wanyu/IteraTeR_full_sent huggingface dataset:
Original: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through three aspects: 1) fast model predictive foothold planning,  2) LQR control for robust motion tracking and 3) adaptive feet for terrain adaptation .
Revised: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through three aspects: 1) fast model predictive foothold planning, 2) applying LQR to projected inverse dynamic control for robust motion tracking and 3) adaptive feet for terrain adaptation .
Edit intent: meaning-changed
Original: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through three aspects: 1) fast model predictive foothold planning,  2) LQR control for robust motion tracking and 3) adaptive feet for terrain adaptation .
Revised: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through three aspects: 1) fast model predictive foothold planning, 2) LQR control for robust motion tracking .
Edit intent: coherence
Original:  The LQR optimal gain matrix with non-zero off-diagonal elements leverages the coupling of dynamics to compensate for system underactuation , such as a quadruped robot with passive ankles. The specially designed foot with adaptive sole aims at improving the traversability of rough terrains with rocks, loose gravel and rubble by enlarging the contact surfaces with ground . Experiments on the quadruped ANYmal demonstrate the effectiveness of the proposed method for robust dynamic locomotion given external disturbances and environmental uncertainties.
Revised:  The LQR optimal gain matrix with non-zero off-diagonal elements leverages the coupling of dynamics to compensate for system underactuation . Meanwhile, the projected inverse dynamic control complements the LQR to satisfy inequality constraints. In addition to these contributions, we show robustness of our control framework to unmodeled adaptive feet . Experiments on the quadruped ANYmal demonstrate the effectiveness of the proposed method for robust dynamic locomotion given external disturbances and environmental uncertainties.
Edit intent: coherence
Original:  We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP is competitive with top-down autoregressive parsing. On the test set, SmBoP obtains an EM score of 60.5%DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract \sim5x 
Revised:  We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP %DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract \sim5x 
Edit intent: clarity
Original:  On the test set, SmBoP obtains an EM score of 60.5%DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract \sim5x 
Revised:  On the test set, SmBoP obtains an EM score of 60.5%DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract leads to a 2.2x speed-up in decoding time and a\sim5x 
Edit intent: meaning-changed
Original:  On the test set, SmBoP obtains an EM score of 60.5%DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract \sim5x 
Revised:  On the test set, SmBoP obtains an EM score of 60.5%DIFDELCMD < \\%%% %DIF < , similar to the best published score for a model that does not use database content, which is at 60.6\\%.abstract \sim5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GraPPa.
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Original:  We propose a multi-agent reinforcement learning (MARL) approach that, in contrast to previous work, can adapt to profound changes in the scenario parameters defining the data harvesting mission, such as the number of deployed UAVs, number and position of IoT devices, or the maximum flying time, without the need to perform expensive recomputations or relearn control policies.
Revised:  We propose a multi-agent reinforcement learning (MARL) approach that, in contrast to previous work, can adapt to profound changes in the scenario parameters defining the data harvesting mission, such as the number of deployed UAVs, number , position and data amount of IoT devices, or the maximum flying time, without the need to perform expensive recomputations or relearn control policies.
Edit intent: meaning-changed
Original:  The path planning problem is translated into a decentralized partially observable Markov decision process (Dec-POMDP), which we solve by training a double deep Q-network (DDQN) to approximate the optimal UAV control policy  .
Revised:  The path planning problem is translated into a decentralized partially observable Markov decision process (Dec-POMDP), which we solve through a deep reinforcement learning (DRL) approach, approximating the optimal UAV control policy .
Edit intent: meaning-changed
Original:  The path planning problem is translated into a decentralized partially observable Markov decision process (Dec-POMDP), which we solve by training a double deep Q-network (DDQN) to approximate the optimal UAV control policy  .
Revised:  The path planning problem is translated into a decentralized partially observable Markov decision process (Dec-POMDP), which we solve by training a double deep Q-network (DDQN) to approximate the optimal UAV control policy without prior knowledge of the challenging wireless channel characteristics in dense urban environments .
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Original:  By exploiting global-local maps of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large  state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints  .
Revised:  By exploiting a combination of centered global and local map representations of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints .
Edit intent: meaning-changed
Original:  By exploiting global-local maps of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large  state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints  .
Revised:  By exploiting global-local maps of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large complex environments and state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints .
Edit intent: clarity
Original:  By exploiting global-local maps of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large  state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints  .
Revised:  By exploiting global-local maps of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints . Finally, learning a control policy that generalizes over the scenario parameter space enables us to analyze the influence of individual parameters on collection performance and provide some intuition about system-level benefits .
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Original:  These "nominal" dynamics allow us to perform tasks under ideal conditions , but when unexpected traps arise in execution, we must find a way to adapt our dynamics and control strategy and continue attempting the task.
