wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, the authors propose a model-driven  feedback control policy for multiple POV's to propagate efficient adversarial trajectories online while respecting traffic rules and other concerns formulated as  admissible state-action space.
Revised:  In this paper, the authors propose a model-driven online feedback control policy for multiple POV's to propagate efficient adversarial trajectories online while respecting traffic rules and other concerns formulated as admissible state-action space.
Edit intent: meaning-changed
Original:  In this paper, the authors propose a model-driven  feedback control policy for multiple POV's to propagate efficient adversarial trajectories online while respecting traffic rules and other concerns formulated as  admissible state-action space.
Revised:  In this paper, the authors propose a model-driven feedback control policy for multiple POVs which propagates efficient adversarial trajectories online while respecting traffic rules and other concerns formulated as admissible state-action space.
Edit intent: clarity
Original:  In this paper, the authors propose a model-driven  feedback control policy for multiple POV's to propagate efficient adversarial trajectories online while respecting traffic rules and other concerns formulated as  admissible state-action space.
Revised:  In this paper, the authors propose a model-driven feedback control policy for multiple POV's to propagate efficient adversarial trajectories while respecting traffic rules and other concerns formulated as admissible state-action space.
Edit intent: clarity
Original:  In this paper, the authors propose a model-driven  feedback control policy for multiple POV's to propagate efficient adversarial trajectories online while respecting traffic rules and other concerns formulated as  admissible state-action space.
Revised:  In this paper, the authors propose a model-driven feedback control policy for multiple POV's to propagate efficient adversarial trajectories online while respecting traffic rules and other concerns formulated as an admissible state-action space.
Edit intent: fluency
Original:  Such scenario-based testing (i) lacks severity guarantees, (ii) is easy for SV to game the test with intelligent driving policies  , and (iii) is inefficient in producing safety-critical instances with limited and expensive testing effort.
Revised:  Such scenario-based testing (i) lacks severity guarantees, (ii) has predefined maneuvers making it easy for an SV with intelligent driving policies , and (iii) is inefficient in producing safety-critical instances with limited and expensive testing effort.
Edit intent: clarity
Original:  Such scenario-based testing (i) lacks severity guarantees, (ii) is easy for SV to game the test with intelligent driving policies  , and (iii) is inefficient in producing safety-critical instances with limited and expensive testing effort.
Revised:  Such scenario-based testing (i) lacks severity guarantees, (ii) is easy for SV to game the test with intelligent driving policies to game the test , and (iii) is inefficient in producing safety-critical instances with limited and expensive testing effort.
Edit intent: clarity
Original:  In this paper, the authors propose a model-driven online feedback control policy for multiple POVs which propagates efficient adversarial trajectories while respecting traffic rules and other concerns formulated as an admissible state-action space.
Revised:  We propose a model-driven online feedback control policy for multiple POVs which propagates efficient adversarial trajectories while respecting traffic rules and other concerns formulated as an admissible state-action space.
Edit intent: clarity
Original: In this paper, we extend a recently proposed framework for message passing on "loopy" networks to the solution of probabilistic models . We derive a self-consistent set of message passing equations that allow for fast computation of probability distributions in systems that contain short loops, potentially with high density, as well as  expressions for the entropy and partition function of such systems , which are notoriously difficult quantities to compute.
Revised: In this paper, we extend a recently proposed framework for message passing on "loopy" networks to the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of networks that contain short loops. Here we provide a solution to this long-standing problem, deriving a belief propagation method that allows for fast calculation of probability distributions in systems that contain short loops, potentially with high density, as well as expressions for the entropy and partition function of such systems , which are notoriously difficult quantities to compute.
Edit intent: meaning-changed
Original:  We derive a self-consistent set of message passing equations that allow for fast computation of probability distributions in systems that contain short loops, potentially with high density, as well as  expressions for the entropy and partition function of such systems , which are notoriously difficult quantities to compute.
Revised:  We derive a self-consistent set of message passing equations that allow for fast computation of probability distributions in systems with short loops, potentially with high density, as well as expressions for the entropy and partition function of such systems , which are notoriously difficult quantities to compute.
