wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Training on randomly sampled 1, 093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30, 022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset  .
Revised:  Training on randomly sampled 1, 093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30, 022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset . We also have good results for a real severely low resource Mayan language, Eastern Pokomchi .
Edit intent: meaning-changed
Original:  In this paper, we propose an innovate Relevance-aware Anomalous Users Detection model ( RAUD) , which extracts and learns both the explicit and implicit relevance from meta data based on heterogeneous information network (HIN) and GNNs. We employ two distinct exploring layers including a self-attention layer and a similarity calculation layer to fully consider the users' intimacy across different relationships, and a following fusion layer based on graph convolution network(GCN) to consolidate the similarity and enhance the node embedding .
Revised:  In this paper, we propose an innovate Relevance-aware Anomalous Users Detection model ( RAU-GNN) to obtain a fine-grained detection result. RAU-GNN first extracts multiple relations of all types of users in social network, including both benign and anomalous users, and accordingly constructs the multiple user relation graph. Secondly, we employ relevance-aware GNN framework to learn the hidden features of users, and discriminate the anomalous users after discriminating. Concretely, by integrating Graph Convolution Network(GCN) and Graph Attention Network(GAT), we design a GCN-based relation fusion layer to aggregate initial information from different relations, and a GAT-based embedding layer to obtain the high-level embeddings. Lastly, we feed the learned representations to the following GNN layer in order to consolidate the similarity and enhance the node embedding .
Edit intent: meaning-changed
Original:  We employ two distinct exploring layers including a self-attention layer and a similarity calculation layer to fully consider the users' intimacy across different relationships, and a following fusion layer based on graph convolution network(GCN) to consolidate the similarity and enhance the node embedding . We evaluate our proposed model with real-world datasets and results show our approach can achieve over 90\% accuracy for user classification, far better than other comparable baselines .
Revised:  We employ two distinct exploring layers including a self-attention layer and a similarity calculation layer to fully consider the users' intimacy across different relationships, and a following fusion layer based on graph convolution network(GCN) to consolidate the node embedding by aggregating the final users' embeddings. We conduct extensive experiment on real-world datasets and results show our approach can achieve over 90\% accuracy for user classification, far better than other comparable baselines .
Edit intent: clarity
Original:  Here we augment the original CLUE approach, to provide what we call \delta-CLUE. CLUE indicates one way to change an input, while remaining on the data manifold, such that the model becomes more confident about its prediction.
Revised:  Here we augment the original CLUE approach, to provide what we call \delta-CLUE. CLUE indicates way to change an input, while remaining on the data manifold, such that the model becomes more confident about its prediction.
Edit intent: clarity
Original:  Here we augment the original CLUE approach, to provide what we call \delta -CLUE. CLUE indicates one way to change an input, while remaining on the data manifold, such that the model becomes more confident about its prediction.
Revised:  Here we augment the original CLUE approach, to provide what we call \delta -CLUE. CLUE indicates one way to change an input, while remaining on the data manifold, such that the model becomes more confident about its prediction.
Edit intent: fluency
Original:  given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations:
Revised:  given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. In this work, we propose a new model, QA-GNN, which addresses the above challenges through two key innovations:
Edit intent: clarity
Original:  (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing .
Revised:  (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph neural networks .
Edit intent: clarity
Original: We propose temporally abstract soft actor-critic ( TASAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner.
Revised: We propose temporally abstract actor-critic ( TASAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner.
Edit intent: clarity
Original: We propose temporally abstract soft actor-critic ( TASAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner.
Revised: We propose temporally abstract soft actor-critic ( TAAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner.
Edit intent: fluency
Original: We propose temporally abstract soft actor-critic ( TASAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner.
Revised: We propose temporally abstract soft actor-critic ( TASAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic (SAC) framework in a simple manner.
Edit intent: fluency
Original:  TASAC adds a second-stage binary policy to choose between the previous action and the action output by an SAC actor. It has two benefits compared to traditional off-policy RL algorithms : persistent exploration and an unbiased multi-step Q operator for TD learning .
Revised:  TASAC adds a second-stage binary policy to choose between the previous action and the action output by an actor. Crucially, its act-or-repeat decision hinges on the actually sampled action instead of the expected behavior of the actor. This post-acting switching scheme let the overall policy make more informed decisions. TAAC has two important features : persistent exploration and an unbiased multi-step Q operator for TD learning .
Edit intent: meaning-changed
Original:  We demonstrate its advantages over several strong baselines across 5 different categories of 14 continuous control tasks , in terms of both sample efficiency and final performance. Because of its simplicity and generality, TASAC can serve as a drop-in replacement for SAC when temporal abstraction is needed.
Revised:  We demonstrate its advantages over several strong baselines across 5 different categories of 14 continuous control tasks . Code is available at URL
Edit intent: meaning-changed
Original:  This paper is intended as a survey and motivation for discussion, and does not claim to give definitive answers to the questions posed. We speculate that these questions are related, and that the nonlinear operators used in implicitly compositional language models may inform explicit compositional modelling .
