wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on three benchmark datasets: FB15K  , YAGO26K-906, and DB111K-174.
Revised:  Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on four benchmark datasets: FB15K , YAGO26K-906, and DB111K-174.
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Original:  Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on three benchmark datasets: FB15K  , YAGO26K-906, and DB111K-174.
Revised:  Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on three benchmark datasets: FB15K , FB15K-237 , YAGO26K-906, and DB111K-174.
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Original: This paper presents a methodology and toolkit for creating a rule-based multi-domain conversational agent for transactions from (1) language annotations of the domains' database schemas and APIs and (2) a couple of hundreds of annotated human dialogues. There is no need for a large annotated training set , which is expensive to acquire.
Revised: The traditional dialogue state tracking (DST) task tracks the dialogue state given the past history of user and agent utterances. This paper proposes to replace the utterances before the current turn with a formal representation , which is expensive to acquire.
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Original:  There is no need for a large annotated training set , which is expensive to acquire. The toolkit uses a pre-defined abstract dialogue state machine to synthesize millions of dialogues based on the domains' information. The annotated and synthesized data are used to train a contextual semantic parser that interprets the user's latest utterance in the context of a formal representation of the conversation up to that point.
Revised:  There is no need for a large annotated training set , which is used as the context in a semantic parser mapping the current user utterance to its formal meaning. In addition, we propose TOC (Task-Oriented Context), a formal dialogue state representation. This approach eliminates the need to parse a long history of natural language utterances; however, it adds complexity to the dialogue annotations. We propose Skim, a contextual semantic parser that interprets the user's latest utterance in the context of a formal representation of the conversation up to that point.
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Original:  The annotated and synthesized data are used to train a contextual semantic parser that interprets the user's latest utterance in the context of a formal representation of the conversation up to that point. Developers can refine the state machine to achieve higher accuracy.
Revised:  The annotated and synthesized data are used to train a contextual semantic parser , trained with a sample-efficient training strategy: (1) a novel abstract dialogue state machine to achieve higher accuracy.
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Original:  Developers can refine the state machine to achieve higher accuracy. On the MultiWOZ benchmark, we achieve over 71 \% turn-by-turn slot accuracy on a cleaned, reannotated test set, without using any of the original training data.
Revised:  Developers can refine the state machine to synthesize training sets with TOC annotations; (2) data augmentation with automatic paraphrasing, (3) few-shot training, and (4) self-training. This paper also presents MultiWOZ 2.4, which consists of the full test set and a partial validation set of MultiWOZ 2.1, reannotated with the TOC representation. Skim achieves 78 \% turn-by-turn slot accuracy on a cleaned, reannotated test set, without using any of the original training data.
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Original:  On the MultiWOZ benchmark, we achieve over 71 \% turn-by-turn slot accuracy on a cleaned, reannotated test set, without using any of the original training data. Our state machine can model 96 \% of the human agent turns.
Revised:  On the MultiWOZ benchmark, we achieve over 71 \% turn-by-turn exact match accuracy and 85\% slot accuracy, while our annotation effort amounts to only 2 \% of the human agent turns.
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Original:  Our state machine can model 96 \% of the human agent turns. Our training strategy improves by 9\% over a baseline that uses the same amount of hand-labeled data , showing the benefit of synthesizing data using the state machine .
Revised:  Our state machine can model 96 \% of the training data used in MultiWOZ 2.1. The MultiWOZ 2.4 dataset will be released upon publication .
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Original:  The integro-differential formulation  poses challenges to numerical solutions of complicated problems.While various numerical methods based on discretizing the computational domain have been developed and have their own merits,some important issues are yet to be solved , such as the computation of infinite domains, the treatment of softening of boundaries due to an incomplete horizon, and time error accumulation in dynamic processes.
Revised:  The integro-differential formulation poses challenges to numerical solutions of complicated and practical problems. Some important issues attract much attention , such as the computation of infinite domains, the treatment of softening of boundaries due to an incomplete horizon, and time error accumulation in dynamic processes.
Edit intent: clarity
Original:  To this end,the boundary integral equations for static and dynamic problems are derived,and the corresponding numerical frameworks are presented.For static loading,this method gives the explicit equation solved directly without iterations.For dynamic loading,we solve the problem in the Laplace domain and obtain the results in the time domain via inversion.This treatment eliminates time error accumulation,and facilitates parallel computation.The computational results on static and dynamic examples within the bond-based peridynamic formulation exhibit several features.
Revised:  The numerical examples demonstrate that the PD-BEM exhibits several features.
Edit intent: clarity
Original:  First, for non-destructive cases, the PD-BEM can be one to two orders of magnitude faster than the peridynamic meshless particle method (PD-MPM)  ;
Revised:  First, for non-destructive cases, the PD-BEM can be one to two orders of magnitude faster than the peridynamic meshless particle method (PD-MPM) that directly discretizes the computational domains ;
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Original:  For destructive cases where new boundaries emerge during the loading process, we propose a coupling scheme where the PD-MPM is applied to the cracked region and the PD-BEM is applied to the un-cracked region such that the time of computation can be significantly reduced .The present method can be generalized to other subjects such as diffusion and multi-physical problems .