Edit intent: clarity
Original:  We derive a self-consistent set of message passing equations that allow for fast computation of probability distributions in systems that contain short loops, potentially with high density, as well as  expressions for the entropy and partition function of such systems , which are notoriously difficult quantities to compute.
Revised:  We derive a self-consistent set of message passing equations that allow for fast computation of probability distributions in systems that contain short loops, potentially with high density, as well as giving expressions for the entropy and partition function of such systems , which are notoriously difficult quantities to compute.
Edit intent: coherence
Original:  We derive a self-consistent set of message passing equations that allow for fast computation of probability distributions in systems that contain short loops, potentially with high density, as well as  expressions for the entropy and partition function of such systems , which are notoriously difficult quantities to compute.
Revised:  We derive a self-consistent set of message passing equations that allow for fast computation of probability distributions in systems that contain short loops, potentially with high density, as well as expressions for the entropy and partition function , which are notoriously difficult quantities to compute.
Edit intent: clarity
Original:  Using the Ising model as an example, we show that our solutions are asymptotically exact on certain classes of networks with short loops and offer a good approximation on more general networks, improving significantly on results derived from standard belief propagation .
Revised:  Using the Ising model as an example, we show that our solutions are asymptotically exact on certain classes of networks with short loops and offer a good approximation on more general networks, improving significantly on standard message passing methods .
Edit intent: clarity
Original: Considering the dynamic nature of traffic, the robust network design problem consists in computing the capacity to be reserved on each network link such that any demand vector belonging to a polyhedral set can be routed.
Revised: Considering the dynamic nature of traffic, we investigate the variant of robust network design problem consists in computing the capacity to be reserved on each network link such that any demand vector belonging to a polyhedral set can be routed.
Edit intent: meaning-changed
Original:  This paper further broadens this family by introducing pass-and-swap (P&S) queues, an extension of OI queues where any customer that completes service is not necessarily the customer that leaves the system.
Revised:  This paper further broadens this family by introducing pass-and-swap (P&S) queues, an extension of OI queues where , upon a service completion, the customer that completes service is not necessarily the customer that leaves the system.
Edit intent: clarity
Original:  This paper further broadens this family by introducing pass-and-swap (P&S) queues, an extension of OI queues where any customer that completes service is not necessarily the customer that leaves the system.
Revised:  This paper further broadens this family by introducing pass-and-swap (P&S) queues, an extension of OI queues where any customer that completes service is not necessarily the one that leaves the system.
Edit intent: clarity
Original: In this paper we focus on unsupervised representation learning and propose a novel framework , Hierarchical Sparse Variational Autoencoder (HSVAE), that imposes sparsity on sentence representations via direct optimisation of Evidence Lower Bound (ELBO) . Our experimental results illustrate that HSVAE is flexible and adapts nicely to the underlying characteristics of the corpus which is reflected by the level of sparsity and its distributional patterns .
Revised: It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning. Of particular interest to this work is the investigation of the sparsity within the VAE framework which has been explored a lot in the image domain, but has been lacking even a basic level of exploration in NLP. Additionally, NLP is also lagging behind in terms of learning sparse representations of large units of text e.g., sentences. We use the VAEs that induce sparse latent representations of large units of text to address the aforementioned shortcomings. First, we move in this direction by measuring the success of unsupervised state-of-the-art (SOTA) and propose a novel framework , Hierarchical Sparse Variational Autoencoder (HSVAE), that imposes sparsity on sentence representations via direct optimisation of Evidence Lower Bound (ELBO) . Our experimental results illustrate that HSVAE is flexible and adapts nicely to the underlying characteristics of the corpus which is reflected by the level of sparsity and its distributional patterns .
Edit intent: meaning-changed
Original: In this paper we focus on unsupervised representation learning and propose a novel framework , Hierarchical Sparse Variational Autoencoder (HSVAE), that imposes sparsity on sentence representations via direct optimisation of Evidence Lower Bound (ELBO) . Our experimental results illustrate that HSVAE is flexible and adapts nicely to the underlying characteristics of the corpus which is reflected by the level of sparsity and its distributional patterns .
