wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we present Falcon 2.0,  a rule-based tool capable of accurately mapping entities and relations in short texts to resources in both DBpedia and Wikidatafollowing the same approach in both cases. The input of Falcon 2.0 is a short natural language text in the English language .
Revised:  In this paper, we present Falcon 2.0, a short natural language text in the English language .
Edit intent: clarity
Original:  Falcon 2.0 resorts to fundamental principles of the English morphology (e.g., N-Gram tiling and N-Gram splitting) and background knowledge of labels alignments obtained from studied knowledge graph to return as an output;
Revised:  Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting) and background knowledge of labels alignments obtained from studied knowledge graph to return as an output;
Edit intent: meaning-changed
Original:  Falcon 2.0 resorts to fundamental principles of the English morphology (e.g., N-Gram tiling and N-Gram splitting) and background knowledge of labels alignments obtained from studied knowledge graph to return as an output; the resulting entity and relation resources are either in the DBpedia or Wikidata knowledge graphs .
Revised:  Falcon 2.0 resorts to fundamental principles of the English morphology (e.g., N-Gram tiling and N-Gram splitting) , and then an optimization approach for linking task .
Edit intent: meaning-changed
Original:  We have empirically studied the impact using only Wikidata on Falcon 2.0 , and observed it is knowledge graph agnostic, i.e., Falcon 2.0 performance and behavior are not affected by the knowledge graph used as background knowledge .
Revised:  We have empirically studied the performance of Falcon 2.0 , and observed it is knowledge graph agnostic, i.e., Falcon 2.0 performance and behavior are not affected by the knowledge graph used as background knowledge .
Edit intent: clarity
Original:  We have empirically studied the impact using only Wikidata on Falcon 2.0 , and observed it is knowledge graph agnostic, i.e., Falcon 2.0 performance and behavior are not affected by the knowledge graph used as background knowledge .
Revised:  We have empirically studied the impact using only Wikidata on Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines .
Edit intent: meaning-changed
Original:  Falcon 2.0 is public and can be reused by the community . Additionally, Falcon 2.0 and its background knowledge bases are available as resources at URL
Revised:  Falcon 2.0 is public and can be reused by the community ; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository. We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at URL
Edit intent: meaning-changed
Original:  Then, we pass to consider a first-order mean-field approximation of the exact model . At this point, we dwell on the stability propertiesof the system by relying on the graph-theoretical notion of equitable partition.
Revised:  Then, we pass to consider a first-order mean-field approximation of the exact model and its stability properties, by relying on the graph-theoretical notion of equitable partition.
Edit intent: clarity
Original:  Here, we describe a rigorous derivation of error-backpropagation-based learning for hierarchical networks of leaky integrate-and-fire neurons . We explicitly address two issues that are relevant for both biological plausibility and applicability to neuromorphic substrates by incorporating dynamics with finite time constants and by optimizing the backward pass with respect to substrate variability. This narrows the gap between previous models of first-spike-time learning and biological neuronal dynamics, thereby also enabling fast and energy-efficient inference on analog neuromorphic devices that inherit these dynamics from their biological archetypes, which we demonstrate on two generations of the BrainScaleS analog neuromorphic architecture .
Revised:  Here, we describe a rigorous derivation of error-backpropagation-based learning for hierarchical networks of leaky integrate-and-fire neurons , relying solely on input and output spike times, and show how it can implement error backpropagation in hierarchical spiking networks. Furthermore, we emulate our framework on the BrainScaleS analog neuromorphic architecture .
Edit intent: coherence
Original:  This narrows the gap between previous models of first-spike-time learning and biological neuronal dynamics, thereby also enabling fast and energy-efficient inference on analog neuromorphic devices that inherit these dynamics from their biological archetypes, which we demonstrate on two generations of the BrainScaleS analog neuromorphic architecture .
Revised:  This narrows the gap between previous models of first-spike-time learning and biological neuronal dynamics, thereby also enabling fast and energy-efficient inference on analog neuromorphic devices that inherit these dynamics from their biological archetypes, which we demonstrate on two generations of the BrainScaleS-2 neuromorphic system and demonstrate its capability of harnessing the chip's speed and energy characteristics. Finally, we examine how our approach generalizes to other neuromorphic platforms by studying how its performance is affected by typical distortive effects induced by neuromorphic substrates .
Edit intent: meaning-changed
Original:  Here, we describe a rigorous derivation of learning such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how it can implement error backpropagation in hierarchical spiking networks.
Revised:  Here, we describe a rigorous derivation of a learning rule for such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how it can implement error backpropagation in hierarchical spiking networks.
Edit intent: clarity
Original:  Here, we describe a rigorous derivation of learning such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how it can implement error backpropagation in hierarchical spiking networks.
Revised:  Here, we describe a rigorous derivation of learning such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how this mechanism can implement error backpropagation in hierarchical spiking networks.
Edit intent: clarity
Original:  By both seeing and hearing, the agent must learn to navigate to an audio-based target. We develop a multi-modal deep reinforcement learning pipeline to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets.
