wanyu/IteraTeR_v2 huggingface dataset:
Original:  Existing algorithms are extended in theory to make them more applicable for real-world experiments.
Revised:  Existing algorithms are extended in theory to make them more applicable to real-world experiments.
Edit intent: fluency
Original:  Previous proofs were restricted to a specific learning function , which only depends on the tracking error derivative (D-type ILC).
Revised:  Previous proofs were restricted to a specific learning function that only depends on the tracking error derivative (D-type ILC).
Edit intent: fluency
Original:  This extension provides more degrees of freedom in the ILC design and, as a result, better performance can be achieved.
Revised:  Our extension provides more degrees of freedom in the ILC design and, as a result, better performance can be achieved.
Edit intent: fluency
Original:  Experiments with two quadrotors attest the practical applicability of the proposed distributed multi-agent ILC approach.
Revised:  Experiments with two quadrotors attest the effectiveness of the proposed distributed multi-agent ILC approach.
Edit intent: clarity
Original:  The key insight is based on a novel proof illustrating the label bias problem and showing that globally normalized models can be strictly more expressive than locally normalized models.
Revised:  The key insight is that the label bias problem and showing that globally normalized models can be strictly more expressive than locally normalized models.
Edit intent: clarity
Original:  The key insight is based on a novel proof illustrating the label bias problem and showing that globally normalized models can be strictly more expressive than locally normalized models.
Revised:  The key insight is based on a novel proof illustrating the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models.
Edit intent: clarity
Original: In computer vision, Convolutional Neural Networks (CNNs) have recently achieved new levels of performance for several inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance.
Revised: In computer vision, convolutional neural networks (CNNs) have recently achieved new levels of performance for several inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance.
Edit intent: fluency
Original:  The resulting Deep Shading simulates all screen-space effects as well as arbitrary combinations thereof at competitive quality and speed while not being programmed by human experts but learned from example images.
Revised:  The resulting Deep Shading simulates various screen-space effects as well as arbitrary combinations thereof at competitive quality and speed while not being programmed by human experts but learned from example images.
Edit intent: clarity
Original:  The resulting Deep Shading simulates all screen-space effects as well as arbitrary combinations thereof at competitive quality and speed while not being programmed by human experts but learned from example images.
Revised:  The resulting Deep Shading simulates all screen-space effects at competitive quality and speed while not being programmed by human experts but learned from example images.
Edit intent: clarity
Original: Transfer learning is aimed to make use of valuable knowledge in a source domain to help the model performance in a target domain.
Revised: Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain.
Edit intent: coherence
Original:  It is particularly important to neural networks because neural models are very likely to be overfitting.
Revised:  It is particularly important to neural networks , which are very likely to be overfitting.
Edit intent: clarity
Original:  In this paper, we conduct a series of empirical studies and provide an illuminating picture on the transferability of neural networks in NLP.
Revised:  In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.
Edit intent: clarity
Original: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment or e.g.  a component of Memory Networks.
Revised: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment or e.g. \ a component of Memory Networks.
Edit intent: fluency
Original:  We argue that  such tasks are similar from the model perspective (especially in the context of high-capacity deep neural models) and propose new baselines by comparing the performance of  popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets.
Revised:  We argue that all such tasks are similar from the model perspective (especially in the context of high-capacity deep neural models) and propose new baselines by comparing the performance of popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets.
Edit intent: clarity
Original:  We argue that  such tasks are similar from the model perspective (especially in the context of high-capacity deep neural models) and propose new baselines by comparing the performance of  popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets.
Revised:  We argue that such tasks are similar from the model perspective and propose new baselines by comparing the performance of popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets.
Edit intent: clarity
Original:  To address the current research fragmentation in a future-proof way, we introduce a unified open source software framework with easily pluggable models  , allowing easy evaluation  on a wide range of semantic natural language tasks .
Revised:  To address the current research fragmentation in a future-proof way, we introduce a unified open source software framework with easily pluggable models and tasks , allowing easy evaluation on a wide range of semantic natural language tasks .
Edit intent: clarity
Original:  To address the current research fragmentation in a future-proof way, we introduce a unified open source software framework with easily pluggable models  , allowing easy evaluation  on a wide range of semantic natural language tasks .
Revised:  To address the current research fragmentation in a future-proof way, we introduce a unified open source software framework with easily pluggable models , allowing easy evaluation on a wide range of semantic natural language datasets .
Edit intent: clarity
Original:  This allows us to outline a path towards a universal machine learned semantic model for machine reading tasks.
Revised:  This allows us to outline a path towards a universal learned semantic model for machine reading tasks.
Edit intent: clarity
Original:  We support this plan by experiments that demonstrate reusability of models trained on different tasks, even across corpora of very different nature.
Revised:  We support this plan by experiments that demonstrate reusability of trained sentence models across tasks and corpora of very different nature.
Edit intent: clarity
Original: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment  or e.g. \ a component of Memory Networks.
Revised: We review the task of Sentence Pair Scoring, popular in the literature in various forms - viewed as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment or e.g. \ a component of Memory Networks.
