wanyu/IteraTeR_v2 huggingface dataset:
Original: Training a task-completion dialogue agent with real users via reinforcement learning (RL) could be prohibitively expensive, because it requires many interactions with  users.
Revised: Training a task-completion dialogue agent with real users via reinforcement learning (RL) could be prohibitively expensive, because it requires many interactions with real users.
Edit intent: fluency
Original:  One  alternative is to resort to a user simulator , while the discrepancy of between simulated and real users makes the learned policy unreliable in practice.
Revised:  One common alternative is to resort to a user simulator , while the discrepancy of between simulated and real users makes the learned policy unreliable in practice.
Edit intent: clarity
Original:  One  alternative is to resort to a user simulator , while the discrepancy of between simulated and real users makes the learned policy unreliable in practice.
Revised:  One alternative is to use a user simulator , while the discrepancy of between simulated and real users makes the learned policy unreliable in practice.
Edit intent: clarity
Original:  The effectiveness of our approach is validated on a movie-booking task in both a simulation setting and a human-in-the-loop setting .
Revised:  The effectiveness of our approach is demonstrated on a movie-ticket booking task in both a simulation setting and a human-in-the-loop setting .
Edit intent: clarity
Original:  The effectiveness of our approach is validated on a movie-booking task in both a simulation setting and a human-in-the-loop setting .
Revised:  The effectiveness of our approach is validated on a movie-booking task in both simulated and human-in-the-loop setting .
Edit intent: fluency
Original:  The effectiveness of our approach is validated on a movie-booking task in both a simulation setting and a human-in-the-loop setting .
Revised:  The effectiveness of our approach is validated on a movie-booking task in both a simulation setting and a human-in-the-loop settings .
Edit intent: fluency
Original:  We show that A-T-LAS_{2,1} is more efficient than convex relaxation and exhibits similar performance to the state of the art method SPF, outperforming it in strong noise regimes and for matrices whose singular vectors do not possess exact (joint-) sparse support . Moreover, contrary to SPF, A-T-LAS_{2,1 .
Revised:  We show that A-T-LAS_{2,1} exhibits similar performance to the state of the art method SPF, outperforming it in strong noise regimes and for matrices whose singular vectors do not possess exact (joint-) sparse support . Moreover, contrary to SPF, A-T-LAS_{2,1 .
Edit intent: coherence
Original:  We show that A-T-LAS_{2,1} is more efficient than convex relaxation and exhibits similar performance to the state of the art method SPF, outperforming it in strong noise regimes and for matrices whose singular vectors do not possess exact (joint-) sparse support . Moreover, contrary to SPF, A-T-LAS_{2,1 .
Revised:  We show that A-T-LAS_{2,1} is more efficient than convex relaxation and exhibits similar performance to the state of the art method SPF, outperforming it in strong noise regimes and for matrices whose singular vectors do not possess exact (joint-) sparse support .
Edit intent: coherence
Original: Compressed Sensing is about recovering an unknown vector of dimension n from m \ll n linear measurements .
Revised: We consider the problem of recovering an unknown vector of dimension n from m \ll n linear measurements .
Edit intent: clarity
Original:  For this purpose we{\it   propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it  S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Revised:  For this purpose we{\it propose a novel algorithm, called{\it S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Edit intent: coherence
Original:  For this purpose we{\it   propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it  S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Revised:  For this purpose we{\it propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it A S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Edit intent: fluency
Original:  For this purpose we{\it   propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it  S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Revised:  For this purpose we{\it propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it T S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Edit intent: fluency
Original:  For this purpose we{\it   propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it  S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Revised:  For this purpose we{\it propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it ikhonov regularization and S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Edit intent: fluency
Original:  For this purpose we{\it   propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it  S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Revised:  For this purpose we{\it propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it Las S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Edit intent: fluency
Original:  For this purpose we{\it   propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it  S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Revised:  For this purpose we{\it propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it so S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Edit intent: fluency
Original:  For this purpose we{\it   propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it  S is based on a multi-penalty regularization for recovery of low-rank matrices with approximately sparse singular vectors , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Revised:  For this purpose we{\it propose a novel algorithm, which we call Alternating Tikhonov regularization and Lasso (A-T-LAS_{2,1{\it S is based on a multi-penalty regularization , which is able to leverage both structures (low-rankness and sparsity) simultaneously and effectively reduce further the number of necessary measurements with respect to the sole use of one of the two sparsity structures.
Edit intent: coherence
Original:  We provide local recovery guarantees of A-T-LAS_{2,1{\it   the one of other similar approaches such as Sparse Power Factorization (SPF).
Revised:  We provide local recovery guarantees of A-T-LAS_{2,1{\it any the one of other similar approaches such as Sparse Power Factorization (SPF).
