wanyu/IteraTeR_full_sent huggingface dataset:
Original:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Revised:  then the maximum number of stable positive steady states is N /2. (2) If the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Edit intent: meaning-changed
Original:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Revised:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is an odd number N, then we provide a condition on the network such that the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Edit intent: meaning-changed
Original:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Revised:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is (N-1)/2 if this condition is satisfied, and this maximum number is (N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Edit intent: meaning-changed
Original:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Revised:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Edit intent: coherence
Original:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 nondegenerate positive steady states, we prove a sufficient and necessary condition for these parameters to yield N+1 stable positive steady states .
Revised:  then the maximum number of stable positive steady states is N , and if the maximum number of positive steady states is 2N+1, then the maximum number of stable positive steady states is N or N + 1. (3)If the maximum number of positive steady states is 2N+1 (N>0), and if a choice of parameters yields 2N+ 1 )/2 otherwise .
Edit intent: meaning-changed
Original:  We evaluate our model on two Arabic reading comprehension tasks, and we show that AraELECTRA outperforms current state-of-the-art Arabic language representation models  given the same pretraining data and with even a smaller model size.
Revised:  We evaluate our model on multiple Arabic NLP tasks, including reading comprehension, sentiment analysis, and named-entity recognition and we show that AraELECTRA outperforms current state-of-the-art Arabic language representation models given the same pretraining data and with even a smaller model size.
Edit intent: meaning-changed
Original:  We evaluate our model on two Arabic reading comprehension tasks, and we show that AraELECTRA outperforms current state-of-the-art Arabic language representation models  given the same pretraining data and with even a smaller model size.
Revised:  We evaluate our model on two Arabic reading comprehension tasks, and we show that AraELECTRA outperforms current state-of-the-art Arabic language representation models , given the same pretraining data and with even a smaller model size.
Edit intent: fluency
Original:  We evaluate different size variants of AraGPT2 using the perplexity measure, where AraGPT2-mega achieves a perplexity of 29.8 on held-out articles from Wikipedia .
Revised:  The Mega model was evaluated and showed success on different tasks including synthetic news generation, and zero-shot question answering. For text generation, our best model achieves a perplexity of 29.8 on held-out articles from Wikipedia .
Edit intent: meaning-changed
Original:  We evaluate different size variants of AraGPT2 using the perplexity measure, where AraGPT2-mega achieves a perplexity of 29.8 on held-out articles from Wikipedia . Pretrained variants of AraGPT2 (base, medium, large, mega) are publicly available on URL hoping to encourage new research directions and applications for Arabic NLP.
Revised:  We evaluate different size variants of AraGPT2 using the perplexity measure, where AraGPT2-mega achieves a perplexity of 29.8 on held-out Wikipedia articles. A study conducted with human evaluators showed the significant success of AraGPT2-mega in generating news articles that are difficult to distinguish from articles written by humans. We thus develop and release an automatic discriminator model with a 98\% percent accuracy in detecting model-generated text. The models are also publicly available, hoping to encourage new research directions and applications for Arabic NLP.
Edit intent: meaning-changed
Original:  AR and NAR generation can be uniformly regarded as what extend of previous tokens can be attended to , and BANG bridges AR and NAR generation through designing a novel model structure for large-scale pre-training.
Revised:  AR and NAR generation can be uniformly regarded as to what extent previous tokens can be attended to , and BANG bridges AR and NAR generation through designing a novel model structure for large-scale pre-training.
Edit intent: clarity
Original:  AR and NAR generation can be uniformly regarded as what extend of previous tokens can be attended to , and BANG bridges AR and NAR generation through designing a novel model structure for large-scale pre-training.
Revised:  AR and NAR generation can be uniformly regarded as what extend of previous tokens can be attended , and BANG bridges AR and NAR generation through designing a novel model structure for large-scale pre-training.
Edit intent: fluency
Original:  AR and NAR generation can be uniformly regarded as what extend of previous tokens can be attended to , and BANG bridges AR and NAR generation through designing a novel model structure for large-scale pre-training.
Revised:  AR and NAR generation can be uniformly regarded as what extend of previous tokens can be attended to , and BANG bridges AR and NAR generation by designing a novel model structure for large-scale pre-training.
Edit intent: fluency
Original:  AR and NAR generation can be uniformly regarded as what extend of previous tokens can be attended to , and BANG bridges AR and NAR generation through designing a novel model structure for large-scale pre-training. A pretrained BANG model can simultaneously support AR, NAR , and semi-NAR generation to meet different requirements.
Revised:  AR and NAR generation can be uniformly regarded as what extend of previous tokens can be attended to , and BANG bridges AR and NAR generation through designing a novel model structure for large-scale pretraining. The pretrained BANG model can simultaneously support AR, NAR , and semi-NAR generation to meet different requirements.
