wanyu/IteraTeR_full_sent huggingface dataset:
Original:  However, this paper shows that, when the model is refuted by the data, an nonempty outer set could deliver conflicting results with another outer set derived from the same underlying model structure, so that the results of outer sets could be misleading in the presence of misspecification.
Revised:  However, this paper shows that, when the model is refuted by the data, a nonempty outer set could deliver conflicting results with another outer set derived from the same underlying model structure, so that the results of outer sets could be misleading in the presence of misspecification.
Edit intent: fluency
Original:  Furthermore, we develop a method to salvage misspecified models  .
Revised:  We also derive sufficient conditions for the non-existence of discordant submodels, therefore providing a class of models for which constructing outer sets cannot lead to misleading interpretations. In the case of discordancy, we follow Masten and Poirier (2020) by developing a method to salvage misspecified models .
Edit intent: meaning-changed
Original:  Models should also consider lexical matches when the query contains rare terms -- such as a person's name or a product model number -- not seen during training, and to avoid retrieving semantically related but irrelevant results.
Revised:  Models should also consider lexical matches when the query contains rare terms--such as a person's name or a product model number -- not seen during training, and to avoid retrieving semantically related but irrelevant results.
Edit intent: fluency
Original:  Models should also consider lexical matches when the query contains rare terms -- such as a person's name or a product model number -- not seen during training, and to avoid retrieving semantically related but irrelevant results.
Revised:  Models should also consider lexical matches when the query contains rare terms -- such as a person's name or a product model number--not seen during training, and to avoid retrieving semantically related but irrelevant results.
Edit intent: fluency
Original:  In this work, we propose to address this issue via contrastive learning  . Concretely, we introduce a social contrastive loss that encourages the encoded motion representation to preserve sufficient information for distinguishing a positive future event from a set of negative ones.
Revised:  In this work, we propose to address this issue via contrastive learning with negative data augmentation . Concretely, we introduce a social contrastive loss that encourages the encoded motion representation to preserve sufficient information for distinguishing a positive future event from a set of negative ones.
Edit intent: meaning-changed
Original:  In this paper, we jointly learn word and concept embeddings by first using the skip-gram method and further fine-tuning them with correlational information manifesting in co-occurring Medical Subject Heading (MeSH) concepts in biomedical citations.
Revised: Biomedical word embeddings are usually pre-trained on free text corpora with neural methods that capture local and global distributional properties. They are leveraged in downstream tasks using various neural architectures that are designed to optimize task-specific objectives that might further tune such embeddings. Since 2018, however, there is a marked shift from these static embeddings to contextual embeddings motivated by language models (e.g., ELMo, transformers such as BERT, and ULMFiT). These dynamic embeddings have the added benefit of being able to distinguish homonyms and acronyms given their context. However, static embeddings are still relevant in low resource settings (e.g., smart devices, IoT elements) and to study lexical semantics from a computational linguistics perspective. In this paper, we jointly learn word and concept embeddings by first using the skip-gram method and further fine-tuning them with correlational information manifesting in co-occurring Medical Subject Heading (MeSH) concepts in biomedical citations.
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Original:  The general theorem is applied to standard Gaussian priors , which are shown to converge to the truth at the minimax optimal rate over Sobolev smoothness classes in any dimension . We further establish contraction rates for p-exponential priors over spatially inhomogeneous Besov function classes for which Gaussian priors are unsuitable, again in any dimension.
Revised:  The general theorem is applied to standard Gaussian priors , which are shown to converge to the truth at the minimax optimal rate over Sobolev smoothness classes in any dimension 
Edit intent: meaning-changed
Original: Social distancing has been one of the core public policy measures used to mitigate the economic and health impacts of the COVID-19 pandemic. Such widespread adoption of social distancing measures is wholly unprecedented, and governments have implemented a variety of policies to encourage compliance. These typically rely on financial penalties (fines) and/or informational messages (nudges). There is, however, a lack of evidence on the impact of these policies .
Revised: Social distancing has been one of the core public policy measures used to mitigate the economic and health impacts of the COVID-19 , but lack evidence on the impact of these policies .
Edit intent: clarity
Original:  We examine the effectiveness of fines and nudges in promoting social distancing in a web-based interactive experiment . The study involves a nearrepresentative sample of 400 participants from the US population , and it was conducted in May 2020 at the height of the pandemic .
