Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8-15 times speedup . Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations.
Edit intent: meaning-changed
Original:  Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations.
Revised:  Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive models without multiple decoding iterations.
Edit intent: clarity
Original:  Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations. In particular, GLAT achieves state-of-the-art results among non-iterative models and even outperforms top iterative counterparts in some specific benchmarks .
Revised:  Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually . Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9 BLEU points .
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Original: Autonomous car racing raises fundamental robotics challenges such as planning minimum-time trajectories under uncertain dynamics and controlling the car at its friction limits . In this project, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport.
Revised: Autonomous car racing raises fundamental robotics challenges such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling. Besides, the requirement of minimizing the lap time, which is a sparse objective, and the difficulty of collecting training data from human experts have also hindered researchers from directly applying learning-based approaches to solve the problem. In the present work, we propose a learning-based system for autonomous car racing in the top-selling car racing game Gran Turismo Sport.
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Original:  Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks . Our approach makes use of maximum-entropy deep reinforcement learning and a new reward design to train a sensorimotor policy to complete a given race track as fast as possible. We evaluate our approach in three different time trial settings with different cars and tracks. Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers .
Revised:  Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks , which is even used to recruit human race car drivers. Our trained policy achieves autonomous racing performance that goes beyond what had been achieved so far by the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers .
Edit intent: clarity
Original:  Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers .
Revised:  Our results show that the obtained controllers not only beat the built-in AI, and, at the same time, outperforms the fastest driver in a dataset of personal best lap times of over 50,000 human drivers .
Edit intent: clarity
Original:  Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers .
Revised:  Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of over 50,000 human drivers .
Edit intent: clarity
Original:  Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers .
Revised:  Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human players .
Edit intent: clarity
Original:  We present an algorithm that, if one knows the number of required samples q in advance, has an overall cost of O^*(\sqrt q \cdot(n/\sqrt m)), which is strictly preferable to O^*(q\cdot (n/\sqrt m)) cost resulting from q invocations of the algorithm by Eden and Rosenbaum .
Revised:  We present an algorithm that, if one knows the number of required samples q in advance, has an overall cost that is sublinear in q, namely, O^*(\sqrt q \cdot(n/\sqrt m)), which is strictly preferable to O^*(q\cdot (n/\sqrt m)) cost resulting from q invocations of the algorithm by Eden and Rosenbaum .
Edit intent: meaning-changed
Original:  We present an algorithm that, if one knows the number of required samples q in advance, has an overall cost of O^*(\sqrt q \cdot(n/\sqrt m)), which is strictly preferable to O^*(q\cdot (n/\sqrt m)) cost resulting from q invocations of the algorithm by Eden and Rosenbaum . More generally, for an input parameter x>1, our algorithm has a preprocessing phase with O^*(n/(x\cdot d_{avg)) cost, which then allows an O(x/\epsilon) per-sample cost, where d_{avg} denotes the average degree of the graph} .
Revised:  We present an algorithm that, if one knows the number of required samples q in advance, has an overall cost of O^*(\sqrt q \cdot(n/\sqrt m)), which is strictly preferable to O^*(q\cdot (n/\sqrt m)) cost resulting from q invocations of the algorithm by Eden and Rosenbaum )) cost, which then allows an O(x/\epsilon) per-sample cost, where d_{avg} denotes the average degree of the graph} .
Edit intent: meaning-changed
Original:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses poly \poly (1/\epsilon) and poly \poly (\log n) dependencies).
Revised:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses \poly (1/\epsilon) and poly \poly (\log n) dependencies).
Edit intent: fluency
Original:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses poly \poly (1/\epsilon) and poly \poly (\log n) dependencies).
Revised:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses poly \poly (1/\epsilon) and \poly (\log n) dependencies).
Edit intent: fluency
Original:  We present an algorithm that, if one knows the number of required samples q in advance, has an overall cost that is sublinear in q, namely, O^*(\sqrt q \cdot(n/\sqrt m)), which is strictly preferable to O^*(q\cdot (n/\sqrt m)) cost resulting from q invocations of the algorithm by Eden and Rosenbaum  .
