wanyu/IteraTeR_full_sent huggingface dataset:
Original:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient  . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Revised:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient . Going forward, we will expand these models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Edit intent: fluency
Original:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient  . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Revised:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Edit intent: fluency
Original:  While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input - the representation at a given layer can only access representations from lower layers, rather than the higher level representations already built in previous time steps .
Revised:  While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input . The representation at a given layer can only access representations from lower layers, rather than the higher level representations already built in previous time steps .
Edit intent: fluency
Original:  While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input - the representation at a given layer can only access representations from lower layers, rather than the higher level representations already built in previous time steps .
Revised:  While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input - the representation at a given layer can only access representations from lower layers, rather than the higher level representations already available .
Edit intent: clarity
Original: Local graph clustering algorithms are designed to efficiently detect small clusters of nodes that are biased to a localized region of a large graph. Although many techniques have been developed for local clustering in graphs, very few algorithms have been designed to detect local clusters in hypergraphs, which better model complex systems involving multiway relationships between data objects. In this paper we present a framework for local clustering in hypergraphs based on minimum cuts and maximum flows.
Revised: Hypergraphs are a useful abstraction for modeling multiway relationships in data, and hypergraph clustering is the task of detecting groups of closely related nodes in such data. Graph clustering has been studied extensively, and there are numerous methods for detecting small, localized clusters without having to explore an entire input graph. However, there are only a few specialized approaches for localized clustering in hypergraphs. Here we present a framework for local clustering in hypergraphs based on minimum cuts and maximum flows.
Edit intent: clarity
Original:  In this paper we present a framework for local clustering in hypergraphs based on minimum cuts and maximum flows. Our approach extends previous research on flow-based local graph clustering, but has been generalized in a number of key ways. First of all, we demonstrate how to incorporate recent results on generalized hypergraph s-t cut problems .
Revised:  In this paper we present a framework for local hypergraph clustering based on minimizing localized ratio cut objectives. Our framework takes an input set of reference nodes in a hypergraph and solves a sequence of hypergraph minimum s-t cut problems .
Edit intent: meaning-changed
Original:  First of all, we demonstrate how to incorporate recent results on generalized hypergraph s-t cut problems . This allows us to accommodate a wide range of different hypergraph cut functions, which can assign different penalties based on how each hyperedgeis split across different clusters. Furthermore, our algorithm comes with a number of attractive theoretical properties in terms of recovering nodes sets with low hypergraph conductance and hypergraph normalized cut scores.
Revised:  First of all, we demonstrate how to incorporate recent results on generalized hypergraph s-t cut problems in order to identify a nearby well-connected cluster of nodes that overlaps substantially with the input set. Our methods extend graph-based techniques but are significantly more general and have new output quality guarantees. First, our methods can minimize new generalized notions of hypergraph cuts, which depend on specific configurations of nodes within each hyperedge, rather than just on the number of cut hyperedges. Second, our framework has several attractive theoretical properties in terms of recovering nodes sets with low hypergraph conductance and hypergraph normalized cut scores.
Edit intent: meaning-changed
Original:  Furthermore, our algorithm comes with a number of attractive theoretical properties in terms of recovering nodes sets with low hypergraph conductance and hypergraph normalized cut scores. Finally, and most importantly, our method is strongly-local, meaning that its runtime depends only on the size of an input set.
Revised:  Furthermore, our algorithm comes with a number of attractive theoretical properties in terms of output cluster quality. Most importantly, our method is strongly-local, meaning that its runtime depends only on the size of an input set.
Edit intent: clarity
Original:  Finally, and most importantly, our method is strongly-local, meaning that its runtime depends only on the size of an input set. In practice this allows our method to quickly find localized clusters without exploring an entire input hypergraph . We demonstrate the power of our method in local cluster detection experiments on an Amazon product hypergraph and a Stack Overflow question hypergraph. Although both datasets involve millions of nodes, millions of edges, and a large average hyperedge size , we are able to detect local clusters in a matter of a few seconds or a few minutes , depending on the size of the cluster .
