wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1  with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Revised:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1 with only 128 examples on SQuAD , while maintaining competitive performance in the high-resource setting .
Edit intent: clarity
Original:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1  with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting . Our findings indicate that careful design of pretraining schemes and model architecture can have a dramatic effect on performance in the few-shot settings .
Revised:  The resulting model obtains surprisingly good results on multiple benchmarks , e.g., 72.7 F1 with only 128 examples on SQuAD , while maintaining competitive (and sometimes better) performance in the high-resource setting .
Edit intent: clarity
Original:  The new method is demonstrated to be effective on various high-order finite-difference and finite-element discretizations of linear parabolic and hyperbolic problems, demonstrating fast, scalable solution of up to 10th order accuracy.
Revised:  The new method can be used with arbitrary existing preconditioners for backward Euler-type time stepping schemes, and is amenable to the use of three-term recursion Krylov methods when the underlying spatial discretization is symmetric. The new method is demonstrated to be effective on various high-order finite-difference and finite-element discretizations of linear parabolic and hyperbolic problems, demonstrating fast, scalable solution of up to 10th order accuracy.
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Original:  In several cases, the new method can achieve 4th-order accuracy using Gauss integration with roughly half the number of preconditioner applications  as required using standard SDIRK techniques .
Revised:  The new method consistently outperforms existing block preconditioning approaches, and in several cases, the new method can achieve 4th-order accuracy using Gauss integration with roughly half the number of preconditioner applications as required using standard SDIRK techniques .
Edit intent: meaning-changed
Original:  In several cases, the new method can achieve 4th-order accuracy using Gauss integration with roughly half the number of preconditioner applications  as required using standard SDIRK techniques .
Revised:  In several cases, the new method can achieve 4th-order accuracy using Gauss integration with roughly half the number of preconditioner applications and wallclock time as required using standard SDIRK techniques .
Edit intent: meaning-changed
Original: Koopman mode decomposition and tensor component analysis are two tools that decompose high dimensional data sets into low dimensional modes that capture  relevant features and/or dynamics.
Revised: Koopman mode decomposition and tensor component analysis (also known as CANDECOMP/PARAFAC or canonical polyadic decomposition) are two popular approaches of decomposing high dimensional data sets into low dimensional modes that capture relevant features and/or dynamics.
Edit intent: meaning-changed
Original:  Despite their similar goal, the two methods are largely used by distinct scientific communities . For the first time, examine the two together and show that, under a certain (reasonable) condition on the data, the theoretical decomposition given by tensor component analysis is the same as that given by Koopman mode decomposition.
Revised:  Despite their similar goal, the two methods are largely used by different scientific communities and formulated in distinct mathematical languages. We examine the two together and show that, under a certain (reasonable) condition on the data, the theoretical decomposition given by tensor component analysis is the same as that given by Koopman mode decomposition.
Edit intent: meaning-changed
Original:  For the first time, examine the two together and show that, under a certain (reasonable) condition on the data, the theoretical decomposition given by tensor component analysis is the same as that given by Koopman mode decomposition.
Revised:  For the first time, examine the two together and show that, under a certain (reasonable) condition on the data, the theoretical decomposition given by tensor component analysis is the same as that given by Koopman mode decomposition.
Edit intent: fluency
Original:  This realization provides possibilities for new  algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Revised:  This provides a "bridge" with which the two communities should be able to more effectively communicate. When this condition is not met, Koopman mode decomposition still provides a tensor decomposition with an algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Edit intent: meaning-changed
Original:  This realization provides possibilities for new  algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Revised:  This realization provides possibilities for newa priori algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Edit intent: meaning-changed
Original:  This realization provides possibilities for new  algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Revised:  This realization provides possibilities for new computable error, providing an alternative to the non-convex optimization that tensor component analysis requires. Our work provides new possibilities for algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Edit intent: meaning-changed
Original:  This realization provides possibilities for new  algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Revised:  This realization provides possibilities for new algorithmic approaches to Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Edit intent: clarity
Original:  This realization provides possibilities for new  algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Revised:  This realization provides possibilities for new algorithmic approaches to both Koopman mode decomposition and tensor component analysis, provides a new perspective on the success of tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Edit intent: meaning-changed
Original:  This realization provides possibilities for new  algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis modes, and a "bridge" with which the two communities can more effectively communicate .
Revised:  This realization provides possibilities for new algorithmic approaches to both Koopman mode decomposition and tensor component analysis, new insight into what is being captured by the tensor component analysis , and builds upon a growing body of work showing that dynamical systems, and Koopman operator theory in particular, can be useful for problems that have historically made use of optimization theory .
Edit intent: meaning-changed
Original:  Numerically, we design a specific splitting scheme, under which we can prove the subsequential convergence for the alternating direction method of multipliers (ADMM)  . Experimentally, we demonstrate visible improvements of L1/L2 over L1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CT reconstruction.
Revised:  Numerically, we design a specific splitting scheme, under which we can prove subsequential and global convergence for the alternating direction method of multipliers (ADMM) . Experimentally, we demonstrate visible improvements of L1/L2 over L1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CT reconstruction.