Revised:  By both seeing and hearing, the agent must learn to navigate to a sounding object. We propose a multi-modal deep reinforcement learning pipeline to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets.
Edit intent: clarity
Original:  We develop a multi-modal deep reinforcement learning pipeline to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets.
Revised:  We develop a multi-modal deep reinforcement learning approach to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets.
Edit intent: clarity
Original:  We further introduce  audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments.
Revised:  We further introduce SoundSpaces: a first-of-its-kind dataset of audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments.
Edit intent: meaning-changed
Original:  We further introduce  audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments.
Revised:  We further introduce audio renderings based on geometrical acoustic simulations for two sets of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments.
Edit intent: meaning-changed
Original:  We further introduce  audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments.
Revised:  We further introduce audio renderings based on geometrical acoustic simulations for a set of publicly available 3D environments (Matterport3D and Replica), and we instrument Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments.
Edit intent: meaning-changed
Original:  We further introduce  audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments.
Revised:  We further introduce audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of real-world scanned environments.
Edit intent: meaning-changed
Original:  We propose that the lead bias can be leveraged in  a simple and effective way in our favor to pretrain abstractive news summarization models on large-scale unlabeled corpus : predicting the leading sentences using the rest of an article.
Revised:  We propose that the lead bias can be leveraged in our favor in a simple and effective way in our favor to pretrain abstractive news summarization models on large-scale unlabeled corpus : predicting the leading sentences using the rest of an article.
Edit intent: meaning-changed
Original:  We propose that the lead bias can be leveraged in  a simple and effective way in our favor to pretrain abstractive news summarization models on large-scale unlabeled corpus : predicting the leading sentences using the rest of an article.
Revised:  We propose that the lead bias can be leveraged in a simple and effective way to pre-train abstractive news summarization models on large-scale unlabeled corpus : predicting the leading sentences using the rest of an article.
Edit intent: clarity
Original:  We propose that the lead bias can be leveraged in  a simple and effective way in our favor to pretrain abstractive news summarization models on large-scale unlabeled corpus : predicting the leading sentences using the rest of an article.
Revised:  We propose that the lead bias can be leveraged in a simple and effective way in our favor to pretrain abstractive news summarization models on large-scale unlabeled news corpora : predicting the leading sentences using the rest of an article.
Edit intent: clarity
Original:  Via careful data cleaning and filtering , our transformer-based pretrained model without any finetuning achieves remarkable results over various news summarization tasks. With further finetuning, our model outperforms many competitive baseline models. Human evaluations further show the effectiveness of our method .
Revised:  Via careful data cleaning and filtering via statistical analysis. We then apply the proposed self-supervised pre-training to existing generation models BART and T5 for domain adaptation. Via extensive experiments on six benchmark datasets, we show that this approach can dramatically improve the summarization quality and achieve state-of-the-art results for zero-shot news summarization without any fine-tuning. For example, in the DUC2003 dataset, the ROUGE-1 score of BART increases 13.7\% after the lead-bias pre-training. We deploy the model in Microsoft News and provide public APIs as well as a demo website for multi-lingual news summarization .
Edit intent: meaning-changed
Original: Lead bias is a common phenomenon in news summarization, where early parts of an article often contain the most salient information . While many algorithms exploit this fact in summary generation , it has a detrimental effect on teaching the model to discriminate and extract important information in general.
Revised: Lead bias is a common phenomenon in news summarization, where early parts of an article often contain the most salient information in the beginning, also known as the lead bias. While this phenomenon can be exploited in generating a summary , it has a detrimental effect on teaching the model to discriminate and extract important information in general.
Edit intent: clarity
Original:  We propose that the lead bias can be leveraged in our favor in a simple and effective way to pre-train abstractive news summarization models on large-scale unlabeled news corpora: predicting the leading sentences using the rest of an article.
Revised:  We propose that this lead bias can be leveraged in our favor in a simple and effective way to pre-train abstractive news summarization models on large-scale unlabeled news corpora: predicting the leading sentences using the rest of an article.
Edit intent: fluency
Original:  Specifically, we aim to approximate the length of the largest of the N queues in the network. From a practical point of view, this has interesting applications, such as modeling the delays in a large supply chain. We present a fluid limit and a steady-state result for the maximum queue length , as N\to\infty.
Revised:  Specifically, we present a fluid limit and a steady-state result for the maximum queue length , as N\to\infty.
Edit intent: clarity
Original:  With the development of deep learning, there are more powerful feature extractors for option. And we think that comprehending the relationship between different feature extractors and data shall be the key. In this work, we put prior knowledge into alpha discovery neural network, combined with different kinds of feature extractors to do this task.
Revised:  With the development of deep learning, more choices to design this algorithm are available. This paper proposes Alpha Discovery Neural Network (ADNN), equipped with different kinds of feature extractors to do this task.
Edit intent: clarity
Original:  In this work, we put prior knowledge into alpha discovery neural network, combined with different kinds of feature extractors to do this task. We find that in the same type of network, simple network structure can produce more informative features than sophisticated network structure, and it costs less training time. However, complex network is good at providing more diversified features. In both experiment and real business environment, fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information  .
