wanyu/IteraTeR_full_sent huggingface dataset:
Original: The collection and annotation of task-oriented conversational data is a costlyand time-consuming manner. Many augmentation techniques have been proposed to improve the performance of state-of-the-art (SOTA) systems in new domains that lack the necessary amount of data for training.
Revised: The collection and annotation of task-oriented conversational data is costly, data augmentation techniques have been proposed to improve the performance of state-of-the-art (SOTA) systems in new domains that lack the necessary amount of data for training.
Edit intent: clarity
Original:  Many augmentation techniques have been proposed to improve the performance of state-of-the-art (SOTA) systems in new domains that lack the necessary amount of data for training. However, these augmentation techniques (e.g. paraphrasing) also require some mediocre amount of data since they use learning-based approaches.
Revised:  Many augmentation techniques have been proposed to improve the performance of state-of-the-art (SOTA) systems in new domains . Up to now, these learning-based techniques (e.g. paraphrasing) also require some mediocre amount of data since they use learning-based approaches.
Edit intent: coherence
Original:  This makes using SOTA systems in emerging low-resource domains infeasible. We, to tackle this problem, introduce a framework , that creates synthetic task-oriented dialogues in a fully automatic manner, which operates with input sizes of as small as a few dialogues .
Revised:  This makes using SOTA systems in emerging low-resource settings infeasible. To tackle this problem, introduce a framework , that creates synthetic task-oriented dialogues in a fully automatic manner, which operates with input sizes of as small as a few dialogues .
Edit intent: clarity
Original:  Our framework uses the simple idea that each turn-pair in a task-oriented dialogue has a certain function and exploits this idea to mix them creating new dialogues .
Revised:  Our framework utilizes belief state annotations to define dialogue functions of each turn pair. It then creates templates of pairs through de-lexicalization, where the dialogue function codifies the allowable incoming and outgoing links of each template. To generate new dialogues, our framework composes allowable adjacent templates in a bottom-up manner .
Edit intent: meaning-changed
Original:  The proposed method is able to achieve a significant gain of robustness over 15\% comparing with the state-of-the-art. We also show that the proposed regularization method complements other defense paradigms, such as adversarial training, to further improve the robustness .
Revised:  Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed regularization method complements other defense paradigms, such as adversarial training, to further improve the robustness .
Edit intent: meaning-changed
Original:  However, unpredictable weather can make real-world testing in adverse conditions  an expensive and time consuming task requiring access to specialist facilities , and weatherproofing of sensitive electronics.
Revised:  However, development of robust autonomous vehicle subsystems requires repeatable, controlled testing - while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions an expensive and time consuming task requiring access to specialist facilities , and weatherproofing of sensitive electronics.
Edit intent: meaning-changed
Original:  However, unpredictable weather can make real-world testing in adverse conditions  an expensive and time consuming task requiring access to specialist facilities , and weatherproofing of sensitive electronics. Simulation provides an alternative to real world testing, with some studies developing increasingly visually realistic representations of the real world on powerful compute hardware.
Revised:  However, unpredictable weather can make real-world testing in adverse conditions an expensive and time consuming task requiring access to specialist facilities . Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world on powerful compute hardware.
Edit intent: clarity
Original:  Simulation provides an alternative to real world testing, with some studies developing increasingly visually realistic representations of the real world on powerful compute hardware. Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception the appearance is of little consequence - rather it is how the perception system performs in the prevailing weather condition that is important .
Revised:  Simulation provides an alternative to real world testing, with some studies developing increasingly visually realistic representations of the real-world being developed. In the context of the complete autonomous vehicle control pipeline, subsystems downstream of perception the appearance is of little consequence - rather it is how the perception system performs in the prevailing weather condition that is important .
Edit intent: clarity
Original:  Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception the appearance is of little consequence - rather it is how the perception system performs in the prevailing weather condition that is important .
Revised:  Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception need to be tested with accurate recreations of the perception system performs in the prevailing weather condition that is important .
Edit intent: clarity
Original:  Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception the appearance is of little consequence - rather it is how the perception system performs in the prevailing weather condition that is important .
Revised:  Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception the appearance is of little consequence - rather it is how the perception system output, rather than focusing on subjective visual realism of the input - whether in simulation or the real world .
Edit intent: clarity
Original:  The system introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle - rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
Revised:  This approach introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle - rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
Edit intent: clarity
Original:  The system introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle - rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
Revised:  The system introduces a latency of less than 8 ms using compute hardware well suited to being carried in the vehicle - rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
Edit intent: clarity
Original:  The system introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle - rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
Revised:  The system introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle - rendering it ideal for real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
Edit intent: fluency
Original:  But research contributions play distinct roles in the unfolding drama of scientific debate, agreement and advance, and institutions may value different kinds of advances . Computational power, access to citation data and an array of modeling techniques have given rise to a widening portfolio of metrics to extract different signals regarding their contribution.