Revised:  A third and newer question is whether a compositional model can be implemented on a quantum computer. Given the fundamentally linear nature of quantum mechanics, we propose that these questions are related, and that the nonlinear operators used in implicitly compositional language models may inform explicit compositional modelling .
Edit intent: meaning-changed
Original:  Compared with the only existing second order ensemble method that combines the two-step BDF timestepping scheme and a special explicit second order Adams-Bashforth treatment of the advection term, this method is more accurate with nominal increase in computational cost.
Revised:  Compared with the only existing second-order ensemble method that combines the two-step BDF timestepping scheme and a special explicit second order Adams-Bashforth treatment of the advection term, this method is more accurate with nominal increase in computational cost.
Edit intent: fluency
Original:  Compared with the only existing second order ensemble method that combines the two-step BDF timestepping scheme and a special explicit second order Adams-Bashforth treatment of the advection term, this method is more accurate with nominal increase in computational cost.
Revised:  Compared with the only existing second order ensemble method that combines the two-step BDF timestepping scheme and a special explicit second-order Adams-Bashforth treatment of the advection term, this method is more accurate with nominal increase in computational cost.
Edit intent: fluency
Original:  In this paper, we propose a new decomposed multi-agent soft actor-critic (mSAC) method, which incorporates the idea of the multi-agent value function decomposition and soft policy iteration framework effectively and is a combination of novel and existing techniques, including decomposed Q value network architecture, decentralized probabilistic policy , and counterfactual advantage function ( optional ).
Revised:  In this paper, we propose a new decomposed multi-agent soft actor-critic (mSAC) method, which effectively combines the advantages of the aforementioned two methods. The main modules include decomposed Q network architecture, decentralized probabilistic policy , and counterfactual advantage function ( optional ).
Edit intent: clarity
Original:  In this paper, we propose a new decomposed multi-agent soft actor-critic (mSAC) method, which incorporates the idea of the multi-agent value function decomposition and soft policy iteration framework effectively and is a combination of novel and existing techniques, including decomposed Q value network architecture, decentralized probabilistic policy , and counterfactual advantage function ( optional ).
Revised:  In this paper, we propose a new decomposed multi-agent soft actor-critic (mSAC) method, which incorporates the idea of the multi-agent value function decomposition and soft policy iteration framework effectively and is a combination of novel and existing techniques, including decomposed Q value network architecture, discrete probabilistic policy and counterfactual advantage function ( optional ).
Edit intent: meaning-changed
Original:  In this paper, we propose a new decomposed multi-agent soft actor-critic (mSAC) method, which incorporates the idea of the multi-agent value function decomposition and soft policy iteration framework effectively and is a combination of novel and existing techniques, including decomposed Q value network architecture, decentralized probabilistic policy , and counterfactual advantage function ( optional ).
Revised:  In this paper, we propose a new decomposed multi-agent soft actor-critic (mSAC) method, which incorporates the idea of the multi-agent value function decomposition and soft policy iteration framework effectively and is a combination of novel and existing techniques, including decomposed Q value network architecture, decentralized probabilistic policy , and counterfactual advantage function ( optinal ).
Edit intent: fluency
Original:   The first and second chapters focus on Adam Smith and Karl Marx, in the broad classical tradition of economics, where it is believed that there is an inseparable relationship between production and distribution, economic growth and inequality.
Revised:  It begins with Adam Smith's critique of Rousseau. The first and second chapters focus on Adam Smith and Karl Marx, in the broad classical tradition of economics, where it is believed that there is an inseparable relationship between production and distribution, economic growth and inequality.
Edit intent: meaning-changed
Original:  Chapters 3 and 4 argue that despite the fact that the founders of the neoclassical school had shown an active interest in  worker poverty, the issues of production and distribution became discussed separately among neoclassicals.
Revised:  Chapters 3 and 4 argue that despite the fact that the founders of the neoclassical school had shown an active interest in social issues, namely worker poverty, the issues of production and distribution became discussed separately among neoclassicals.
Edit intent: meaning-changed
Original:  Then, in chapters 9 and 10, I will discuss the fact that Thomas Piketty's seminal work is a new development in this "inequality renaissance," and try to gain a perspective on future trends in the debate.
Revised:  Then, in chapters 9 and 10, I discuss the fact that Thomas Piketty's seminal work is a new development in this "inequality renaissance," and try to gain a perspective on future trends in the debate.
Edit intent: clarity
Original:  Experimental results on the state-of-the-art face recognition model show that, compared with the  methods that without PTB, the proposed attack method can significantly improve the performance of backdoor attacks in real physical world.
Revised:  Experimental results on the state-of-the-art face recognition model show that, compared with the backdoor methods that without PTB, the proposed attack method can significantly improve the performance of backdoor attacks in real physical world.
Edit intent: fluency
Original:  Under various complex physical conditions, by injecting only a very small ratio (0.5\%) of backdoor instances, the  success rate of physical backdoor attacks with the PTB method on VGGFace is 82\%, while the attack success rate of backdoor attacks without the proposed PTB method is lower than 11\%.
Revised:  Under various complex physical conditions, by injecting only a very small ratio (0.5\%) of backdoor instances, the attack success rate of physical backdoor attacks with the PTB method on VGGFace is 82\%, while the attack success rate of backdoor attacks without the proposed PTB method is lower than 11\%.