Revised: In this paper we focus on unsupervised representation learning and other strong VAE-based sparsification baselines for text and propose a hierarchical sparse VAE model to address the stability issue of SOTA. Then, we look at the implications of sparsity on text classification across 3 datasets, and highlight a link between performance of sparse latent representations on downstream tasks and its ability to encode task-related information .
Edit intent: meaning-changed
Original: We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Paraphrase Generation , Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages.
Revised: We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation , Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages.
Edit intent: meaning-changed
Original:  On these datasets, we also show that by using pre-trained models and paraphrases from iNLTK, we can achieve more than 95\% of the previous best performance by using less than 10\% of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub.
Revised:  On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95\% of the previous best performance by using less than 10\% of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub.
Edit intent: clarity
Original:  Experimental results on three public datasets show TADAM can outperform the state-of-the-art method by a large margin , especially by 3.4 \% on E-commerce dataset that has an obvious topic shift.
Revised:  Experimental results on three public datasets show TADAM can outperform the state-of-the-art method , especially by 3.4 \% on E-commerce dataset that has an obvious topic shift.
Edit intent: clarity
Original:  Experimental results on three public datasets show TADAM can outperform the state-of-the-art method by a large margin , especially by 3.4 \% on E-commerce dataset that has an obvious topic shift.
Revised:  Experimental results on three public datasets show TADAM can outperform the state-of-the-art method by a large margin , especially by 3.3 \% on E-commerce dataset that has an obvious topic shift.
Edit intent: meaning-changed
Original:  We apply our method to analyze belief formation of stock market expectations with survey data from the German Socio-Economic Panel (SOEP) and find evidence for non-classical measurement error in subjective belief data.
Revised:  We apply our method to analyze belief formation of stock market expectations with survey data from the German Socio-Economic Panel (SOEP) and find evidence for nonclassical measurement error in subjective belief data.
Edit intent: fluency
Original:  To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graphaugmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.
Revised:  To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.
Edit intent: fluency
Original:  Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 15.98\%, 17.49\% , in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.
Revised:  Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60 , in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.
Edit intent: meaning-changed
Original: Although neural language models are effective at capturing statistics of natural language , their representations are challenging to interpret. In particular , it is unclear how these modelsretain information over multiple timescales .
Revised: Language models must capture statistical dependencies between words at timescales ranging from very short to very long. Earlier work has demonstrated that dependencies in natural language tend to decay with distance between words according to a power law. However , it is unclear how these modelsretain information over multiple timescales .
Edit intent: meaning-changed
Original:  In this work, we construct explicitly multi-timescale language models by manipulating the input and URLet gate biases in a long short-term memory (LSTM) network. The distribution of timescales is selected to approximate power law statistics of natural language through a combination of exponentially decaying memory cells. We then empirically analyze the timescale of information routed through each part of the model using word ablation experiments and URLet gate visualizations. These experiments show that the multi-timescale model successfully learns representations at the desired timescales, and that the distribution includes longer timescales than a standard LSTM.
Revised:  In this work, we construct explicitly multi-timescale language models by manipulating the input and URLet gate biases in a long short-term memory (LSTM) language models can capture power law decay. We found that unit timescales within an LSTM, which are determined by the URLet gate bias, should follow an Inverse Gamma distribution. Experiments then showed that LSTM language models trained on natural English text learn to approximate this theoretical distribution. Further, we found that explicitly imposing the theoretical distribution upon the model during training yielded better language model perplexity overall, with particular improvements for predicting low-frequency (rare) words. Moreover, the explicit multi-timescale model successfully learns representations at the desired timescales, and that the distribution includes longer timescales than a standard LSTM.
Edit intent: meaning-changed
Original:  These experiments show that the multi-timescale model successfully learns representations at the desired timescales, and that the distribution includes longer timescales than a standard LSTM. Further, information about high-,mid-, and low-frequency words is routed preferentially through units with the appropriate timescales.