Edit intent: meaning-changed
Original:  Numerically, we design a specific splitting scheme, under which we can prove the subsequential convergence for the alternating direction method of multipliers (ADMM)  . Experimentally, we demonstrate visible improvements of L1/L2 over L1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CT reconstruction.
Revised:  Numerically, we design a specific splitting scheme, under which we can prove the subsequential convergence for the alternating direction method of multipliers (ADMM) under certain conditions . Experimentally, we demonstrate visible improvements of L1/L2 over L1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CT reconstruction.
Edit intent: clarity
Original:  Most prior work analysing the citations between patents and scientific publications focussed on the front-page citations  , which are well structured and provided in the metadata of patent archives such as Google Patents. In the 2019 paper by Verberne et al., the authors evaluate two sequence labelling methods for extracting references from patents: Conditional Random Fields (CRF) and Flair .
Revised:  Most prior work analysing the citations between patents and scientific publications focussed on the front-page citations , which are well structured and provided in the metadata of patent archives . In this paper we build on prior work using Conditional Random Fields (CRF) and Flair .
Edit intent: clarity
Original:  In the 2019 paper by Verberne et al., the authors evaluate two sequence labelling methods for extracting references from patents: Conditional Random Fields (CRF) and Flair . In this paper we extend that work, by (1) improving the quality of the training data and (2) applying BERT-based models to the problem.
Revised:  In the 2019 paper by Verberne et al., the authors evaluate two sequence labelling methods for extracting references from patents: Conditional Random Fields (CRF) and Flair for reference extraction. We improve the quality of the training data and (2) applying BERT-based models to the problem.
Edit intent: clarity
Original:  In this paper we extend that work, by (1) improving the quality of the training data and (2) applying BERT-based models to the problem. We use error analysis throughout our work to find problems in the dataset, improve our models and reason about the types of errors different modelsare susceptible to. We first discuss the work by Verberne et al. and other related work in Section2. We describe the improvements we make in the dataset, and the new models proposed for this task.
Revised:  In this paper we extend that work, by (1) improving the quality of the training data and (2) applying BERT-based models on the labelled data (BERT, bioBERT, sciBERT). We find that the improved training data leads to a large improvement in the quality of the trained models. In addition, the BERT models beat CRF and Flair, with recall scores around 97\% obtained with cross validation. With the best model we label a large collection of 33 thousand patents, extract the citations, and match them to publications in the Web of Science database. We extract 50\% more references than with the new models proposed for this task.
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Original:  We describe the improvements we make in the dataset, and the new models proposed for this task. We compare the results of our new models with previous results, both on the labelled dataset and a larger unlabelled corpus. We end with a discussion on the characteristics of the results of our new models, followed by a conclusion. Our code and improved dataset are released under an open-source license on github .
Revised:  We describe the improvements we make in the dataset, and the old training data and methods: 735 thousand references in total. With these patent-publication links, follow-up research will further analyze which types of scientific work lead to inventions .
Edit intent: meaning-changed
Original:  After a semantic analysis of the data, information is stored in a knowledge graph which is  used to comprehend instructions, expressed in natural language , and execute the required tasks in a deterministic manner.
Revised:  After a semantic analysis of the sensory and other related data, the processed information is stored in a knowledge graph which is used to comprehend instructions, expressed in natural language , and execute the required tasks in a deterministic manner.
Edit intent: meaning-changed
Original:  After a semantic analysis of the data, information is stored in a knowledge graph which is  used to comprehend instructions, expressed in natural language , and execute the required tasks in a deterministic manner.
Revised:  After a semantic analysis of the data, information is stored in a knowledge graph which is used to comprehend instructions, expressed in natural language . This imparts industrial robots cognitive behavior to execute the required tasks in a deterministic manner.
Edit intent: meaning-changed
Original:  The clan embedding has multiplicative distortion t if for every pair  x,y  some copy y'\in f(y) in the clan of y is close to the chief of x: \min_{y'\in f(y)}d(y',\chi(x))\le t\cdot d(x,y).
Revised:  The clan embedding has multiplicative distortion t if for every pair ( x,y some copy y'\in f(y) in the clan of y is close to the chief of x: \min_{y'\in f(y)}d(y',\chi(x))\le t\cdot d(x,y).
Edit intent: fluency
Original:  The clan embedding has multiplicative distortion t if for every pair  x,y  some copy y'\in f(y) in the clan of y is close to the chief of x: \min_{y'\in f(y)}d(y',\chi(x))\le t\cdot d(x,y).
Revised:  The clan embedding has multiplicative distortion t if for every pair x,y ) some copy y'\in f(y) in the clan of y is close to the chief of x: \min_{y'\in f(y)}d(y',\chi(x))\le t\cdot d(x,y).
Edit intent: fluency
Original:  Next we turn to minor free graphs , who were previously stochastically embedded into bounded treewidth graphs with expected additive distortion \epsilon D (D being the diameter) .