Edit intent: fluency
Original:  We show that A-T-LAS_{2,1 in strong noise regimes and for matrices  whose singular vectors do not possess exact (joint-) sparse support.
Revised:  We show that A-T-LAS_{2,1 in strong noise regimes and for matrices , whose singular vectors do not possess exact (joint-) sparse support.
Edit intent: fluency
Original: We consider the problem of recovering an unknown effectively sparse low-rank matrix from a small set of incomplete and inaccurate linear measurements of the form y = \mathcal A (X) + \eta, where \eta is an %DIFDELCMD < {\it %%% ineliminable noise.
Revised: We consider the problem of recovering an unknown effectively sparse low-rank matrix from a small set of incomplete and inaccurate linear measurements of the form y = \mathcal A (X) + \eta, where \eta is an %DIFDELCMD < {\it %%% noise.
Edit intent: clarity
Original:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% A lternatingT ikhonov regularization andLas so (A-T-LA  _{2,1}), to solve it.
Revised:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% lternatingT ikhonov regularization andLas so (A-T-LA _{2,1}), to solve it.
Edit intent: fluency
Original:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% A lternatingT ikhonov regularization andLas so (A-T-LA  _{2,1}), to solve it.
Revised:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% A T ikhonov regularization andLas so (A-T-LA _{2,1}), to solve it.
Edit intent: fluency
Original:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% A lternatingT ikhonov regularization andLas so (A-T-LA  _{2,1}), to solve it.
Revised:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% A lternating ikhonov regularization andLas so (A-T-LA _{2,1}), to solve it.
Edit intent: fluency
Original:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% A lternatingT ikhonov regularization andLas so (A-T-LA  _{2,1}), to solve it.
Revised:  We describe an optimization problem for matrix recovery under the considered model and propose a novel algorithm, called %DIFDELCMD < {\it %DIFDELCMD < %%% A lternatingT ikhonov regularization and so (A-T-LA _{2,1}), to solve it.
Edit intent: fluency
Original:  We prove global convergence for %DIFDELCMD < {\it %%% any linear measurement model to stationary points and local convergence to global minimizers of the multi-penalty objective functional.
Revised:  We prove global convergence for %DIFDELCMD < {\it %%% any linear measurement model to stationary points and local convergence to global minimizers of the multi-penalty objective functional.
Edit intent: fluency
Original:  However, machine learning is typically just one processing stage in complex end-to-end applications, which involve multiple components in a mobile Systems-on-a-chip (SoC).
Revised:  However, machine learning is typically just one processing stage in complex end-to-end applications, involving multiple components in a mobile Systems-on-a-chip (SoC).
Edit intent: coherence
Original:  Focusing on just ML accelerators loses bigger optimization opportunity at the system (SoC) level.
Revised:  Focusing only on ML accelerators loses bigger optimization opportunity at the system (SoC) level.
Edit intent: fluency
Original:  Our semantics leads to the study of two main computational problems , namely the possibility and certainty of query answers.
Revised:  Our semantics leads to the study of two main computational problems : the possibility and certainty of query answers.
Edit intent: clarity
Original:  Traditional techniques use structural as well as node  similarities to find matches of a given query graph in a (large) target graph.
Revised:  Traditional techniques use structural as well as node similarities to find matches of a query in the target graph.
Edit intent: clarity
Original:  However, almost all previous research has tacitly ignored the presence of relationships and context (usually manifested in the form of node/edge label distributions) in the query.
Revised:  However, these techniques have ignored the presence of relationships and context (usually manifested in the form of node/edge label distributions) in the query.
Edit intent: clarity
Original:  However, almost all previous research has tacitly ignored the presence of relationships and context (usually manifested in the form of node/edge label distributions) in the query.
Revised:  However, almost all previous research has tacitly ignored the presence of context (usually manifested in the form of node/edge label distributions) in the query.
Edit intent: clarity
Original:  In this paper, we propose CGQ -- Relationship-Aware Contextual Graph Querying for real-world graphs.
Revised:  In this paper, we propose CGQ ( Contextual Graph Querying for real-world graphs.
Edit intent: coherence
Original:  Given a query graph and a target graph, CGQ identifies the (top-k) maximal common subgraphs between the query and the target graphs with the highest contextual similarity.
Revised:  Given a query graph and a target graph, aims to identify the (top-k) maximal common subgraphs between the query and the target graphs with the highest contextual similarity.
Edit intent: clarity
Original:  Given a query graph and a target graph, CGQ identifies the (top-k) maximal common subgraphs between the query and the target graphs with the highest contextual similarity.
Revised:  Given a query graph and a target graph, CGQ identifies the (top-k) maximal common subgraph(s) between the query and the target graphs with the highest contextual similarity.