Revised:  For destructive cases where new boundaries emerge during the loading process, we propose a coupling scheme where the PD-MPM is applied to the cracked region and the PD-BEM is applied to the un-cracked region such that the time of computation can be significantly reduced .
Edit intent: coherence
Original:  Our proposed method is also orthogonal to existing compact pretrained language models such as DistilBERT using knowledge distillation, since a further 1.79x average compression rate can be achieved on top of DistilBERT with zero or minor accuracy degradation.
Revised:  Our proposed method is also orthogonal to existing compact pre-trained language models such as DistilBERT using knowledge distillation, since a further 1.79x average compression rate can be achieved on top of DistilBERT with zero or minor accuracy degradation.
Edit intent: fluency
Original: We analyze the solution regularity and discrete-time approximations of extended mean field control (extended MFC) problems, which seek optimal control of McKean-Vlasov dynamics whose coefficients involve mean field interactions both on the state and actions, and where objectives are optimized over open-loop strategies.
Revised: We consider extended mean field control (extended MFC) problems, which seek optimal control of McKean-Vlasov dynamics whose coefficients involve mean field interactions both on the state and actions, and where objectives are optimized over open-loop strategies.
Edit intent: clarity
Original:  We further show that any \epsilon-optimal controls of these discrete-time problems converge to the optimal control of the original problems . To establish the time regularity of optimal controls and the convergence of time discretizations, we extend the canonical path regularity results to general coupled McKean-Vlasov forward-backward stochastic differential equations, which are of independent interest .
Revised:  We further show that any \epsilon-optimal controls of these discrete-time problems converge to the optimal control of the original problems .
Edit intent: coherence
Original:  The objective is to predict and optimally store the files on the caches to maximize the total number of cache hits . We propose LeadCache - an online caching policy based on the Follow-the-Perturbed-Leader paradigm.
Revised:  The objective is to predict and optimally store the files on the caches to maximize the total number of cache hits in an online setting. The problem is non-trivial due to the non-convex and non-smooth nature of the objective function. In this paper, we propose LeadCache - an online caching policy based on the Follow-the-Perturbed-Leader paradigm.
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Original:  We implement the policy by designing a new linear-time Pipage rounding algorithm. With an additional\texttt{  Strong-Law-type assumption, we show that the total number of file fetches under \texttt{LeadCache} remains almost surely finite .
Revised:  We design two efficient implementations of the\texttt{ Strong-Law-type assumption, we show that the total number of file fetches under \texttt{LeadCache} remains almost surely finite .
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Original:  With an additional\texttt{  Strong-Law-type assumption, we show that the total number of file fetches under \texttt{LeadCache} remains almost surely finite .
Revised:  With an additional\texttt{ policy, one based on Pipage rounding and the other based on Madow's sampling, each of which makes precisely one call to an LP-solver per iteration. With a Strong-Law-type assumption, we show that the total number of file fetches under \texttt{LeadCache} remains almost surely finite .
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Original:  With an additional\texttt{  Strong-Law-type assumption, we show that the total number of file fetches under \texttt{LeadCache} remains almost surely finite . Additionally , we derive a tight regret lower bound using results from graph coloring.
Revised:  With an additional\texttt{ Strong-Law-type assumption, we show that the total number of file fetches under \texttt{LeadCache} remains almost surely finite over an infinite horizon. Finally , we derive a tight regret lower bound using results from graph coloring.
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Original:  In order to quantify resilience uncertainty across the network resolutions (macro to micro), we develop an arbitrary polynomial chaos (aPC) expansion method  to estimate the resilience subject to parameter uncertainties with arbitrary distributions.
Revised:  In order to quantify resilience uncertainty across the network resolutions (macro to micro), we develop an arbitrary polynomial chaos (aPC) expansion method is developed in this paper to estimate the resilience subject to parameter uncertainties with arbitrary distributions.
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Original:  EventProp can be applied to spiking networks with arbitrary connectivity, including recurrent, convolutional and deep feed-forward architectures.
Revised:  As errors are backpropagated in an event-based manner (at spike times), EventProp requires storing state variables only at these times, providing favorable memory requirements. EventProp can be applied to spiking networks with arbitrary connectivity, including recurrent, convolutional and deep feed-forward architectures.
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Original:  While we consider the leaky integrate-and-fire neuron model in this work, our methodology to derive the gradient can be applied to other spiking neuron models . As errors are backpropagated in an event-based manner (at spike times), EventProp requires the storage of state variables only at these times, providing favorable memory requirements .
Revised:  While we consider the leaky integrate-and-fire neuron model in this work, our methodology to derive the gradient can be applied to other spiking neuron models .
Edit intent: coherence
Original: We derive the backpropagation algorithm for spiking neural networks composed of leaky integrate-and-fire neurons operating in continuous time. This algorithm, EventProp, computes the exact gradient of an arbitrary loss function of spike times and membrane potentials by backpropagating errors in time .
