wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Neural networks have successfully advanced in the task of information extraction from structured documents . In business document processing more precise techniques equal more automation and less manual work. In this paper we will design and examine various fully trainable approaches to use siamese networks, concepts of similarity, one-shot learning and context/memory awareness.
Revised:  Neural networks have been successfully applied before - even though they have been trained only on relatively small datasets with hundreds of documents so far. To successfully explore deep learning techniques and improve the information extraction results, a dataset with more than twenty-five thousand documents has been compiled, anonymized and is published as a part of this work. We will expand our previous work where we proved that convolutions, graph convolutions and self-attention can work together and exploit all the information present in a structured document. Taking the fully trainable method one step further, we will now design and examine various fully trainable approaches to use siamese networks, concepts of similarity, one-shot learning and context/memory awareness.
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Original:  All the techniques used are not problem-specific and should be generalizable to help in other tasks and contexts .
Revised:  Qualitative analysis is provided to verify that the new model performs better for all target classes. Additionally, multiple structural observations about the causes of the underperformance of some architectures are revealed. All the source codes, parameters and implementation details are published together with the dataset in the hope to push the research boundaries since all the techniques used in this work are not problem-specific and should be generalizable to help in other tasks and contexts .
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Original:  We introduce an open-source dynamic load balancing model  to address this problem within OpenFOAM, an open-source C++ library for Computational Fluid Dynamics (CFD).
Revised:  We introduce an open-source dynamic load balancing model named DLBFoam to address this problem within OpenFOAM, an open-source C++ library for Computational Fluid Dynamics (CFD).
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Original:  As a result of  highly non-linear characteristics of chemical kinetics, a large variation in  convergence rates of  ODE integrator may occur, leading to a high load imbalance across multiprocessor configurations.
Revised:  As a result of the highly non-linear characteristics of chemical kinetics, a large variation in convergence rates of ODE integrator may occur, leading to a high load imbalance across multiprocessor configurations.
Edit intent: fluency
Original:  As a result of  highly non-linear characteristics of chemical kinetics, a large variation in  convergence rates of  ODE integrator may occur, leading to a high load imbalance across multiprocessor configurations.
Revised:  As a result of highly non-linear characteristics of chemical kinetics, a large variation in the convergence rates of ODE integrator may occur, leading to a high load imbalance across multiprocessor configurations.
Edit intent: fluency
Original:  As a result of  highly non-linear characteristics of chemical kinetics, a large variation in  convergence rates of  ODE integrator may occur, leading to a high load imbalance across multiprocessor configurations.
Revised:  As a result of highly non-linear characteristics of chemical kinetics, a large variation in convergence rates of the ODE integrator may occur, leading to a high load imbalance across multiprocessor configurations.
Edit intent: fluency
Original:  When the load balancing is utilized together with a reference mapping model also introduced in this paper , a speed-up by a factor of 10 is reported for practical reactive flow simulations .
Revised:  When the load balancing is utilized together with a reference mapping model is utilized to further speed-up the simulations. When DLBFoam it utilized with both these features enabled , a speed-up by a factor of 10 is reported for practical reactive flow simulations .
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Original:  Although , the task of dialog response generation is generally seen as a sequence to sequence (Seq2Seq) problem, researchers in the past have found it challenging to train dialog systems using the standard Seq2Seq models.
Revised:  Although the task of dialog response generation is generally seen as a sequence to sequence (Seq2Seq) problem, researchers in the past have found it challenging to train dialog systems using the standard Seq2Seq models.
Edit intent: coherence
Original:  Although , the task of dialog response generation is generally seen as a sequence to sequence (Seq2Seq) problem, researchers in the past have found it challenging to train dialog systems using the standard Seq2Seq models.
Revised:  Although , the task of dialog response generation is generally seen as a sequence-to-sequence (Seq2Seq) problem, researchers in the past have found it challenging to train dialog systems using the standard Seq2Seq models.
Edit intent: fluency
Original:  In this paper, we  show how a standard transformer can be morphed into a hierarchical one by using specially designed attention masks and positional embeddings. Our experiments show strong improvements in context-to-response generation performance for task-oriented dialog system over the current state-of-the-art approaches .
Revised:  In this paper, we show how a standard transformer can be morphed into a hierarchical one by using specially designed attention masks and positional encodings. We demonstrate that Hierarchical Encoding helps achieve better natural language understanding of the contexts in transformer-based models for task-oriented dialog system over the current state-of-the-art approaches .
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Original: The edge connectivity of a simple graph is the least number of edges whose removal disconnects the graph. We study the quantum query complexity of computing the edge connectivity of a simple graph with n vertices and m edges in both the adjacency matrix and adjacency array models.
Revised: The edge connectivity of a simple graph is the least number of edges whose removal disconnects G. We completely characterize the quantum query complexity of computing the edge connectivity of a simple graph with n vertices and m edges in both the adjacency matrix and adjacency array models.