Edit intent: clarity
Original:  Experiments on question generation (SQuAD 1.1), summarization (XSum) , and dialogue (PersonaChat) show that BANG improves NAR and semi-NAR performance significantly as well as attaining comparable performance with strong AR pretrained models.
Revised:  Experiments on question generation (SQuAD 1.1), summarization (XSum) and dialogue generation (PersonaChat) show that BANG improves NAR and semi-NAR performance significantly as well as attaining comparable performance with strong AR pretrained models.
Edit intent: clarity
Original:  Compared with the semi-NAR strong baselines, BANG achieves absolute improvements of 14.01 and 5.24 in  overall scores of SQuAD  and XSum, respectively.
Revised:  Compared with the semi-NAR strong baselines, BANG achieves absolute improvements of 14.01 and 5.24 in the overall scores of SQuAD and XSum, respectively.
Edit intent: fluency
Original:  Compared with the semi-NAR strong baselines, BANG achieves absolute improvements of 14.01 and 5.24 in  overall scores of SQuAD  and XSum, respectively.
Revised:  Compared with the semi-NAR strong baselines, BANG achieves absolute improvements of 14.01 and 5.24 in overall scores of SQuAD 1.1 and XSum, respectively.
Edit intent: meaning-changed
Original:  In addition, BANG achieves absolute improvements of 10.73, 6.39 , and 5.90 in  overall scores of SQuAD, XSUM , and PersonaChat compared with the NAR strong baselines, respectively .
Revised:  In addition, BANG achieves absolute improvements of 10.73, 6.39 and 5.90 in overall scores of SQuAD, XSUM , and PersonaChat compared with the NAR strong baselines, respectively .
Edit intent: fluency
Original:  In addition, BANG achieves absolute improvements of 10.73, 6.39 , and 5.90 in  overall scores of SQuAD, XSUM , and PersonaChat compared with the NAR strong baselines, respectively .
Revised:  In addition, BANG achieves absolute improvements of 10.73, 6.39 , and 5.90 in the overall scores of SQuAD, XSUM , and PersonaChat compared with the NAR strong baselines, respectively .
Edit intent: fluency
Original:  In addition, BANG achieves absolute improvements of 10.73, 6.39 , and 5.90 in  overall scores of SQuAD, XSUM , and PersonaChat compared with the NAR strong baselines, respectively .
Revised:  In addition, BANG achieves absolute improvements of 10.73, 6.39 , and 5.90 in overall scores of SQuAD, XSUM and PersonaChat respectively compared with the NAR strong baselines, respectively .
Edit intent: fluency
Original:  In addition, BANG achieves absolute improvements of 10.73, 6.39 , and 5.90 in  overall scores of SQuAD, XSUM , and PersonaChat compared with the NAR strong baselines, respectively .
Revised:  In addition, BANG achieves absolute improvements of 10.73, 6.39 , and 5.90 in overall scores of SQuAD, XSUM , and PersonaChat compared with the strong NAR baselines .
Edit intent: clarity
Original:  Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT's and target language vocabulary.
Revised:  Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT's and target language vocabulary.
Edit intent: fluency
Original:  We propose a methodology for creating reading comprehension datasets that better reflect the challenges of coreference reasoning and use it to  show that state-of-the-art models still struggle with these phenomena.
Revised:  We propose a methodology for creating reading comprehension datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set. The results on our dataset show that state-of-the-art models still struggle with these phenomena.
Edit intent: meaning-changed
Original:  After extending the stabilization procedure of  [ 17 ] to incompressible flow problems, we theoretically prove that, combined with the Raviart-Thomas isogeometric element , we are able to recover the well-posedness of the formulation and, consequently, optimal a priori error estimates.
Revised:  After extending the stabilization procedure of ~ [ 17 ] to incompressible flow problems, we theoretically prove that, combined with the Raviart-Thomas isogeometric element , we are able to recover the well-posedness of the formulation and, consequently, optimal a priori error estimates.
Edit intent: meaning-changed
Original:  After extending the stabilization procedure of  [ 17 ] to incompressible flow problems, we theoretically prove that, combined with the Raviart-Thomas isogeometric element , we are able to recover the well-posedness of the formulation and, consequently, optimal a priori error estimates.
Revised:  After extending the stabilization procedure of [ 16 ] to incompressible flow problems, we theoretically prove that, combined with the Raviart-Thomas isogeometric element , we are able to recover the well-posedness of the formulation and, consequently, optimal a priori error estimates.
Edit intent: meaning-changed
Original:  After extending the stabilization procedure of  [ 17 ] to incompressible flow problems, we theoretically prove that, combined with the Raviart-Thomas isogeometric element , we are able to recover the well-posedness of the formulation and, consequently, optimal a priori error estimates.
Revised:  After extending the stabilization procedure of [ 17 ] to incompressible flow problems, we theoretically prove that, combined with the Raviart-Thomas , the N\'ed\'elec, and the Taylor-Hood (in this case, only when the isogeometric map is the identity) isogeometric elements , we are able to recover the well-posedness of the formulation and, consequently, optimal a priori error estimates.