Revised:  We examine the effectiveness of fines and nudges in promoting social distancing in a web-based interactive experiment conducted during the pandemic on a near-representative sample of the US population , and it was conducted in May 2020 at the height of the pandemic .
Edit intent: clarity
Original:  Moreover, using coarse-grained modeling units, such as subword and character, achieves better results than the letter.abstract 
Revised:  Moreover, using coarse-grained modeling units, such as subword and character, achieves better results than the letter.abstract \% relative improvements in six languages compared with previous work. Among these languages, English achieves a gain of 52.4\%. Moreover, using coarse-grained modeling units, such as subword or character, achieves better results than fine-grained modeling units, such as phone or letter.
Edit intent: meaning-changed
Original: In this paper , we adopt an information-theoretic approach to investigate the fundamental lower bounds on the maximum deviations in feedback control systems, where the plant is linear time-invariant while the controller can generically be any causal functions as long as it stabilizes the plant.
Revised: This paper is on the application of information theory to the analysis of fundamental lower bounds on the maximum deviations in feedback control systems, where the plant is linear time-invariant while the controller can generically be any causal functions as long as it stabilizes the plant.
Edit intent: clarity
Original:  It is seen in general that the lower bounds are characterized by the unstable poles (or nonminimum-phase zeros) of the plant as well as the level of randomness (as quantified by the conditional entropy ) contained in the disturbance.
Revised:  It is seen in general that the lower bounds are characterized by the unstable poles (or nonminimum-phase zeros) of the plant as well as the conditional entropy of the disturbance.
Edit intent: clarity
Original: This paperis on the application of information theory to the analysis of fundamental lower bounds on the maximum deviations in feedback control systems, where the plant  is linear time-invariant while the controller can generically be any causal functions as long as it stabilizes the plant.
Revised: In this paper, we examine the fundamental performance limitations in the control of stochastic dynamical systems; more specifically, we derive generic \mathcal{L where the plant is linear time-invariant while the controller can generically be any causal functions as long as it stabilizes the plant.
Edit intent: meaning-changed
Original: This paperis on the application of information theory to the analysis of fundamental lower bounds on the maximum deviations in feedback control systems, where the plant  is linear time-invariant while the controller can generically be any causal functions as long as it stabilizes the plant.
Revised: This paperis on the application of information theory to the analysis of fundamental lower bounds on the maximum deviations in feedback control systems, where the plant (i.e., the dynamical system to be controlled) is linear time-invariant while the controller can generically be any causal functions as long as it stabilizes the plant.
Edit intent: meaning-changed
Original: This paperis on the application of information theory to the analysis of fundamental lower bounds on the maximum deviations in feedback control systems, where the plant  is linear time-invariant while the controller can generically be any causal functions as long as it stabilizes the plant. It is seen in general that the lower bounds are characterized by the unstable poles (or nonminimum-phase zeros) of the plant as well as the conditional entropy of the disturbance.
Revised: This paperis on the application of information theory to the analysis of fundamental lower bounds on the maximum deviations in feedback control systems, where the plant is linear time-invariant , and it is seen in general that the lower bounds are characterized by the unstable poles (or nonminimum-phase zeros) of the plant as well as the conditional entropy of the disturbance.
Edit intent: coherence
Original:  Such bounds provide fundamental limits on how short the distribution tails in control systems can be made by feedback .
Revised:  We then analyze the setting where the plant is assumed to be (strictly) causal, for which case the lower bounds are determined by the conditional entropy of the disturbance. We also discuss the special cases of p = 2 and p = \infty, which correspond to minimum-variance control and controlling the maximum deviations, respectively. In addition, we investigate the power-spectral characterization of the lower bounds as well as its relation to the Kolmogorov-Szeg\"o formula .
Edit intent: meaning-changed
Original: Due to the rate at which the COVID-19 pandemic is spreading, a model for indoors airborne transmission that is quick to implement is required. We create such a model assuming that  the concentration of airborne infectious particles is governed by an advection-diffusion-reaction equation .
Revised: Due to the rate at which the COVID-19 , that the concentration of airborne infectious particles is governed by an advection-diffusion-reaction equation .