Revised:  Finally, and most importantly, our method is strongly-local, meaning that its runtime depends only on the size of the input set, and does not need to explore the entire hypergraph to find good local clusters. We use our methodology to effectively identify clusters in hypergraphs of real-world data with millions of nodes, millions of edges, and a large average hyperedge size , we are able to detect local clusters in a matter of a few seconds or a few minutes , depending on the size of the cluster .
Edit intent: clarity
Original:  Although both datasets involve millions of nodes, millions of edges, and a large average hyperedge size , we are able to detect local clusters in a matter of a few seconds or a few minutes , depending on the size of the cluster .
Revised:  Although both datasets involve millions of nodes, millions of hyperedges, and large average hyperedge size , we are able to detect local clusters in a matter of a few seconds or a few minutes , depending on the size of the cluster .
Edit intent: clarity
Original:  Although both datasets involve millions of nodes, millions of edges, and a large average hyperedge size , we are able to detect local clusters in a matter of a few seconds or a few minutes , depending on the size of the cluster .
Revised:  Although both datasets involve millions of nodes, millions of edges, and a large average hyperedge size with runtimes ranging between a few seconds and a few minutes , depending on the size of the cluster .
Edit intent: clarity
Original:  Although both datasets involve millions of nodes, millions of edges, and a large average hyperedge size , we are able to detect local clusters in a matter of a few seconds or a few minutes , depending on the size of the cluster .
Revised:  Although both datasets involve millions of nodes, millions of edges, and a large average hyperedge size , we are able to detect local clusters in a matter of a few seconds or a few minutes .
Edit intent: clarity
Original: Collaborative text editing and versioning is known to be a tough topic. Diffs, OT and CRDT are three relevant classes of algorithms which all have their issues. CRDT is the only one that works correctly and deterministically in a distributed environment, at the unfortunate cost of data structure complexity and metadata overheads. A chronofold is a data structure for editable linear collections based on the Causal Tree CRDT model.
Revised: Chronofold is a replicated data structure for versioned text, based on the Causal Tree CRDT model.
Edit intent: clarity
Original:  A chronofold is a data structure for editable linear collections based on the Causal Tree CRDT model. A chronofold maintains time-ordering and space-ordering of its elements. Simply put, a it is both a log and a text at the same time, which makes it very convenient for text versioning and synchronization. Being a simple array-based data structure with O(1) insertions, chronofold makes CRDT overheads acceptable for many practical applications .
Revised:  A chronofold is a data structure for editable linear collections based on the extended Causal Tree model. Past models of this kind either retrofitted local linear orders to a distributed system (the OT approach) or employed distributed data models locally (the CRDT approach). That caused either extreme fragility in a distributed setting or egregious overheads in local use. Overall, that local/distributed impedance mismatch is cognitively taxing and causes lots of complexity. We solve that by using subjective linear orders locally at each replica, while inter-replica communication uses a distributed model. A separate translation layer insulates local data structures from the distributed environment. We modify the Lamport timestamping scheme to make that translation as trivial as possible. We believe our approach has applications beyond the domain of collaborative editing .
Edit intent: meaning-changed
Original: Producing natural and accurate responses like human beings is the ultimate goal of intelligent dialogue agents. So far, most of the past works concentrate on selecting or generating one pertinent and fluent response according to current query and its context. These models work on a one-to-one environment, making one response to one utterance each round.However, in real human-human conversations, human often sequentially sends several short messages for readability instead of a long message in one turn.
Revised: Different people have different habits of describing their intents in conversations. Some people may tend to deliberate their full intents in several successive utterances, i.e., they use several consistent messages for readability instead of a long message in one turn.