Edit intent: clarity
Original: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment  or e.g. \ a component of Memory Networks.
Revised: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment or e.g. \ a component of Memory Networks.
Edit intent: clarity
Original: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment  or e.g. \ a component of Memory Networks.
Revised: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment , Paraphrasing or e.g. \ a component of Memory Networks.
Edit intent: fluency
Original: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment  or e.g. \ a component of Memory Networks.
Revised: We review the task of Sentence Pair Scoring, popular in the literature in various forms --- slanted as Answer Sentence Selection, Paraphrasing, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment or e.g. a component of Memory Networks.
Edit intent: fluency
Original:  To address the current research fragmentation in a future-proof way, we introduce a unified open source software framework with easily pluggable models and tasks, allowing easy evaluation of sentence models on a wide range of semantic natural language datasets.
Revised:  We introduce a unified open source software framework with easily pluggable models and tasks, allowing easy evaluation of sentence models on a wide range of semantic natural language datasets.
Edit intent: coherence
Original:  We support this plan by experiments that demonstrate reusability of trained sentence models across tasks and corpora of very different nature .
Revised:  We support this plan by experiments that demonstrate reusability of trained sentence model .
Edit intent: clarity
Original: We present a deep learning approach for speeding up constrained procedural modeling.
Edit intent: coherence
Original:  We augment procedural models with neural networks : these networks control how the model makes random choices based on what output it has generated thus far.
Revised:  We augment procedural models with neural networks which control how the model makes random choices based on what output it has generated thus far.
Edit intent: clarity
Original:  We augment procedural models with neural networks : these networks control how the model makes random choices based on what output it has generated thus far.
Revised:  We augment procedural models with neural networks : these networks control how the model makes random choices based on the output it has generated thus far.
Edit intent: clarity
Original:  We call such a model a neurally-guided procedural model .
Revised:  We call such models neurally-guided procedural model .
Edit intent: fluency
Original:  We call such a model a neurally-guided procedural model .
Revised:  We call such a model a neurally-guided procedural models .
Edit intent: fluency
Original:  As a pre-computation, we train these models on constraint-satisfying example outputs generated via SMC. They are then used as efficient importance samplers for SMC , generating high-quality results with very few samples.
Revised:  As a pre-computation, we train these models on constraint-satisfying example outputs generated via SMC. They are then used as efficient SMC importance samplers , generating high-quality results with very few samples.
Edit intent: clarity
Original: Given a word \( w \) over a finite alphabet, we consider, in three special cases, the generalised star-height of the languages in which \( w \) occurs as a contiguous subword (factor) an exact number of times and of the languages in which \( w \) occurs as a contiguous subword modulo a fixed number, and prove that in each case it is at most one.
Revised: Given a word w over a finite alphabet, we consider, in three special cases, the generalised star-height of the languages in which \( w \) occurs as a contiguous subword (factor) an exact number of times and of the languages in which \( w \) occurs as a contiguous subword modulo a fixed number, and prove that in each case it is at most one.
Edit intent: fluency
Original: Given a word \( w \) over a finite alphabet, we consider, in three special cases, the generalised star-height of the languages in which \( w \) occurs as a contiguous subword (factor) an exact number of times and of the languages in which \( w \) occurs as a contiguous subword modulo a fixed number, and prove that in each case it is at most one.
Revised: Given a word \( w \) over a finite alphabet, we consider, in three special cases, the generalised star-height of the languages in which w occurs as a contiguous subword (factor) an exact number of times and of the languages in which \( w \) occurs as a contiguous subword modulo a fixed number, and prove that in each case it is at most one.
Edit intent: fluency
Original: Given a word \( w \) over a finite alphabet, we consider, in three special cases, the generalised star-height of the languages in which \( w \) occurs as a contiguous subword (factor) an exact number of times and of the languages in which \( w \) occurs as a contiguous subword modulo a fixed number, and prove that in each case it is at most one.
Revised: Given a word \( w \) over a finite alphabet, we consider, in three special cases, the generalised star-height of the languages in which \( w \) occurs as a contiguous subword (factor) an exact number of times and of the languages in which w occurs as a contiguous subword modulo a fixed number, and prove that in each case it is at most one.
Edit intent: fluency
Original:  Finally, phase plane portrait are generated to describe the dynamics between susceptible fractions of human hosts and infected fractions of mosquito vectors 
Revised:  Finally, phase plane portraits are generated to describe the dynamics between susceptible fractions of human hosts and infected fractions of mosquito vectors 
Edit intent: fluency
Original:  Finally, phase plane portrait are generated to describe the dynamics between susceptible fractions of human hosts and infected fractions of mosquito vectors 
Revised:  Finally, phase plane portrait are generated to describe the dynamics between susceptible fractions of human hosts and infected fractions of mosquito vectors .
Edit intent: fluency
Original: Mutation rates and fitness costs of deleterious mutations are difficult to measure in vivo but essential for a quantitative understanding of of evolution.
Revised: Mutation rates and fitness costs of deleterious mutations are difficult to measure in vivo but essential for a quantitative understanding of evolution.