Edit intent: clarity
Original:  We create such a model assuming that  the concentration of airborne infectious particles is governed by an advection-diffusion-reaction equation . As schools and universities are intensively discussing how to operate their indoor spaces, we solve the model for a classroom, assuming one infectious person at the centre of the room and quantify the effect of several ventilation settings when the person is breathing or talking, both with and without a face mask . We compare our model with more complex models and with experimental data and find good agreement. The framework can be easily applied to any other high-risk location. We also generate maps of the Time To Infection (TTI) by airborne transmission, for various activities and ventilation levels. Using the TTI maps we quantify the airborne transmission risk and make recommendations for the Safe Occupancy Time (SOT), for a chosen risk tolerance level. As expected, we find that the SOT decreases with ventilation quality.
Revised:  We create such a model assuming that the concentration of airborne infectious particles is governed by an advection-diffusion-reaction equation with the particles advected by airflow, diffused due to turbulence, emitted by infected people and removed due to the room ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person who breathes or talks, with or without a mask and model a quasi-3D setup that incorporates a recirculating air-conditioning flow. A semi-analytic solution is available and this enables fast simulations. We quantify the effect of ventilation and particle emission rate on the particle concentration, infection risk and the SOT decreases with ventilation quality.
Edit intent: meaning-changed
Original:  As expected, we find that the SOT decreases with ventilation quality. We also establish the recommended "vacancy time" after an activity in the room. For example, to achieve an airborne transmission risk of less than 5\%, the pre-pandemic ASHRAE recommended ventilationwould allow 37-minute lessons followed by 35-minute breaks but if the ventilation is poor, 25-minute lessons should be followed by 166-minute breaks. Our model also uncovers power-law relationships for the concentration as a function of time and particle emission rate and for the TTI as a function of ventilation rate and particle emission rate;
Revised:  As expected, we find that the `time to probable infection' (TTPI). Good agreement with CFD models is achieved. Furthermore, we derive power laws that quantify the effect of ventilation, emission rate and for the TTI as a function of ventilation rate and particle emission rate;
Edit intent: clarity
Original:  Our model also uncovers power-law relationships for the concentration as a function of time and particle emission rate and for the TTI as a function of ventilation rate and particle emission rate; these may be used as a fast tool to predict the SOT in several realistic scenarios. Finally, we show that wearing a mask of 50\% efficiency could increase the SOT by 98\% and reduce the vacancy time by up to 55\%, depending on the ventilation setting .
Revised:  Our model also uncovers power-law relationships for the concentration as a function of time and particle emission rate and infectiousness of the virus on the TTPI. The model can be easily updated to take into account modified parameter values. This work paves the way for establishing `safe occupancy times' at any location and has direct applicability in mitigating the spread of the COVID-19 pandemic .
Edit intent: meaning-changed
Original:  However, most existing similarity-based methods follow the transductive setting. These methods cannot directly generalize to unseen data because they should be re-built to predict the interactions for new arriving drugs , targets, or drug-target pairs.
Revised:  However, most existing similarity-based methods need to be re-trained to predict interactions for any new drugs or targets and cannot directly perform predictions for both new drugs, new targets, and new drug-target pairs.
Edit intent: clarity
Original: We investigate the ability of general-purpose pretrained vision and language  V&L models to perform reasoning in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image.
Revised: We investigate the reasoning ability of pretrained vision and language V&L models to perform reasoning in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image.
Edit intent: clarity
Original: We investigate the ability of general-purpose pretrained vision and language  V&L models to perform reasoning in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image.
Revised: We investigate the ability of general-purpose pretrained vision and language ( V&L models to perform reasoning in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image.
Edit intent: fluency
Original: We investigate the ability of general-purpose pretrained vision and language  V&L models to perform reasoning in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image.
Revised: We investigate the ability of general-purpose pretrained vision and language V&L ) models in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image.
Edit intent: clarity
Original:  We propose a number of explanations for these findings: LXMERT 's results on the image-sentence alignment task (and to a lesser extent those obtained by ViLBERT 12-in-1) indicate that the model may exhibit catastrophic URLetting .
Revised:  We propose a number of explanations for these findings: LXMERT (and to a lesser extent those obtained by ViLBERT 12-in-1) indicate that the model may exhibit catastrophic URLetting .