Revised:  These "nominal" dynamics allow us to perform tasks in scenarios where the dynamics matches the training data , but when unexpected traps arise in execution, we must find a way to adapt our dynamics and control strategy and continue attempting the task.
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Original:  We evaluate our method on simulated planar pushing and peg-in-hole as well as real robot peg-in-hole problems against adaptive control and reinforcement learning baselines, where traps arise due to unexpected obstacles that we only observe through contact.
Revised:  We evaluate our method on simulated planar pushing and peg-in-hole as well as real robot peg-in-hole problems against adaptive control , reinforcement learning, trap-handling baselines, where traps arise due to unexpected obstacles that we only observe through contact.
Edit intent: clarity
Original: Current approaches to open-domain question answering often make crucial assumptions that prevent them from generalizing to real-world settings, including the access to parameterized retrieval systems well-tuned for the task, access to structured metadata like knowledge bases and web links, or a priori knowledge of the complexity of questions to be answered (e.g., single-hop or multi-hop). To address these limitations, we propose a unified system to answer open-domain questions of arbitrary complexity directly from text that works with off-the-shelf retrieval systems on arbitrary text collections .
Revised: We develop a unified system to answer open-domain questions of arbitrary complexity directly from text that works with off-the-shelf retrieval systems on arbitrary text collections .
Edit intent: clarity
Original:  We employ a single multi-task  model to perform all the necessary subtasks---retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents---in an iterative fashion.
Revised:  We employ a single multi-task transformer model to perform all the necessary subtasks---retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents---in an iterative fashion.
Edit intent: meaning-changed
Original:  To emulate a more realistic setting, we also constructed a new unified benchmark by collecting about 200 multi-hop questions that require three Wikipedia pages to answer, and combining them with existing datasets .
Revised:  We avoid making crucial assumptions as previous work that do not transfer well to real-world settings, including exploiting knowledge of the fixed number of retrieval steps required to answer each question or using structured metadata like knowledge bases or web links that have limited availability. Instead, we design a system that would answer open-domain questions on any text collection without prior knowledge of reasoning complexity. To emulate this setting, we also constructed a new unified benchmark by collecting about 200 multi-hop questions that require three Wikipedia pages to answer, and combining them with existing datasets .
Edit intent: meaning-changed
Original:  To emulate a more realistic setting, we also constructed a new unified benchmark by collecting about 200 multi-hop questions that require three Wikipedia pages to answer, and combining them with existing datasets .
Revised:  To emulate a more realistic setting, we construct a new benchmark by combining existing one- and two-step datasets with a new collection of 203 questions that require three Wikipedia pages to answer, and combining them with existing datasets .
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Original: As the excessive pre-training cost arouses the need to improve efficiency , considerable efforts have been made to train BERT progressively--start from an inferior but low-cost model and gradually  increase the computational complexity.
Revised: As the excessive pre-training cost arouses the need to improve efficiency , considerable efforts have been made to train BERT progressively--start from an inferior but low-cost model and gradually grow the model to increase the computational complexity.
Edit intent: clarity
Original:  In light of our analyses, the proposed method CompoundGrow speeds up BERT pre-training by 73.6\% and 82.2\% for the base and large models respectively  while achieving comparable performances . Code will be released for reproduction and future studies.
Revised:  In light of our analyses, the proposed method speeds up BERT pre-training by 73.6\% and 82.2\% for the base and large models respectively while achieving comparable performances . Code will be released for reproduction and future studies.
Edit intent: clarity
Original:  In light of our analyses, the proposed method CompoundGrow speeds up BERT pre-training by 73.6\% and 82.2\% for the base and large models respectively  while achieving comparable performances . Code will be released for reproduction and future studies.
Revised:  In light of our analyses, the proposed method CompoundGrow speeds up BERT pre-training by 73.6\% and 82.2\% for the base and large models respectively , while achieving comparable performances . Code will be released for reproduction and future studies.
Edit intent: fluency
Original:  In light of our analyses, the proposed method CompoundGrow speeds up BERT pre-training by 73.6\% and 82.2\% for the base and large models respectively  while achieving comparable performances . Code will be released for reproduction and future studies.
Revised:  In light of our analyses, the proposed method CompoundGrow speeds up BERT pre-training by 73.6\% and 82.2\% for the base and large models respectively while achieving comparable performances 
Edit intent: coherence
Original:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images ( Olah et al. , 2017 ) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Revised:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al. , 2017 ) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Edit intent: clarity
Original:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images ( Olah et al. , 2017 ) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Revised:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images ( Olah et al. 2017 ) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Edit intent: fluency
Original:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images ( Olah et al. , 2017 ) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Revised:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images ( Olah et al. , 2017 with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Edit intent: fluency
Original:  We find that synthetic images indeed provide helpful information about feature map activations (82 \% accuracy; chance would be 50\%). However, natural images-originally intended to be a baseline-outperform synthetic images by a wide margin (92\% accuracy). Additionally, participants are faster and more confident for natural images, whereas subjective impressions about the interpretability of feature visualization are mixed. The higher informativeness of natural images holds across most layers, for both expert and lay participants as well as for hand- and randomly-picked feature visualizations. Even if only a single reference image is given, synthetic images provide less information than natural images (65\% vs. 73\%). In summary, popular synthetic images from feature visualizations are significantly less informative for assessing CNN activations than natural images. We argue that future visualization methods should improve over this simple baseline.