Revised:  But research contributions play distinct roles in the unfolding drama of scientific debate, agreement and advance, which are differentially valued by scientists and their institutions . Computational power, access to citation data and an array of modeling techniques have given rise to a widening portfolio of metrics to extract different signals regarding their contribution.
Edit intent: clarity
Original:  But research contributions play distinct roles in the unfolding drama of scientific debate, agreement and advance, and institutions may value different kinds of advances . Computational power, access to citation data and an array of modeling techniques have given rise to a widening portfolio of metrics to extract different signals regarding their contribution.
Revised:  But research contributions play distinct roles in the unfolding drama of scientific debate, agreement and advance, and institutions may value different kinds of advances . Computational power, access to citation data and an array of modeling techniques have given rise to a widening portfolio of metrics that extract different signals regarding their contribution.
Edit intent: fluency
Original:  Disruption  captures how research comes to eclipse  the prior work on which it builds , becoming recognized as a new scientific direction. We demonstrate that : 1) novel papers disrupt existing theories and expand the scientific frontier; 2) novel papers are more likely to become "sleeping beauties" and accumulate citation impact over the long run ;
Revised:  Disruption captures how research comes to eclipse the prior work on which it builds . We theorize that novel papers will exhibit disruptive impact over time, and demonstrate how they are much more likely than conventional papers to disrupt current literature. Novel papers do not do so immediately, but often become "sleeping beauties" and accumulate citation impact over the long run ;
Edit intent: meaning-changed
Original:  2) novel papers are more likely to become "sleeping beauties" and accumulate citation impact over the long run ; 3) novelty can be reformulated as distance in journal embedding spaces to map the moving frontier of science .
Revised:  2) novel papers are more likely to become "sleeping beauties" and accumulate citation impact over the long run . In summary, new directions for science are created from a lack of consensus. Finally, we show how novelty can be reformulated as distance in journal embedding spaces to map the moving frontier of science .
Edit intent: meaning-changed
Original:   DNN-based solution can provide decision-makers with more efficient routing than  non-DNN solution. Although DNN-based solution can save considerable time in evacuation routing , it does not come close to compensating for the extra time required for social distancing  and its advantage disappears as the  vehicle capacity approaches the number of people per household.
Revised:  DNN-based solution can provide decision-makers with more efficient routing than non-DNN routing solution. However , it does not come close to compensating for the extra time required for social distancing and its advantage disappears as the vehicle capacity approaches the number of people per household.
Edit intent: coherence
Original:  Although DNN-based solution can save considerable time in evacuation routing , it does not come close to compensating for the extra time required for social distancing  and its advantage disappears as the  vehicle capacity approaches the number of people per household.
Revised:  Although DNN-based solution can save considerable time in evacuation routing , it does not come close to compensating for the extra time required for social distancing , and its advantage disappears as the vehicle capacity approaches the number of people per household.
Edit intent: fluency
Original:  Although DNN-based solution can save considerable time in evacuation routing , it does not come close to compensating for the extra time required for social distancing  and its advantage disappears as the  vehicle capacity approaches the number of people per household.
Revised:  Although DNN-based solution can save considerable time in evacuation routing , it does not come close to compensating for the extra time required for social distancing and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.
Edit intent: clarity
Original:  Policies learned through self-play may adopt arbitrary conventions and  rely on multi-step counterfactual reasoning based on  assumptions about other agents' actions and thus fail when paired with humans or independently trained agents .
Revised:  Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step counterfactual reasoning based on assumptions about other agents' actions and thus fail when paired with humans or independently trained agents .
Edit intent: meaning-changed
Original:  Policies learned through self-play may adopt arbitrary conventions and  rely on multi-step counterfactual reasoning based on  assumptions about other agents' actions and thus fail when paired with humans or independently trained agents .
Revised:  Policies learned through self-play may adopt arbitrary conventions and rely on multi-step counterfactual reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents .
Edit intent: meaning-changed
Original:  Policies learned through self-play may adopt arbitrary conventions and  rely on multi-step counterfactual reasoning based on  assumptions about other agents' actions and thus fail when paired with humans or independently trained agents . In contrast, no current methods can learn optimal policies that are fully grounded, i.e., do not rely on counterfactual information from observing other agents' actions.
Revised:  Policies learned through self-play may adopt arbitrary conventions and rely on multi-step counterfactual reasoning based on assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.
Edit intent: clarity
Original:  To address this, we present off-belief learning (OBL) : at each time step OBL agents assume that all past actions were taken by a given, fixed policy (\pi_0), but  that future actions will be taken by an optimal policy under these same assumptions .
Revised:  To address this, we present off-belief learning (OBL) . At each timestep OBL agents follow a policy \pi_1 that is optimized assuming past actions were taken by a given, fixed policy (\pi_0), but that future actions will be taken by an optimal policy under these same assumptions .