Edit intent: clarity
Original:  We propose a number of explanations for these findings: LXMERT 's results on the image-sentence alignment task (and to a lesser extent those obtained by ViLBERT 12-in-1) indicate that the model may exhibit catastrophic URLetting .
Revised:  We propose a number of explanations for these findings: LXMERT 's results on the image-sentence alignment task (and to some extent ViLBERT 12-in-1) indicate that the model may exhibit catastrophic URLetting .
Edit intent: clarity
Original:  We propose a number of explanations for these findings: LXMERT 's results on the image-sentence alignment task (and to a lesser extent those obtained by ViLBERT 12-in-1) indicate that the model may exhibit catastrophic URLetting . As for our results on the counting probe, we find evidence that all models are impacted by dataset bias, and also fail to individuate entities in the visual input   .
Revised:  We propose a number of explanations for these findings: LXMERT 's results on the image-sentence alignment task (and to a lesser extent those obtained by ViLBERT 12-in-1) show some evidence of catastrophic URLetting on task (1). Concerning our results on the counting probe, we find evidence that all models are impacted by dataset bias, and also fail to individuate entities in the visual input .
Edit intent: clarity
Original:  As for our results on the counting probe, we find evidence that all models are impacted by dataset bias, and also fail to individuate entities in the visual input   .
Revised:  As for our results on the counting probe, we find evidence that all models are impacted by dataset bias, and also fail to individuate entities in the visual input L models is their ability to solve complex tasks, our findings suggest that understanding their reasoning and grounding capabilities requires more targeted investigations on specific phenomena .
Edit intent: meaning-changed
Original:  Our approach includes (1) learning query-agnostic phrase representations via question generation and distillation; (2) novel negative-sampling methodsfor global normalization; (3) query-side fine-tuning for transfer learning .
Revised:  We present an effective method to learn phrase representations from the supervision of reading comprehension tasks, coupled with novel negative sampling methods. We also propose a query-side fine-tuning for transfer learning .
Edit intent: clarity
Original:  On five popular  QA datasets, our model DensePhrases improves  previous phrase retrieval models by 15\%-25\% absolute accuracy and matches the performance of state-of-the-art retriever-reader models.
Revised:  On five popular open-domain QA datasets, our model DensePhrases improves previous phrase retrieval models by 15\%-25\% absolute accuracy and matches the performance of state-of-the-art retriever-reader models.
Edit intent: clarity
Original:  On five popular  QA datasets, our model DensePhrases improves  previous phrase retrieval models by 15\%-25\% absolute accuracy and matches the performance of state-of-the-art retriever-reader models.
Revised:  On five popular QA datasets, our model DensePhrases improves over previous phrase retrieval models by 15\%-25\% absolute accuracy and matches the performance of state-of-the-art retriever-reader models.
Edit intent: clarity
Original:  However, since the knowledge in logical databases in practice always is incomplete  , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived .
Revised:  However, the knowledge in logical databases in practice always is incomplete , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived .
Edit intent: coherence
Original:  However, since the knowledge in logical databases in practice always is incomplete  , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived .
Revised:  However, since the knowledge in logic databases in practice always is incomplete , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived .
Edit intent: fluency
Original:  However, since the knowledge in logical databases in practice always is incomplete  , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived .
Revised:  However, since the knowledge in logical databases in practice always is incomplete . Hence , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived .
Edit intent: coherence
Original:  However, since the knowledge in logical databases in practice always is incomplete  , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived . In consequence, negative information from negated expressions does not help in this context, because only negative knowledge can be derived from this. Therefore ,  we aim at reducing syntactic negation, strictly speaking, the negated event or property , to its inverse.
Revised:  However, since the knowledge in logical databases in practice always is incomplete , forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived , we aim at reducing syntactic negation, strictly speaking, the negated event or property , to its inverse.
Edit intent: coherence
Original:  In this paper, we describe an effective procedure to determine the negated event or property in order to replace it with it inverseand our overall system for cognitive reasoning. We demonstrate the procedure with examples and evaluate it with several benchmarks .
Revised:  In this paper, we describe an effective procedure to determine the negated event or property in order to replace it by its inverse. This lays the basis of cognitive reasoning, employing both logic and machine learning for general question answering. We evaluate our procedure by several benchmarks and demonstrate its practical usefulness in our cognitive reasoning system .
Edit intent: meaning-changed