Edit intent: fluency
Original:  Using whole genome deep sequencing data on longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and the distribution of fitness costs in HIV-1 from the temporal dynamics of genetic variation.
Revised:  Using whole genome deep sequencing data from longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and the distribution of fitness costs in HIV-1 from the temporal dynamics of genetic variation.
Edit intent: fluency
Original:  Using whole genome deep sequencing data on longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and the distribution of fitness costs in HIV-1 from the temporal dynamics of genetic variation.
Revised:  Using whole genome deep sequencing data on longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and fitness costs in HIV-1 from the temporal dynamics of genetic variation.
Edit intent: clarity
Original:  About half of all non-synonymous mutations have fitness costs greater than 10 \%, while half of \\%DIF > ), while  synonymous mutations have costs below 1 \% such that they are essentially neutral over the course of a year.
Revised:  About half of all nonsynonymous mutations have large fitness costs ( greater than 10 \%, while half of \\%DIF > ), while synonymous mutations have costs below 1 \% such that they are essentially neutral over the course of a year.
Edit intent: clarity
Original:  About half of all non-synonymous mutations have fitness costs greater than 10 \%, while half of \\%DIF > ), while  synonymous mutations have costs below 1 \% such that they are essentially neutral over the course of a year.
Revised:  About half of all non-synonymous mutations have fitness costs greater than 10 \\%DIF > ), while synonymous mutations have costs below 1 \% such that they are essentially neutral over the course of a year.
Edit intent: fluency
Original:  About half of all non-synonymous mutations have fitness costs greater than 10 \%, while half of \\%DIF > ), while  synonymous mutations have costs below 1 \% such that they are essentially neutral over the course of a year.
Revised:  About half of all non-synonymous mutations have fitness costs greater than 10 \%, while half of \\%DIF > ), while most synonymous mutations have costs below 1 \% such that they are essentially neutral over the course of a year.
Edit intent: clarity
Original:  The proposed model is able to learn high-level discriminative features and double propagate information between aspect and opinion terms simultaneously.
Revised:  The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms simultaneously.
Edit intent: clarity
Original:  Furthermore , it is flexible to incorporate linguistic or lexicon features into the proposed model to further boost its performance in terms of information extraction . Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems  .
Revised:  Furthermore , it is flexible to incorporate linguistic or lexicon features into the proposed model to further boost its information extraction performance . Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems .
Edit intent: clarity
Original:  Furthermore , it is flexible to incorporate linguistic or lexicon features into the proposed model to further boost its performance in terms of information extraction . Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems  .
Revised:  Furthermore , it is flexible to incorporate linguistic or lexicon features into the proposed model to further boost its performance in terms of information extraction . Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge .
Edit intent: clarity
Original:  In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question-answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions.
Revised:  In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions.
Edit intent: fluency
Original:  The produced question-answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics.
Revised:  The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics.
Edit intent: fluency
Original:  Furthermore, when presented to human evaluators, the generated questions appear to be indistinguishable from real human-generated questions.
Revised:  Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.
Edit intent: clarity
Original: The micompr R package implements a procedure for comparing multivariate samples associated with different factor levels or groups.
Revised: The micompr R package implements a procedure for comparing multivariate samples associated with different groups.
Edit intent: clarity
Original:  The procedure is appropriate for multivariate observations with highly correlated and similar scale dimensions, such as time series, images or spectrometric measures .
Revised:  The procedure is appropriate for comparing samples of time series, images or spectrometric measures .
Edit intent: clarity
Original:  The procedure is appropriate for multivariate observations with highly correlated and similar scale dimensions, such as time series, images or spectrometric measures .
Revised:  The procedure is appropriate for multivariate observations with highly correlated and similar scale dimensions, such as time series, images , spectrometric measures or similar multivariate observations .
Edit intent: clarity
Original: The micompr R package implements a procedure for comparing multivariate samples associated with different groups .
Revised: The micompr R package implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution .
Edit intent: clarity
Original:  This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences , avoiding manual selection of specific points or summary statistics .
Revised:  This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences .
Edit intent: clarity
Original: The micompr R package implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution.
Revised: The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution.
Edit intent: fluency
Original:  There is thus a need for theoretical approaches that yield nontrivial quantitative predictions of how spatial community structure shapes information processing in complex microbial ecosystems.
Revised:  There is thus a need for predictive theories of how spatial community structure shapes information processing in complex microbial ecosystems.
Edit intent: clarity
Original: Oil markets influence profoundly world economies through determination of prices of energy and transports.
Revised: Oil markets profoundly influence world economies through determination of prices of energy and transports.
Edit intent: fluency
Original:  Our first key finding is that shocks with shorter than one week response are increasingly important to the transmission mechanism over  studied period.
Revised:  Our first key finding is that shocks with shorter than one week are increasingly important to the transmission mechanism over studied period.
Edit intent: clarity
Original:  Our first key finding is that shocks with shorter than one week response are increasingly important to the transmission mechanism over  studied period.
Revised:  Our first key finding is that shocks with shorter than one week response are increasingly important to the transmission mechanism over the studied period.
Edit intent: fluency