Revised: Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm, applying this algorithm to spiking networks was previously hindered by the existence of discrete spike events and discontinuities .
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Original:  For the first time, by leveraging methods from optimal control theory, we are able to backpropagate errors through spike discontinuities without approximationsor smoothing operations. As errors are backpropagated in an event-based manner ( at spike times ), EventProp requires storing state variables only at these times, providing favorable memory requirements.
Revised:  For the first time, this work derives the backpropagation algorithm for a continuous-time spiking neural network and a general loss function by applying the adjoint method together with the proper partial derivative jumps, allowing for backpropagation through discrete spike events without approximations. This algorithm, EventProp, backpropagates errors at spike times ), EventProp requires storing state variables only at these times, providing favorable memory requirements.
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Original:  As errors are backpropagated in an event-based manner ( at spike times ), EventProp requires storing state variables only at these times, providing favorable memory requirements. EventProp can be applied to spiking networks with arbitrary connectivity, including recurrent, convolutional and deep feed-forward architectures. While we consider the leaky integrate-and-fire neuron model in this work, our methodology to derive the gradient can be applied to other spiking neuron models. We demonstrate learning using gradients computed via EventProp in a deep spiking network using an event-based simulator and a non-linearly separable dataset encoded using spike time latencies .
Revised:  As errors are backpropagated in an event-based manner ( at spike times in order to compute the exact gradient in an event-based, temporally and spatially sparse fashion. We use gradients computed via EventProp in a deep spiking network using an event-based simulator and a non-linearly separable dataset encoded using spike time latencies .
Edit intent: clarity
Original: We provide new algorithms for maintaining approximate distances in a weighted undirected graph G=(V,E ) subject to edge deletions. Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of s sources in (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Revised: We provide new algorithms for maintaining approximate distances in a weighted undirected graph G=(V,E ,w), a hopset H of hopbound \beta and stretch (1+\epsilon) -approximate distances from a set of s sources in (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
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Original:  Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of s sources in (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Revised:  Our first result is an algorithm that maintains (1+\epsilon) is a set of s sources in (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Edit intent: clarity
Original:  Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of s sources in (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Revised:  Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Edit intent: clarity
Original:  Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of s sources in (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Revised:  Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of s sources in edges such that for any pair of nodes u, v \in V, there is a path in G \cup H of at most \beta hops, whose length is within a (1+\epsilon) factor from the distance between u and v in G. We provide a decremental algorithm for maintaining hopsets with a polylogarithmic hopbound, with a total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
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Original:  Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of s sources in (sm) total update time , assuming that s= n^{\Omega(1), \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Revised:  Our first result is an algorithm that maintains (1+\epsilon) -approximate distances from a set of s sources in (sm) total update time , \epsilon = \Omega(1) and |E|= n^{1+\Omega(1)}.
Edit intent: clarity
Original:  This }  matches the best known static algorithm , up to polylogarithmic factors for a wide range of settings. The currently best known algorithm for the problem is obtained by running the single-source algorithm of Henzinger, Krinninger and Nanongkai, FOCS'14%DIFDELCMD < ] %%% independently from each source.
Revised:  This } matches the best known static algorithm , up to polylogarithmic factors Henzinger, Krinninger and Nanongkai, FOCS'14%DIFDELCMD < ] %%% independently from each source.
Edit intent: coherence
Original:  The currently best known algorithm for the problem is obtained by running the single-source algorithm of Henzinger, Krinninger and Nanongkai, FOCS'14%DIFDELCMD < ] %%% independently from each source.
Revised:  The currently best known algorithm for the problem is obtained by running the single-source algorithm of %DIFDELCMD < ] %%% independently from each source.
Edit intent: coherence
Original:  The currently best known algorithm for the problem is obtained by running the single-source algorithm of Henzinger, Krinninger and Nanongkai, FOCS'14%DIFDELCMD < ] %%% independently from each source. Our result improves over the update time bound of this solution by removing a 2 ^{\tilde{O(\log^{3/4} n)} factor.
Revised:  The currently best known algorithm for the problem is obtained by running the single-source algorithm of Henzinger, Krinninger and Nanongkai, FOCS'14%DIFDELCMD < ] %%% . Previously, the best known decremental hopset algorithm had a hopbound of 2 ^{\tilde{O(\log^{3/4} n)} factor.
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Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves  over the best known  update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single source shortest paths in amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves over the best known update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Edit intent: clarity
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves  over the best known  update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} (\sqrt{\log n})}} . This improves over the best known update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Edit intent: meaning-changed
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves  over the best known  update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves super-polynomially over the best known update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Edit intent: meaning-changed
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves  over the best known  update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -} (1+\epsilon)-approximate single-source shortest paths with amortized update time of 2^{\tilde{O}(\sqrt{\log n})} , when 0< \epsilon<1 is a constant and |E|= n2^{\tilde{\Omega(\sqrt{\log n})}} . This improves over the best known amortized update time of 2^{\tilde{O}(\log^{3/4} n)} by [ Henzinger, Krinninger and Nanongkai, FOCS'14].
Edit intent: fluency