Revised:  We then focus on minor-free graphs of diameter prameterized by D, which were known to be stochastically embeddable into bounded treewidth graphs with expected additive distortion \epsilon D (D being the diameter) .
Edit intent: meaning-changed
Original:  We use this embeddings to construct the first (bicriteria quasi-polynomial ) approximation schemes for the metric \rho-dominating set and metric \rho-independent set problems in minor free graphs.
Revised:  We use these embeddings to construct the first (bicriteria quasi-polynomial ) approximation schemes for the metric \rho-dominating set and metric \rho-independent set problems in minor free graphs.
Edit intent: fluency
Original:  We use this embeddings to construct the first (bicriteria quasi-polynomial ) approximation schemes for the metric \rho-dominating set and metric \rho-independent set problems in minor free graphs.
Revised:  We use this embeddings to construct the first (bicriteria quasi-polynomial time) approximation scheme for the metric \rho-dominating set and metric \rho-independent set problems in minor free graphs.
Edit intent: meaning-changed
Original:  We use this embeddings to construct the first (bicriteria quasi-polynomial ) approximation schemes for the metric \rho-dominating set and metric \rho-independent set problems in minor free graphs.
Revised:  We use this embeddings to construct the first (bicriteria quasi-polynomial ) approximation schemes for the metric \rho-dominating set and metric \rho-independent set problems in minor-free graphs.
Edit intent: fluency
Original:  Given a matrix A, it produces a low-rank approximation of the form {\bf UBV}^T, where U and V are approximately orthogonal and B is block bidiagonal.
Revised:  Given a matrix A, it produces a low-rank approximation of the form {\bf UBV}^T, where U and V have orthonormal columns in exact arithmetic and B is block bidiagonal.
Edit intent: meaning-changed
Original:  It{\bf  {\bf   is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Revised:  In finite precision, the columns of both{\bf {\bf is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Edit intent: meaning-changed
Original:  It{\bf  {\bf   is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Revised:  It{\bf U {\bf is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Edit intent: meaning-changed
Original:  It{\bf  {\bf   is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Revised:  It{\bf and{\bf is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Edit intent: meaning-changed
Original:  It{\bf  {\bf   is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Revised:  It{\bf {\bf V is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Edit intent: meaning-changed
Original:  It{\bf  {\bf   is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Revised:  It{\bf {\bf will be close to orthonormal. Our algorithm is closely related to the randQB algorithms of Yu, Gu, and Li (2018) in that the entries of B are incrementally generated and the Frobenius norm approximation error may be efficiently estimated.
Edit intent: meaning-changed
Original:  We  demonstrate how various functional form, independence, and monotonicity assumptions can be imposed as constraints in our optimization procedure to tighten the identified set, and we show how these assumptions have meaningful interpretations in terms of restrictions on latent types.
Revised:  We leverage results on hyperplane arrangements and cell enumeration from the literature on computational geometry in order to provide a tractable means of computing the identified set. We demonstrate how various functional form, independence, and monotonicity assumptions can be imposed as constraints in our optimization procedure to tighten the identified set, and we show how these assumptions have meaningful interpretations in terms of restrictions on latent types.
Edit intent: meaning-changed
Original:  We  demonstrate how various functional form, independence, and monotonicity assumptions can be imposed as constraints in our optimization procedure to tighten the identified set, and we show how these assumptions have meaningful interpretations in terms of restrictions on latent types.
Revised:  We demonstrate how various functional form, independence, and monotonicity assumptions can be imposed as constraints in our optimization procedure to tighten the identified set, and we show how these assumptions can be assigned meaningful interpretations in terms of restrictions on latent types.
Edit intent: clarity
Original:  We  demonstrate how various functional form, independence, and monotonicity assumptions can be imposed as constraints in our optimization procedure to tighten the identified set, and we show how these assumptions have meaningful interpretations in terms of restrictions on latent types. In the special case when the index function is linear in the latent variables, we leverage results in computational geometry to provide a tractable means of constructing the sharp set of constraints for our optimization problems .
Revised:  We demonstrate how various functional form, independence, and monotonicity assumptions can be imposed as constraints in our optimization procedure to tighten the identified set, and we show how these assumptions have meaningful interpretations in terms of restrictions on latent response types .
Edit intent: clarity
Original:  Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4 - 4.0 x for INT8 inference on a T4 GPU system as compared to FP32 inference.
Revised:  Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4 -4.0 x for INT8 inference on a T4 GPU system as compared to FP32 inference.
Edit intent: fluency
Original:  We publicly release PhoNLP as an open-source toolkit under the MIT License .
Revised:  We publicly release PhoNLP as an open-source toolkit under the Apache License 2.0. Although we specify PhoNLP for Vietnamese, our PhoNLP training and evaluation command scripts in fact can directly work for other languages that have a pre-trained BERT-based language model and gold annotated corpora available for the three tasks of POS tagging, NER and dependency parsing.
Edit intent: meaning-changed
Original:   We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees  ]  .
Revised:  Here, we study this price for the following clustering problems: k-means, k-medians, k-centers and maximum-spacing. We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees ] .
Edit intent: meaning-changed