Edit intent: clarity
Original:  We study the quantum query complexity of computing the edge connectivity of a simple graph with n vertices and m edges in both the adjacency matrix and adjacency array models. For classical randomized algorithms,\tilde \tau) queries and time.
Revised:  We study the quantum query and time complexity of the minimum cut problem in the adjacency matrix model. If G has n vertices and edge weights at least 1 and at most \tau, we give a quantum algorithm to solve the minimum cut problem using\tilde \tau) queries and time.
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Original:  Moreover, for every integer 1 }\le \le \tau) many queries to the adjacency matrix of G. These results contrast with the classical randomized case where } \Omega(n^2) and \Omega(m) lower bounds are known for these models, respectively, showing that the trivial algorithm is optimal in terms of query complexity for both cases.
Revised:  Moreover, for every integer 1 }\le \tau\le \tau) many queries to the adjacency matrix of G. These results contrast with the classical randomized case where } \Omega(n^2) and \Omega(m) lower bounds are known for these models, respectively, showing that the trivial algorithm is optimal in terms of query complexity for both cases.
Edit intent: fluency
Original:  Moreover, for every integer 1 }\le \le \tau) many queries to the adjacency matrix of G. These results contrast with the classical randomized case where } \Omega(n^2) and \Omega(m) lower bounds are known for these models, respectively, showing that the trivial algorithm is optimal in terms of query complexity for both cases.
Revised:  Moreover, for every integer 1 }\le \le n we give an example of a graph G with edge weights 1 and \tau such that solving the minimum cut problem on G requires \Omega(n^{3/2\tau) many queries to the adjacency matrix of G. These results contrast with the classical randomized case where } \Omega(n^2) and \Omega(m) lower bounds are known for these models, respectively, showing that the trivial algorithm is optimal in terms of query complexity for both cases.
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Original:  Moreover, for every integer 1 }\le \le \tau) many queries to the adjacency matrix of G. These results contrast with the classical randomized case where } \Omega(n^2) and \Omega(m) lower bounds are known for these models, respectively, showing that the trivial algorithm is optimal in terms of query complexity for both cases. In contrast, we show upper bounds of \tilde O(n^{3/2\tilde  ) and \tilde O( \sqrt{mn + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
Revised:  Moreover, for every integer 1 }\le \le \tau) many queries to the adjacency matrix of G. These results contrast with the classical randomized case where } \Omega(n^2) queries to the adjacency matrix are needed in the worst case even to decide if an unweighted graph is connected or not. In the adjacency array model, when G has m edges the classical randomized complexity of the minimum cut problem is \tilde O(n^{3/2\tilde ) and \tilde O( \sqrt{mn + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
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Original:  In contrast, we show upper bounds of \tilde O(n^{3/2\tilde  ) and \tilde O( \sqrt{mn + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
Revised:  In contrast, we show upper bounds of \tilde \Theta(m). We show that the quantum query and time complexity are\tilde ) and \tilde O( \sqrt{mn + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
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Original:  In contrast, we show upper bounds of \tilde O(n^{3/2\tilde  ) and \tilde O( \sqrt{mn + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
Revised:  In contrast, we show upper bounds of \tilde O(n^{3/2\tilde O(\sqrt{mn\tau ) and \tilde O( \sqrt{mn + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
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Original:  In contrast, we show upper bounds of \tilde O(n^{3/2\tilde  ) and \tilde O( \sqrt{mn + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
Revised:  In contrast, we show upper bounds of \tilde O(n^{3/2\tilde ) and \tilde O( \sqrt{mn\tau + n^{3/2}), respectively, where again the edge weights are between 1 and \tau.
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Original:  For dense graphs we give lower bounds on the quantum query complexity of } \Omega(n ). Our upper bounds follow by combining a lemma on the structure of near-minimum cuts due to Rubinstein, Schramm and Weinberg (ITCS 2018) with a quantum sparsification algorithm) for \tau > 1 and \Omega(\tau n) for any 1 \leq \tau \leq n.
Revised:  For dense graphs we give lower bounds on the quantum query complexity of } \Omega(n ^{3/2) for \tau > 1 and \Omega(\tau n) for any 1 \leq \tau \leq n.
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Original:  Our query algorithm uses a quantum algorithm for graph sparsification } by Apers and de Wolf (FOCS 2020) . For a weighted graph, instead of edge connectivity the relevant notion is the weight of a minimum cut, i.e. the minimum total weight of a set of edges whose removal disconnects the graph. For weighted graphs we show that in the worst case \Omega(n^2) queries can be needed to compute the weight of a minimum cut by a quantum algorithm in both the adjacency matrix and adjacency array models, thus no quantum speedup is possible in this case .
Revised:  Our query algorithm uses a quantum algorithm for graph sparsification } by Apers and de Wolf (FOCS 2020) and results on the structure of near-minimum cuts by Kawarabayashi and Thorup (STOC 2015) and Rubinstein, Schramm and Weinberg (ITCS 2018). Our time efficient implementation builds on Karger's tree packing technique (STOC 1996) .