Edit intent: clarity
Original:  To overcome this computational bottleneck, we propose a hierarchical index, CGQ-Tree, with its associated CGQ search algorithm.
Revised:  To overcome this computational bottleneck, we propose a hierarchical index, CGQ-Tree, with its associated search algorithm.
Edit intent: fluency
Original:  Empirically, the CGQ search algorithm is capable of achieving speed-ups of up to three orders of magnitude over a baseline strategy.
Edit intent: coherence
Original: Graph querying is the task of finding similar embeddings of a given query graph in a large target graph . Existing techniques employ the use of structural as well as node and edge label similarities to find matches of a query in the target graph.
Revised: The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node and edge label similarities to find matches of a query in the target graph.
Edit intent: clarity
Original: Graph querying is the task of finding similar embeddings of a given query graph in a large target graph . Existing techniques employ the use of structural as well as node and edge label similarities to find matches of a query in the target graph.
Revised: Graph querying is the task of finding similar embeddings of a given query graph in a large target graph . Existing techniques employ the use of structural as well as node similarities to find matches of a query in the target graph.
Edit intent: clarity
Original: Graph querying is the task of finding similar embeddings of a given query graph in a large target graph . Existing techniques employ the use of structural as well as node and edge label similarities to find matches of a query in the target graph.
Revised: Graph querying is the task of finding similar embeddings of a given query graph in a large target graph . Existing techniques employ the use of structural as well as node and edge label similarities to find matches of a given query graph in a (large) target graph.
Edit intent: fluency
Original:  However, these techniques have ignored the presence of context (usually manifested in the form of node /edge label distributions) in the query .
Revised:  However, almost all existing techniques have tacitly ignored the presence of context (usually manifested in the form of node /edge label distributions) in the query .
Edit intent: clarity
Original:  However, these techniques have ignored the presence of context (usually manifested in the form of node /edge label distributions) in the query .
Revised:  However, these techniques have ignored the presence of relationships in graphs, which are usually encoded through interactions between node and edge labels .
Edit intent: clarity
Original:  Our experiments show that the proposed CGQ index is effective, efficient  and scalable.
Revised:  Our experiments show that the proposed CGQ index is effective, efficient , and scalable.
Edit intent: fluency
Original: In this paper, the edge caching problem in fog radio access networks (F-RAN) is investigated.
Revised: In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated.
Edit intent: fluency
Original:  We consider content popularity in terms of time and space  from the perspective of regional users.
Revised:  We consider content popularity in terms of time and space is considered from the perspective of regional users.
Edit intent: clarity
Original:  Taking into account that users request the contents they are interested in, we propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the "Online Gradient Descent" (OGD) method and the "Follow The (Proximally) Regularized Leader" (FTRL-Proximal) method.
Revised:  We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the "Online Gradient Descent" (OGD) method and the "Follow The (Proximally) Regularized Leader" (FTRL-Proximal) method.
Edit intent: coherence
Original:  Taking into account that users request the contents they are interested in, we propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the "Online Gradient Descent" (OGD) method and the "Follow The (Proximally) Regularized Leader" (FTRL-Proximal) method.
Revised:  Taking into account that users request the contents they are interested in, we propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the "Follow The (Proximally) Regularized Leader" (FTRL-Proximal) method.
Edit intent: fluency
Original:  Taking into account that users request the contents they are interested in, we propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the "Online Gradient Descent" (OGD) method and the "Follow The (Proximally) Regularized Leader" (FTRL-Proximal) method.
Revised:  Taking into account that users request the contents they are interested in, we propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the "Online Gradient Descent" (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method.
Edit intent: fluency
Original:  Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low computational complexity, but it also  can track the content popularity with spatial and temporal popularity dynamics in time without delay.
Revised:  Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, can track the content popularity with spatial and temporal popularity dynamics in time without delay.
Edit intent: clarity
Original:  Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low computational complexity, but it also  can track the content popularity with spatial and temporal popularity dynamics in time without delay.
Revised:  Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low computational complexity, but it also can track the content popularity with spatial and temporal popularity dynamic in time without delay.
Edit intent: fluency
Original: For any class C of bipartite graphs, we define quasi-C\cal  to be the class of all graphs G such that every bipartition of G belongs to \cal C.
Revised: For any class C of bipartite graphs, we define quasi-\cal to be the class of all graphs G such that every bipartition of G belongs to \cal C.
Edit intent: fluency
Original: For any class C of bipartite graphs, we define quasi-C\cal  to be the class of all graphs G such that every bipartition of G belongs to \cal C.
Revised: For any class C of bipartite graphs, we define quasi-C\cal C to be the class of all graphs G such that every bipartition of G belongs to \cal C.
Edit intent: fluency