Edit intent: fluency
Original: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k  ]  .
Revised: We give an algorithm, that given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k ] .
Edit intent: fluency
Original: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k  ]  .
Revised: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k . This is the first 2-approximation algorithm for treewidth that is faster than the known exact algorithms. In particular, our algorithm improves upon both the previous best approximation ratio of 5 in time 2^{O(k) ] .
Edit intent: meaning-changed
Original: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k  ]  .
Revised: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k FOCS 2013, SICOMP 2016] .
Edit intent: meaning-changed
Original: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k  ]  .
Revised: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k ]. Our algorithm is based on a local improvement method adapted from a proof of Bellenbaum and Diestel .
Edit intent: meaning-changed
Original: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k  ]  .
Revised: We give an algorithm, which given an n-vertex graph G and an integer k, in time 2^{O(k)} n either outputs a tree decomposition of G of width at most 2k + 1 or determines that the treewidth of G is larger than k ] Comb. Probab. Comput. 2002 .
Edit intent: meaning-changed
Original:  In this paper, we show how large pretrained language models can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to their pretraining objective: We utilize their generative abilities to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.
Revised:  In this paper, we show how large PLMs can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to their pretraining objective: We utilize their generative abilities to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.
Edit intent: clarity
Original:  In this paper, we show how large pretrained language models can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to their pretraining objective: We utilize their generative abilities to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.
Revised:  In this paper, we show how large pretrained language models can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to the pretraining objective: We utilize their generative abilities to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.
Edit intent: fluency
Original:  In this paper, we show how large pretrained language models can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to their pretraining objective: We utilize their generative abilities to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.
Revised:  In this paper, we show how large pretrained language models can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to their pretraining objective: We utilize the generative abilities of PLMs to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.
Edit intent: clarity
Original:  How to effectively train multilingual models has not been well studied. Using multilingual machine translation as a testbed, we study optimization challenges from loss landscape and parameter plasticity perspectives. We found that imbalanced trainingdata poses task interference between high and low resource languages , characterized by nearly orthogonal gradients for major parameters and the optimization trajectory being mostly dominated by high resource .
Revised:  How to effectively train multilingual models has not been well understood. In this paper, we show that a common situation in multilingual training, data imbalance among languages, poses optimization tension between high resource and low resource languages , characterized by nearly orthogonal gradients for major parameters and the optimization trajectory being mostly dominated by high resource .
Edit intent: clarity
Original:  We show that local curvature of the loss surface affects the degree of interference, and existing heuristics of data subsampling implicitly reduces the sharpness, although still face a trade-off between high and low resource languages . We propose a principled multi-objective optimization algorithm, Curvature Aware Task Scaling (CATS), which improves both optimization and generalization especially for low resource.
Revised:  We show that common training method which upsamples low resources can not robustly optimize population loss with risks of either underfitting high resource languages or overfitting low resource ones. Drawing on recent findings on the geometry of loss landscape and its effect on generalization, we propose a principled multi-objective optimization algorithm, Curvature Aware Task Scaling (CATS), which improves both optimization and generalization especially for low resource.
Edit intent: meaning-changed
Original:  We propose a principled multi-objective optimization algorithm, Curvature Aware Task Scaling (CATS), which improves both optimization and generalization especially for low resource. Experiments on TED, WMT and OPUS-100 benchmarks demonstrate that CATS advances the Pareto front of accuracy while being efficient to apply to massive multilingual settings at the scale of 100 languages.
Revised:  We propose a principled multi-objective optimization algorithm, Curvature Aware Task Scaling (CATS), which adaptively rescales gradients from different tasks with a meta objective of guiding multilingual training to low-curvature neighborhoods with uniformly low loss for all languages. We ran experiments on common benchmarks ( TED, WMT and OPUS-100 benchmarks demonstrate that CATS advances the Pareto front of accuracy while being efficient to apply to massive multilingual settings at the scale of 100 languages.
Edit intent: meaning-changed
Original:  Experiments on TED, WMT and OPUS-100 benchmarks demonstrate that CATS advances the Pareto front of accuracy while being efficient to apply to massive multilingual settings at the scale of 100 languages.
Revised:  Experiments on TED, WMT and OPUS-100 ) with varying degrees of data imbalance. CATS effectively improved multilingual optimization and as a result demonstrated consistent gains on low resources ( to BLEU) without hurting high resources. In addition, CATS is robust to overparameterization and large batch size training, making it a promising training method for massive multilingual models that truly improve low resource languages.
Edit intent: meaning-changed
Original:  Due to this data privacy concern, MPs may be reluctant to contribute their datasets to MA. Using ideas from cryptography, we propose a distributed computation protocol between a MA and a MP in which MA obtains insights from mobility data without MP having to reveal its trade secrets or sensitive data of its users.
Revised:  Due to this data privacy concern, MPs may be reluctant to contribute their datasets to MA. Using ideas from cryptography, we propose an interactive protocol between a MA and a MP in which MA obtains insights from mobility data without MP having to reveal its trade secrets or sensitive data of its users.
Edit intent: clarity