Edit intent: clarity
Original: However, in real human-human conversations, human often sequentially sends several short messages for readability instead of a long message in one turn. Thus messages will not end with an explicit ending signal, which is crucial for agents to decide when to reply. So the first step for an intelligent dialogue agent is not replying but deciding if it should reply at the moment. To address this issue, in this paper , we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.
Revised: However, in real human-human conversations, human often sequentially sends several short messages for readability instead of a long sentence to express their question. This creates a predicament faced by dialogue systems' application, especially in real-world industrial scenarios, in which the dialogue system is unsure that whether it should answer the user's query immediately or wait for users' further supplementary input. Motivated by such interesting quandary, we define a novel task: Wait-or-Answer to better tackle this dilemma faced by dialogue systems. We shed light on a new research topic about how the dialogue system can be more competent to behave in this Wait-or-Answer quandary. Further , we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.
Edit intent: meaning-changed
Original:  To address this issue, in this paper , we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.
Revised:  To address this issue, in this paper , we propose a predictive approach dubbed Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.
Edit intent: clarity
Original:  To address this issue, in this paper , we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.
Revised:  To address this issue, in this paper , we propose a novel Imagine-then-Arbitrate (ITA) to resolve this Wait-or-Answer task. More specifically, we take advantage of an arbitrator model to help the agent decide whether to wait or to make a response directly.
Edit intent: meaning-changed
Original:  To address this issue, in this paper , we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly. Our method has two imaginator modules and an arbitrator module. The two imaginators will learn the agent's and user's speaking style respectively, generate possible utterances as the input of the arbitrator, combining with dialogue history. And the arbitrator decides whether to wait or to make a response to the user directly. To verify the performance and effectiveness of our method, we prepared two dialogue datasets and compared our approach with several popular models. Experimental results show that our model performs well on addressing ending prediction issue and outperforms baseline models .
Revised:  To address this issue, in this paper , we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or answer. The arbitrator's decision is made with the assistance of two ancillary imaginator models: a wait imaginator and an answer imaginator. The wait imaginator tries to predict what the user would supplement and use its prediction to persuade the arbitrator that the user has some information to add, so the dialogue system should wait. The answer imaginator, nevertheless, struggles to predict the answer of the dialogue system and convince the arbitrator that it's a superior choice to answer the users' query immediately. To our best knowledge, our paper is the first work to explicitly define the Wait-or-Answer task in the dialogue system. Additionally, our proposed ITA approach significantly outperforms the existing models in solving this Wait-or-Answer problem .
Edit intent: meaning-changed
Original: Different people have different habits of describing their intents in conversations. Some people may tend to deliberate their full intents in several successive utterances, i. e., they use several consistent messages for readability instead of a long sentence to express their question.
Revised: Producing natural and accurate responses like human beings is the ultimate goal of intelligent dialogue agents. So far, most of the past works concentrate on selecting or generating one pertinent and fluent response according to current query and its context. These models work on a one-to-one environment, making one response to one utterance each round. However, in real human-human conversations, human often sequentially sends several short messages for readability instead of a long sentence to express their question.
Edit intent: meaning-changed
Original:  Some people may tend to deliberate their full intents in several successive utterances, i. e., they use several consistent messages for readability instead of a long sentence to express their question. This creates a predicament faced by dialogue systems' application, especially in real-world industrial scenarios, in which the dialogue system is unsure that whether it should answer the user's query immediately or wait for users' further supplementary input. Motivated by such interesting quandary, we define a novel task: Wait-or-Answer to better tackle this dilemma faced by dialogue systems. We shed light on a new research topic about how the dialogue system can be more competent to behave in this Wait-or-Answer quandary. Further , we propose a predictive approach dubbed Imagine-then-Arbitrate (ITA) to resolve this Wait-or-Answer task.