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Original:  Constrained RL algorithms approach this problem by training agents to maximize given reward functions while respectingexplicitly defined constraints. However, in many cases, manually designing accurate constraints is a challenging task .
Revised:  Constrained RL algorithms approach this problem by training agents to maximize given reward functions while respectingexplicitly for the real world deployment of reinforcement learning (RL), it is critical that RL agents are aware of these constraints, so that they can act safely .
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Original:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown constraints , we propose a framework to learn the most likely constraints that the expert respects.
Revised:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respecting constraints , we propose a framework to learn the most likely constraints that the expert respects.
Edit intent: clarity
Original:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown constraints , we propose a framework to learn the most likely constraints that the expert respects.
Revised:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown we consider the problem of learning constraints from demonstrations of a constraint-abiding agent's behavior. We experimentally validate our approach and show that our framework can successfully learn the most likely constraints that the expert respects.
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Original:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown constraints , we propose a framework to learn the most likely constraints that the expert respects.
Revised:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown constraints , we propose a framework to learn the most likely constraints that the agent respects.
Edit intent: clarity
Original:  We then train agents to maximize the given reward function subject to the learned constraints  . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .
Revised:  We then train agents to maximize the given reward function subject to the learned constraintstransferable . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .
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Original:  We then train agents to maximize the given reward function subject to the learned constraints  . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .
Revised:  We then train agents to maximize the given reward function subject to the learned constraints to new agents that may have different morphologies and/or reward functions . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .
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Original:  We then train agents to maximize the given reward function subject to the learned constraints  . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .
Revised:  We then train agents to maximize the given reward function subject to the learned constraints . Previous works in this regard have either mainly been restricted to tabular (discrete) settings, specific types of constraints or assume knowledge of transition dynamics of the environment .
Edit intent: meaning-changed
Original:  We then train agents to maximize the given reward function subject to the learned constraints  . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .
Revised:  We then train agents to maximize the given reward function subject to the learned constraints . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume the environment's transition dynamics .
Edit intent: clarity
Original: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio  of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Revised: We explore approximation algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Edit intent: clarity
Original: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio  of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Revised: We study harmonic-based algorithms for the d-dimensional geometric bin packing problem (dBP) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Edit intent: clarity
Original: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio  of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Revised: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) gave a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Edit intent: clarity
Original: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio  of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Revised: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm for dBP having an asymptotic approximation ratio of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Edit intent: clarity
Original: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio  of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Revised: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio (AAR) of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Edit intent: meaning-changed
Original: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio  of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) for dD BP and dD SP when items are not allowed to be rotated.
Revised: We study harmonic-based algorithms for the d-dimensional (dD) generalizations of three classical geometric packing problems: geometric bin packing (BP), strip packing (SP), and geometric knapsack (KS) . Caprara (MOR 2008) studied a harmonic-based algorithm \mathtt{HDH an asymptotic approximation ratio of T_{\infty}^{d-1} (where T_{\infty} \approx 1.691) . However, their algorithm doesn't allow items to be rotated.
Edit intent: coherence
Original:  We give fast and simple harmonic-based algorithms with asymptotic approximation ratios of T_{\infty^{d-1}, T_{\infty}^{d} and (1-\epsilon)3^{-d} for dD SP, dD BP and dD KS, respectively, when orthogonal rotations are allowed }  about all or a subset of axes.
Revised:  ^{d-1}, T_{\infty}^{d} and (1-\epsilon)3^{-d} for dD SP, dD BP and dD KS, respectively, when orthogonal rotations are allowed } about all or a subset of axes.
Edit intent: coherence
Original:  We give fast and simple harmonic-based algorithms with asymptotic approximation ratios of T_{\infty^{d-1}, T_{\infty}^{d} and (1-\epsilon)3^{-d} for dD SP, dD BP and dD KS, respectively, when orthogonal rotations are allowed }  about all or a subset of axes.
Revised:  We give fast and simple harmonic-based algorithms with asymptotic approximation ratios of T_{\infty^{d-1}, T_{\infty}^{d} and (1-\epsilon)3^{-d} for dD SP, dD BP and dD KS, respectively, when orthogonal rotations are allowed } This is in contrast to some common applications of dBP, like packing boxes into shipping containers. We give approximation algorithms for dBP when items can be orthogonally rotated about all or a subset of axes.
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Original:  This gives the first approximation algorithm for dD KS for d > 3. Furthermore, we provide a more sophisticated harmonic-based algorithm, which we call HGaP_k, that is T_{\infty}^{d-1}(1+\epsilon) -asymptotic-approximate for dD BP for the rotational case .
Revised:  We first give a fast and simple harmonic-based algorithm having AAR T_{\infty a more sophisticated harmonic-based algorithm, which we call HGaP_k, that is T_{\infty}^{d-1}(1+\epsilon) -asymptotic-approximate for dD BP for the rotational case .
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