Edit intent: meaning-changed
Original: We present a focused study of few-shot crosslingual transfer , a recently proposed NLP scenario: a pretrained multilingual encoder is first finetuned on many annotations in a high resource language (typically English), and then finetuned on a few annotations (the ``few shots'') in a target language. Few-shot transfer brings large improvements over zero-shot transfer.
Revised: Few-shot crosslingual transfer has been shown to outperform its zero-shot transfer.
Edit intent: clarity
Original:  Few-shot transfer brings large improvements over zero-shot transfer. However, we show that it inherently has large variance and it is necessary to report results on multiple sets of few shots for stable results and to guarantee fair comparison of different algorithms.
Revised:  Few-shot transfer brings large improvements over zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for stable results and to guarantee fair comparison of different algorithms.
Edit intent: meaning-changed
Original:  However, we show that it inherently has large variance and it is necessary to report results on multiple sets of few shots for stable results and to guarantee fair comparison of different algorithms. To address this problem, we publish our few-shot sets.
Revised:  However, we show that it inherently has large variance and it is necessary to report results on multiple sets of few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot sets.
Edit intent: meaning-changed
Original:  To address this problem, we publish our few-shot sets. In a study of why few-shot learning outperforms zero-shot transfer, we show that large models heavily rely on lexical hints when finetuned on a few shots and then overfit quickly.
Revised:  To address this problem, we publish our few-shot transfer, which highlights the role of lexical features. Additionally, we show that large models heavily rely on lexical hints when finetuned on a few shots and then overfit quickly.
Edit intent: meaning-changed
Original:  In a study of why few-shot learning outperforms zero-shot transfer, we show that large models heavily rely on lexical hints when finetuned on a few shots and then overfit quickly. We evaluate different methods that use few-shot annotations, but do not observe significant improvements over the baseline.
Revised:  In a study of why few-shot learning outperforms zero-shot transfer, we show that a straightforward full model finetuning approach is quite effective for few-shot annotations, but do not observe significant improvements over the baseline.
Edit intent: meaning-changed
Original: We present a first-of-its-kind large synthetic training dataset for online hate classification,created from scratch with trained annotators over multiple rounds of dynamic data collection.
Revised: We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset of ~40,000 entries, generated and labelled by trained annotators over multiple rounds of dynamic data collection.
Edit intent: meaning-changed
Original: We present a first-of-its-kind large synthetic training dataset for online hate classification,created from scratch with trained annotators over multiple rounds of dynamic data collection. We provide a 40,623 example dataset with annotations for fine-grained labels , including a large number of challenging contrastive perturbation examples.
Revised: We present a first-of-its-kind large synthetic training dataset for online hate classification,created from scratch with trained annotators over multiple rounds of dynamic data creation. It includes ~15,000 challenging perturbations and each hateful entry has fine-grained labels , including a large number of challenging contrastive perturbation examples.
Edit intent: meaning-changed
Original:  We provide a 40,623 example dataset with annotations for fine-grained labels , including a large number of challenging contrastive perturbation examples. Unusually for an abusive content dataset, it comprises 54\% hateful and 46\% not hateful entries .
Revised:  We provide a 40,623 example dataset with annotations for fine-grained labels for the type and target of hate. Hateful entries make up 54\% hateful and 46\% not hateful entries .
Edit intent: clarity
Original:  We show that model performance and robustness can be greatly improved using the dynamic data collection paradigm. The model error rate decreased across rounds , from 72.1\% in the first round to 35.8\% in the last round, showing that models became increasingly harder to trick -- even though content become progressively more adversarial as annotators became more experienced. Hate speech detection is an important and subtle problem that is still very challenging for existing AI methods. We hope that the models , dataset and dynamic system that we present here will help improve current approaches, having a positive social impact.
Revised:  We show that model performance is substantially improved using this approach. Models trained on later rounds of data collection perform better on test sets and are harder for annotators to trick. They also perform better on HateCheck, a suite of functional tests for online hate detection. We provide the code , dataset and dynamic system that we present here will help improve current approaches, having a positive social impact.
Edit intent: clarity
Original:  First, we show that initially training the model on short subsequences , before moving on to longer ones , both reduces overall training time and, surprisingly, gives a large improvement in perplexity. We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Revised:  First, we show that initially training the model on short subsequences , before moving on to longer ones , both reduces overall training time and, surprisingly, substantially improves perplexity. Second, we show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Edit intent: clarity
Original:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Revised:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens when generating sequences that are larger than the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Edit intent: fluency
Original:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Revised:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that exceed the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Edit intent: clarity
Original:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length that the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Revised:  We then show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens ( when generating sequences that are larger than the maximal length the transformer can handle at once ) . Existing methods require computationally expensive relative position embeddings;
Edit intent: fluency