Revised:  In this work, we put prior knowledge into alpha discovery neural network, combined with different kinds of feature extractors to construct diversified financial technical factors based on prior knowledge. The experiment result shows that both fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information .
Edit intent: clarity
Original:  In both experiment and real business environment, fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information  .
Revised:  In both experiment and real business environment, fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information . ADNN effectively enrich the current factor pool because in all cases, ADNN can construct more informative and diversified features than GP. Moreover, features constructed by ADNN can always improve original strategy return, Sharpe ratio and max draw-down .
Edit intent: meaning-changed
Original:  The experiment result shows that both fully-connected network and recurrent network are good at extracting information from  financial time series , but convolution network structure can not effectively extract this information.
Revised:  We mainly made three contributions. First, we use domain knowledge in quantitative trading to design the sampling rules and object function. Second, pre-training and model pruning has been used to replace genetic programming, because it can conduct more efficient evolution process. Third, the feature extractors in ADNN can be replaced by different feature extractors and produce different functions. The experiment results show that ADNN can construct more informative and diversified features than GP, which can effectively enriches the current factor pool. The fully-connected network and recurrent network are good at extracting information from financial time series , but convolution network structure can not effectively extract this information.
Edit intent: meaning-changed
Original:  The experiment result shows that both fully-connected network and recurrent network are good at extracting information from  financial time series , but convolution network structure can not effectively extract this information. ADNN effectively enrich the current factor pool because in all cases, ADNN can construct more informative and diversified features than GP. Moreover, features constructed by ADNN can always improve original strategy return, Sharpe ratio and max draw-down  .
Revised:  The experiment result shows that both fully-connected network and recurrent network are good at extracting information from financial time series than the convolution neural network. In real practice, features constructed by ADNN can always improve original strategy return, Sharpe ratio and max draw-down .
Edit intent: clarity
Original:  Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse.
Revised:  Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and gpostsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse.
Edit intent: fluency
Original:  In this paper, we propose a stochastic investment model for  South Africa by modelling price inflation rates, share dividends, long term and short-term interest rates for the period 1960-2018 and inflation-linked bonds for the period 2000-2018.
Revised:  In this paper, we propose a stochastic investment model for actuarial use in South Africa by modelling price inflation rates, share dividends, long term and short-term interest rates for the period 1960-2018 and inflation-linked bonds for the period 2000-2018.
Edit intent: meaning-changed
Original:  Possible by-directional relations between the economic series have been considered and the model is designed to provide long-term forecasts that should find application in long-term modelling for both pension funds and life insurance companies .
Revised:  Possible bi-directional relations between the economic series have been considered and the model is designed to provide long-term forecasts that should find application in long-term modelling for both pension funds and life insurance companies .
Edit intent: fluency
Original:  Possible by-directional relations between the economic series have been considered and the model is designed to provide long-term forecasts that should find application in long-term modelling for both pension funds and life insurance companies .
Revised:  Possible by-directional relations between the economic series have been considered , the parameters and their confidence intervals have been estimated recursively to examine their stability and the model validation has been tested. The model is designed to provide long-term forecasts that should find application in long-term modelling for both pension funds and life insurance companies .
Edit intent: meaning-changed
Original:  Possible by-directional relations between the economic series have been considered and the model is designed to provide long-term forecasts that should find application in long-term modelling for both pension funds and life insurance companies .
Revised:  Possible by-directional relations between the economic series have been considered and the model is designed to provide long-term forecasts that should find application in long-term modelling for institutions such as pension funds and life insurance companies .
Edit intent: clarity
Original:  Possible by-directional relations between the economic series have been considered and the model is designed to provide long-term forecasts that should find application in long-term modelling for both pension funds and life insurance companies .
Revised:  Possible by-directional relations between the economic series have been considered and the model is designed to provide long-term forecasts that should find application in long-term modelling for both pension funds and life insurance companies in South Africa
Edit intent: meaning-changed
Original:  Often the regression may be high dimensional, depending on many variables. This paper gives minimal conditions for root-n consistent and efficient estimation of such objects when the regression and the Riesz representer of the functional are approximately sparse and the sum of the absolute value of the coefficients is bounded. The approximately sparse functions we consider are those where an approximation by some t regressors has root mean square error less than or equal to Ct^{-\xi for C, \xi>0.
Revised:  Often the regression may be high dimensional, for C, \xi>0.
Edit intent: clarity
Original:  This condition is stronger than the corresponding condition 1+2>1/2 for Holder classes of functions}  .
Revised:  This condition is stronger than the corresponding condition 1+2>1/2 for Holder classes of functions} approximately sparse regression. Such objects include a wide variety of interesting parameters such as the covariance between two regression residuals, a coefficient of a partially linear model, an average derivative, and the average treatment effect. We give lower bounds on the convergence rate of estimators of such objects and find that these bounds are substantially larger than in a low dimensional, semiparametric setting .
Edit intent: meaning-changed