Revised:  These experiments show that the multi-timescale model selectively routes information about different types of words through units with the appropriate timescales.
Edit intent: clarity
Original:  Further, information about high-,mid-, and low-frequency words is routed preferentially through units with the appropriate timescales. Thus we show how to construct language modelswith interpretable representations of different information timescales .
Revised:  Further, information about high-,mid-, and low-frequency words is routed preferentially through units with different timescales, potentially improving model interpretability. These results demonstrate the importance of careful, theoretically-motivated analysis of memory and timescale in language models .
Edit intent: coherence
Original:  The MscaleDNN employs a multiscale structure in the design of its DNN using radial scalings to convert the approximation of high frequency components of the highly oscillatory Stokes solution to one of lower frequencies.
Revised:  The MscaleDNN employs a multi-scale structure in the design of its DNN using radial scalings to convert the approximation of high frequency components of the highly oscillatory Stokes solution to one of lower frequencies.
Edit intent: fluency
Original: It is common to model learning in games so that either a deterministic process or a finite state Markov chain describes the evolution of play. Such processes can however produce undesired outputs, where the players' behavior is heavily influenced by the modeling. In simulations we see how the assumptions in (Young, 1993), a well-studied model for stochastic stability, lead to unexpected behavior in games without strict equilibria, such as Matching Pennies. The behavior should be considered a modeling artifact. In this paper we propose a continuous-state space modelfor learning in games that can converge to mixed Nash equilibria, the Recency Weighted Sampler (RWS). The RWS is similar in spirit Young's model, but introduces a notion of best response where the players sample from a recency weighted history of interactions. We derive properties of the RWS which are known to hold for finite-state space models of adaptive play, such as the convergence to and existence of a unique invariant distribution of the process, and the concentration of that distribution on minimal CURB blocks. Then, we establish conditions under which the RWS process concentrates on mixed Nash equilibria inside minimal CURB blocks .
Revised: We introduce and study a model of long-run convention formation for rare interactions. Players in this model form beliefs by observing a recency-weighted sample of past interactions, to which they noisily best respond. We propose a continuous state Markov model, well-suited for our setting, and develop a methodology that is relevant for a larger class of similar learning models. We show that the model admits a unique asymptotic distribution which concentrates its mass on some minimal CURB block configuration. In contrast to existing literature of long-run convention formation, we focus on behavior inside minimal CURB blocks and provide conditions for convergence to (approximate) mixed equilibria conventions inside minimal CURB blocks .
Edit intent: clarity
Original: We propose Parametric UMAP , a parametric variation of the UMAP (Uniform Manifold Approximation and Projection) algorithm. UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data.
Revised: UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data.
Edit intent: coherence
Original:  We then show that UMAP loss can be extended to arbitrary deep learning applications, for example constraining the latent distribution of autoencoders,  and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data.
Revised:  We then explore UMAP as a regularization, constraining the latent distribution of autoencoders, and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data.
Edit intent: meaning-changed
Original:  We then show that UMAP loss can be extended to arbitrary deep learning applications, for example constraining the latent distribution of autoencoders,  and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data.
Revised:  We then show that UMAP loss can be extended to arbitrary deep learning applications, for example constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data.
Edit intent: meaning-changed
Original: We show that deciding whether a ]  given relational structure R admits a weak near unanimity polymorphism is polynomial time solvable .
Revised: We consider the problem of finding a homomorphism from an input digraph G to a fixed digraph H. We show that if H admits a weak near unanimity polymorphism \phi then deciding whether G admits a homomorphism to H (HOM(H)) is polynomial-time solvable. This gives proof of the dichotomy conjecture (now dichotomy theorem) by Feder and Vardi. Our approach is combinatorial, and it is simpler than the two algorithms found by Bulatov and Zhuk. We have implemented our algorithm and show some experimental results. We use our algorithm together with the recent result ] given relational structure R admits a weak near unanimity polymorphism is polynomial time solvable .
Edit intent: meaning-changed