Revised:  We present an algorithm that, if one knows the number of required samples q in advance, has an overall cost that is sublinear in q, namely, O^*(\sqrt q \cdot(n/\sqrt m)), which is strictly preferable to O^*(q\cdot (n/\sqrt m)) cost resulting from q invocations of the algorithm by Eden and Rosenbaum . Subsequent to a preliminary version of this work, T\v{e .
Edit intent: meaning-changed
Original:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses %DIFDELCMD < \poly%%%  (1/\epsilon) and %DIFDELCMD < \poly%%%  (\log n) dependencies).
Revised:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses %DIFDELCMD < \poly%%% poly (1/\epsilon) and %DIFDELCMD < \poly%%% (\log n) dependencies).
Edit intent: meaning-changed
Original:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses %DIFDELCMD < \poly%%%  (1/\epsilon) and %DIFDELCMD < \poly%%%  (\log n) dependencies).
Revised:  Eden and Rosenbaum provided upper and lower bounds of \Theta^*(n/\sqrt m) for sampling a single edge in general graphs (where O^*(\cdot) suppresses %DIFDELCMD < \poly%%% (1/\epsilon) and %DIFDELCMD < \poly%%% poly (\log n) dependencies).
Edit intent: meaning-changed
Original:  This extracts some of the same short-term variability that is resynthesized, and it shows that fluctuations with periods between 8 and 21 days are present in U.S. mortality  data.
Revised:  This extracts some of the same short-term variability that is resynthesized, and it shows that fluctuations with periods between 8 and 21 days are present in U.S. mortality data. These methods have applications that include modeling epidemiological time-series data as well as identifying less obvious properties of the data.
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Original: We study the problem of robustly estimating the mean of a d-dimensional distribution given N examples, where \varepsilon N examples may be arbitrarily corrupted and most coordinates of every example may be missing  .
Revised: We study the problem of robustly estimating the mean of a d-dimensional distribution given N examples, where \varepsilon N examples may be arbitrarily corrupted and most coordinates of every example may be missing and \varepsilon N examples may be arbitrarily corrupted .
Edit intent: meaning-changed
Original: It has become increasingly clear that the COVID-19 epidemic is characterized by overdispersion whereby the majority of the transmission is driven by a minority of infected individuals. Such a strong departure from the homogeneity assumptions of the traditional well-mixed compartment model is usually hypothesized to be the result of short-term super-spreader events , such as an individual's extreme rate of virus shedding at the peak of infectivity while attending a large gathering without appropriate mitigation. However, we demonstrate that the spread of epidemics is primarily sensitive to long-term, or persistent heterogeneity of individual susceptibility or infectivity. We demonstrate how to incorporate this heterogeneity into a wide class of epidemiological models , and derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of short-term overdispersion: (1) It results in a major modification of the early epidemic dynamics;
Revised: Epidemics generally spread through a succession of waves that reflect factors on multiple timescales. On short timescales, super-spreading events lead to burstiness and overdispersion, while long-term persistent heterogeneity in susceptibility is expected to lead to a reduction in the infection peak and the herd immunity threshold (HIT). Here, we develop a general approach to encompass both timescales, including time variations in individual social activity, and demonstrate how to incorporate this heterogeneity into a wide class of epidemiological models , and derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of short-term overdispersion: (1) It results in a major modification of the early epidemic dynamics;
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Original:  We demonstrate how to incorporate this heterogeneity into a wide class of epidemiological models , and derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of short-term overdispersion: (1) It results in a major modification of the early epidemic dynamics;
Revised:  We demonstrate how to incorporate them phenomenologically into a wide class of epidemiological models , and derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of short-term overdispersion: (1) It results in a major modification of the early epidemic dynamics;
Edit intent: meaning-changed
Original:  We demonstrate how to incorporate this heterogeneity into a wide class of epidemiological models , and derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of short-term overdispersion: (1) It results in a major modification of the early epidemic dynamics;
Revised:  We demonstrate how to incorporate this heterogeneity into a wide class of epidemiological models through parameterization. We derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of short-term overdispersion: (1) It results in a major modification of the early epidemic dynamics;
Edit intent: meaning-changed