Revised:  Some people may tend to deliberate their full intents in several successive utterances, i. e., they use several consistent messages for readability instead of a long message in one turn. Thus messages will not end with an explicit ending signal, which is crucial for agents to decide when to reply. So the first step for an intelligent dialogue agent is not replying but deciding if it should reply at the moment. To address this issue, in this paper , we propose a predictive approach dubbed Imagine-then-Arbitrate (ITA) to resolve this Wait-or-Answer task.
Edit intent: meaning-changed
Original:  Further , we propose a predictive approach dubbed Imagine-then-Arbitrate (ITA) to resolve this Wait-or-Answer task. More specifically, we take advantage of an arbitrator model to help the dialogue system decide to wait or answer.
Revised:  Further , we propose a predictive approach dubbed Imagine-then-Arbitrate (ITA) neural dialogue model to help the dialogue system decide to wait or answer.
Edit intent: clarity
Original:  More specifically, we take advantage of an arbitrator model to help the dialogue system decide to wait or answer. The arbitrator's decision is made with the assistance of two ancillary imaginator models: a wait imaginator and an answer imaginator. The wait imaginator tries to predict what the user would supplement and use its prediction to persuade the arbitrator that the userhas some information to add, so the dialogue system should wait. The answer imaginator, nevertheless, struggles to predict the answer of the dialogue system and convince the arbitrator that it's a superior choice to answer the users' query immediately. To our best knowledge, our paper is the first work to explicitly define the Wait-or-Answer task in the dialogue system. Additionally, our proposed ITA approach significantly outperforms the existing modelsin solving this Wait-or-Answer problem .
Revised:  More specifically, we take advantage of an arbitrator model to help the dialogue system decide to wait or to make a response directly. Our method has two imaginator modules and an arbitrator module. The two imaginators will learn the agent's and user's speaking style respectively, generate possible utterances as the input of the arbitrator, combining with dialogue history. And the arbitrator decides whether to wait or to make a response to the user directly. To verify the performance and effectiveness of our method, we prepared two dialogue datasets and compared our approach with several popular models. Experimental results show that our model performs well on addressing ending prediction issue and outperforms baseline models .
Edit intent: meaning-changed
Original:  Instead of adapting a single system, we train many translation systems using different architectures and data partitions . Using bandit learning techniques on simulated user feedback, we learn a policy to choose which system to use for a particular translation task.
Revised:  Instead of adapting a single system, we train many translation systems using different architectures , datasets, and optimization methods . Using bandit learning techniques on simulated user feedback, we learn a policy to choose which system to use for a particular translation task.
Edit intent: clarity
Original:  amounts to the effective size of its event space, and can be quantified using the R\'enyi family of indices (also known as Hill numbers in ecology or Hannah-Kay indices in economics), which are indexed by an elasticity parameter q \geq 0. Importantly, under these indices, the heterogeneity of a composite system (the \gamma-heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the \alpha- and \beta -heterogeneities , respectively).
Revised:  is the effective size of its event space, and can be quantified using the R\'enyi family of indices (also known as Hill numbers in ecology or Hannah-Kay indices in economics), which are indexed by an elasticity parameter q \geq 0. Importantly, under these indices, the heterogeneity of a composite system (the \gamma-heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the \alpha- and \beta -heterogeneities , respectively).
Edit intent: fluency
Original:  amounts to the effective size of its event space, and can be quantified using the R\'enyi family of indices (also known as Hill numbers in ecology or Hannah-Kay indices in economics), which are indexed by an elasticity parameter q \geq 0. Importantly, under these indices, the heterogeneity of a composite system (the \gamma-heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the \alpha- and \beta -heterogeneities , respectively).
Revised:  amounts to the effective size of its event space, and can be quantified using the R\'enyi family of indices (also known as Hill numbers in ecology or Hannah-Kay indices in economics), which are indexed by an elasticity parameter q \geq 0. Under these indices, the heterogeneity of a composite system (the \gamma-heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the \alpha- and \beta -heterogeneities , respectively).
Edit intent: coherence