wanyu/IteraTeR_full_sent huggingface dataset:
Original: The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigms to address this kind of tasksat present . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Revised: The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigms to address this kind of tasksat present . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Edit intent: clarity
Original: The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigms to address this kind of tasksat present . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Revised: The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enabled them to be ranked as one of the best paradigms to address this kind of tasksat present . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Edit intent: fluency
Original: The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigms to address this kind of tasksat present . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Revised: The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigm to address Natural Language Processing (NLP) tasks . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Edit intent: clarity
Original: The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigms to address this kind of tasksat present . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
Revised: The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigms to address this kind of tasksat present . NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established relationships between a hypothesis and a premise.
Edit intent: fluency
Original:  In this paper, we propose a new architecture , siamese multilingual transformer (SML ), to efficiently align multilingual embeddings for Natural Language Inference . SML leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend each other to later be combined through a matrix alignment method.
Revised:  In this paper, we propose a new architecture called Siamese Inter-Lingual Transformer (SILT ), to efficiently align multilingual embeddings for Natural Language Inference . SML leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend each other to later be combined through a matrix alignment method.
Edit intent: meaning-changed
Original:  In this paper, we propose a new architecture , siamese multilingual transformer (SML ), to efficiently align multilingual embeddings for Natural Language Inference . SML leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend each other to later be combined through a matrix alignment method.
Revised:  In this paper, we propose a new architecture , siamese multilingual transformer (SML ), to efficiently align multilingual embeddings for Natural Language Inference , allowing for unmatched language pairs to be processed. SILT leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend each other to later be combined through a matrix alignment method.
Edit intent: meaning-changed
Original:  Our method is thoroughly validated on two large surgical video datasets, i.e., Cholec80 and M2CAI16 Challenge datasets, and significantly outperforms the state-of-the-art approaches at a processing speed of 91 fps.
Revised:  Our method is thoroughly validated on two large surgical video datasets, i.e., Cholec80 and M2CAI16 Challenge datasets, and outperforms the state-of-the-art approaches at a processing speed of 91 fps.
Edit intent: clarity
Original:  In this paper, we create a dataset for classifying constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments.
Revised:  In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments.
Edit intent: clarity
Original: Summarizing data via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making.
Revised: In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making.
Edit intent: meaning-changed
Original:  In this work , we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Revised:  Here , we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Edit intent: clarity
Original:  We show the code is capable of extracting fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Revised:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Edit intent: clarity
Original:  Here, we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Revised:  Here, we redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Edit intent: clarity
Original:  Here, we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Revised:  Here, we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Edit intent: fluency
Original:  Here, we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Revised:  Here, we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate use on local machines and remote clusters, and by beginners and advanced users alike.
Edit intent: clarity
Original:  Here, we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike.
Revised:  Here, we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, by beginners and advanced users alike.
Edit intent: coherence
Original:  We demonstrate the performance of this code matches our previous MATLAB implementation but at a fraction of the computational cost. We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Revised:  We demonstrate that its performance is on par with previous MATLAB implementations but runs an order of magnitude faster. We tested it against challenge data and demonstrate its performance is comparable to state-of-the-art analysis platforms. We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Edit intent: meaning-changed
Original:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Revised:  We show the code can extract fluorescence intensity values for single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Edit intent: clarity
Original:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Revised:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Edit intent: fluency
Original:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Revised:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and cellular copy numbers of fluorescently labeled biomolecules.
Edit intent: fluency
Original:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules.
Revised:  We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently-labeled biomolecules.
Edit intent: fluency
Original:  We introduce the subordinated Cox-Ross-Rubinstein (CRR) model and prove that it converges in distribution to the subordinated B-S model defined in \mbox{%DIFAUXCMD gajda .
Revised:  We introduce the subordinated Cox-Ross-Rubinstein model and prove that it converges in distribution to the subordinated B-S model defined in \mbox{%DIFAUXCMD gajda .
Edit intent: clarity
Original:  We introduce the subordinated Cox-Ross-Rubinstein (CRR) model and prove that it converges in distribution to the subordinated B-S model defined in \mbox{%DIFAUXCMD gajda .
Revised:  We introduce the subordinated Cox-Ross-Rubinstein (CRR) model and prove that the price of the underlying in that model converges in distribution to the subordinated B-S model defined in \mbox{%DIFAUXCMD gajda .