Revised:  We find that synthetic images indeed provide helpful information about feature map activations (82 
Edit intent: coherence
Original:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al.  2017  with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Revised:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al. ( 2017 with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Edit intent: fluency
Original:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al.  2017  with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Revised:  Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al. 2017 ) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Edit intent: fluency
Original: Generating natural sentences from Knowledge Graph (KG) triples, known as Data-To-Text Generation, is a task with many datasets for which numerous complex systems have been developed. However, no prior work has attempted to perform this generation at scale by converting an entire KG into natural text  .
Revised: Generating natural sentences from Knowledge Graph (KG) triples, known as Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text .
Edit intent: clarity
Original:  In contrast to the many architectures that have been developed to integrate the structural differences between these two sources, our approach converts the KG into the same format as natural text allowing it to be seamlessly plugged into existing natural language systems.
Revised:  In contrast to the many architectures that have been developed to integrate these two sources, our approach converts the KG into the same format as natural text allowing it to be seamlessly plugged into existing natural language systems.
Edit intent: coherence
Original:  In contrast to the many architectures that have been developed to integrate the structural differences between these two sources, our approach converts the KG into the same format as natural text allowing it to be seamlessly plugged into existing natural language systems.
Revised:  In contrast to the many architectures that have been developed to integrate the structural differences between these two sources, our approach converts the KG into natural text, allowing it to be seamlessly plugged into existing natural language systems.
Edit intent: clarity
Original:  In contrast to the many architectures that have been developed to integrate the structural differences between these two sources, our approach converts the KG into the same format as natural text allowing it to be seamlessly plugged into existing natural language systems.
Revised:  In contrast to the many architectures that have been developed to integrate the structural differences between these two sources, our approach converts the KG into the same format as natural text allowing it to be seamlessly integrated into existing language models. It carries the further advantages of improved factual accuracy and reduced toxicity in the resulting language model.
Edit intent: meaning-changed
Original:  In this work, we develop a recurrent architecture that processes code with all variable names anonymized, i. e. replaced with unique placeholders. The proposed architecture outperforms standard NLP baselines on code completion task by a large margin in the anonymized setting, and improves the base model in the non-anonymized setting, being ensembled with it .
Revised:  In this work, we develop dynamic embeddings, a recurrent mechanism that adjusts the learned semantics of the variable when it obtains more information about the variable's role in the program. We show that using the proposed dynamic embeddings significantly improves the performance of the recurrent neural network, in code completion and bug fixing tasks .
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Original: Current state-of-the-art graph-based dependency parsers differ on various dimensions. Among others, these include (a) the choice of pre-trained word embeddings or language models used for representing token, (b) training setups performing only parsing or additional tasks such as part-of-speech-tagging, and (c) their mechanism of constructing trees or graphs from edge scores.
Revised: Current state-of-the-art graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various dimensions, including their choice of pre-trained word embeddings or language models used for representing token, (b) training setups performing only parsing or additional tasks such as part-of-speech-tagging, and (c) their mechanism of constructing trees or graphs from edge scores.
Edit intent: meaning-changed
Original:  Among others, these include (a) the choice of pre-trained word embeddings or language models used for representing token, (b) training setups performing only parsing or additional tasks such as part-of-speech-tagging, and (c) their mechanism of constructing trees or graphs from edge scores. Because of this, it is difficult to estimate the impact of these architectural decisions when comparing parsers. In this paper, we perform a series of experiments on STEPS, a new modular graph-based parser for basic and enhanced Universal Dependencies, analyzing the effects of architectural configurations .
Revised:  Among others, these include (a) the choice of pre-trained language models and whether they use LSTM layers. With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based parser for basic and enhanced Universal Dependencies, analyzing the effects of architectural configurations .
Edit intent: clarity
Original:  We find that  pre-trained embeddings have by far the greatest and most clear-cut impact on parser performance . The choice of factorized vs. unfactorized architectures and a multi-task training setup affect parsing accuracy in more subtle ways, depending on target language and output representation (trees vs. graphs).
Revised:  We find that pre-trained embeddings have by far the greatest and most clear-cut impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits. A multi-task training setup affect parsing accuracy in more subtle ways, depending on target language and output representation (trees vs. graphs).
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