Edit intent: meaning-changed
Original:  To address this, we present off-belief learning (OBL) : at each time step OBL agents assume that all past actions were taken by a given, fixed policy (\pi_0), but  that future actions will be taken by an optimal policy under these same assumptions .
Revised:  To address this, we present off-belief learning (OBL) : at each time step OBL agents assume that all past actions were taken by a given, fixed policy (\pi_0), but assuming that future actions will be taken by an optimal policy under these same assumptions .
Edit intent: fluency
Original:  To address this, we present off-belief learning (OBL) : at each time step OBL agents assume that all past actions were taken by a given, fixed policy (\pi_0), but  that future actions will be taken by an optimal policy under these same assumptions .
Revised:  To address this, we present off-belief learning (OBL) : at each time step OBL agents assume that all past actions were taken by a given, fixed policy (\pi_0), but that future actions will be taken by \pi_1 .
Edit intent: meaning-changed
Original:  When \pi_0 is uniform random, OBL learns the optimal grounded policy . OBL can be iterated in a hierarchy, where the optimal policy from one level becomes the input to the next .
Revised:  When \pi_0 is uniform random, OBL converges to an optimal policy that does not rely on inferences based on other agents' behavior (an optimal grounded policy) . OBL can be iterated in a hierarchy, where the optimal policy from one level becomes the input to the next .
Edit intent: meaning-changed
Original:  When \pi_0 is uniform random, OBL learns the optimal grounded policy . OBL can be iterated in a hierarchy, where the optimal policy from one level becomes the input to the next . This introduces counterfactual reasoning in a controlled manner.
Revised:  When \pi_0 is uniform random, OBL learns the optimal grounded policy . OBL can be iterated in a hierarchy, where the optimal policy from one level becomes the input to the next , thereby introducing multi-level cognitive reasoning in a controlled manner.
Edit intent: clarity
Original:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination  . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Revised:  Unlike existing approaches, which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Edit intent: clarity
Original:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination  . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Revised:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it suitable for zero-shot coordination . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Edit intent: clarity
Original:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination  . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Revised:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination (ZSC) . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Edit intent: meaning-changed
Original:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination  . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Revised:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Edit intent: clarity
Original:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination  . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI /zero-shot coordination problem Hanabi.
Revised:  Unlike independent RL which may converge to any equilibrium policy, OBL converges to a unique policy, making it more suitable for zero-shot coordination . OBL can be scaled to high-dimensional settings with a fictitious transition mechanism and shows strong performance in both a simple toy-setting and the benchmark human-AI ZSC problem Hanabi.
Edit intent: clarity
Original: The rigidity of a matrix A for target rank r is the minimum number of entries of A that need to be changed in order to obtain a matrix of rank at most r. Matrix rigidity was introduced by Valiant in 1977 as a tool to prove circuit lower bounds for linear functions and since then this notion has also found applications in other areas of complexity theory.
Revised: The rigidity of a matrix A for target rank r is the minimum number of entries of A that need to be changed in order to obtain a matrix of rank at most r. At MFCS'77, Valiant introduced matrix rigidity as a tool to prove circuit lower bounds for linear functions and since then this notion has also found applications in other areas of complexity theory.
Edit intent: meaning-changed
Original: The rigidity of a matrix A for target rank r is the minimum number of entries of A that need to be changed in order to obtain a matrix of rank at most r. Matrix rigidity was introduced by Valiant in 1977 as a tool to prove circuit lower bounds for linear functions and since then this notion has also found applications in other areas of complexity theory.
Revised: The rigidity of a matrix A for target rank r is the minimum number of entries of A that need to be changed in order to obtain a matrix of rank at most r. Matrix rigidity was introduced by Valiant in 1977 as a tool to prove circuit lower bounds for linear functions and since then this notion received much attention and found applications in other areas of complexity theory.
Edit intent: clarity
Original:  Recently (arXiv 2021) , Alman proved that for any field \mathbb{F, d\geq 2 and arbitrary }  matrices M_1, \ldots, M_n \in \mathbb{F^{d\times d}, one can get a d^n\times d^n matrix of rank }%DIFDELCMD < \le %%% d^{(1-\gamma)n \dots\ge  ^{ ( 1+\varepsilon ) n } entries of the Kronecker product M = M_1\otimes M_2\otimes \ldots\otimes M_n\le  , where 1/\gamma is roughly 2^d/\varepsilon^2.
Revised:  , d\geq 2 and arbitrary } matrices M_1, \ldots, M_n \in \mathbb{F^{d\times d}, one can get a d^n\times d^n matrix of rank }%DIFDELCMD < \le %%% d^{(1-\gamma)n \dots\ge ^{ ( 1+\varepsilon ) n } entries of the Kronecker product M = M_1\otimes M_2\otimes \ldots\otimes M_n\le , where 1/\gamma is roughly 2^d/\varepsilon^2.
Edit intent: coherence