Edit intent: meaning-changed
Original:  We introduce the subordinated Cox-Ross-Rubinstein (CRR) model and prove that it converges in distribution to the subordinated B-S model defined in \mbox{%DIFAUXCMD gajda .
Revised:  We introduce the subordinated Cox-Ross-Rubinstein (CRR) model and prove that it converges in distribution to the price of underlying in the subordinated Black-Scholes model defined in \mbox{%DIFAUXCMD gajda .
Edit intent: meaning-changed
Original:  We introduce the subordinated Cox-Ross-Rubinstein (CRR) model and prove that it converges in distribution to the subordinated B-S model defined in \mbox{%DIFAUXCMD gajda .
Revised:  We introduce the subordinated Cox-Ross-Rubinstein (CRR) model and prove that it converges in distribution to the subordinated B-S model defined in 25 .
Edit intent: meaning-changed
Original: This paper studies optimal targeting as a means to increase fundraising efficacy. We randomly provide potential donors with an unconditional gift and use causal-machine learning techniques to "optimally " target this fundraising tool to the predicted net donors: individuals who, in expectation, give more than their solicitation costs.
Revised: Ineffective fundraising lowers the resources charities can use for goods provision. We combine a field experiment and a causal machine-learning approach to increase a charity's fundraising effectiveness. The approach optimally targets fundraising to individuals whose expected donations exceed solicitation costs.
Edit intent: meaning-changed
Original: Successful methods for unsupervised neural machine translation (UNMT) employ cross-lingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires the model to align the lexical- and high-level representations of the two languages.
Revised: Successful methods for unsupervised neural machine translation (UNMT) employ crosslingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires the model to align the lexical- and high-level representations of the two languages.
Edit intent: fluency
Original: In this study we demonstrate the viability of deploying BERT-style models to AWS Lambda in a production environment . Since the freely available pre-trained models are too large to be deployed in this way, we utilize knowledge distillation and fine-tune the models on proprietary datasets for two real-world tasks: sentiment analysis and semantic textual similarity.
Revised: In this study we demonstrate the viability of deploying BERT-style models to serverless environments in a production environment . Since the freely available pre-trained models are too large to be deployed in this way, we utilize knowledge distillation and fine-tune the models on proprietary datasets for two real-world tasks: sentiment analysis and semantic textual similarity.
Edit intent: meaning-changed
Original: In this study we demonstrate the viability of deploying BERT-style models to AWS Lambda in a production environment . Since the freely available pre-trained models are too large to be deployed in this way, we utilize knowledge distillation and fine-tune the models on proprietary datasets for two real-world tasks: sentiment analysis and semantic textual similarity.
Revised: In this study we demonstrate the viability of deploying BERT-style models to AWS Lambda in a production setting . Since the freely available pre-trained models are too large to be deployed in this way, we utilize knowledge distillation and fine-tune the models on proprietary datasets for two real-world tasks: sentiment analysis and semantic textual similarity.
Edit intent: clarity
Original:  The subsequent performance analysis shows that this solution does not only report latency levels acceptable for production use but that it is also a cost-effective alternative to small-to-medium size deployments of BERT models, all without any infrastructure overhead.
Revised:  The subsequent performance analysis shows that this solution results in latency levels acceptable for production use but that it is also a cost-effective alternative to small-to-medium size deployments of BERT models, all without any infrastructure overhead.
Edit intent: clarity
Original:  The subsequent performance analysis shows that this solution does not only report latency levels acceptable for production use but that it is also a cost-effective alternative to small-to-medium size deployments of BERT models, all without any infrastructure overhead.
Revised:  The subsequent performance analysis shows that this solution does not only report latency levels acceptable for production use and that it is also a cost-effective alternative to small-to-medium size deployments of BERT models, all without any infrastructure overhead.
Edit intent: coherence
Original:  The subsequent performance analysis shows that this solution does not only report latency levels acceptable for production use but that it is also a cost-effective alternative to small-to-medium size deployments of BERT models, all without any infrastructure overhead.
Revised:  The subsequent performance analysis shows that this solution does not only report latency levels acceptable for production use but that it is also a cost-effective approach for small-to-medium size deployments of BERT models, all without any infrastructure overhead.
Edit intent: clarity
Original:  We also demonstrate that the imitative expert policy with deep ensemble-based uncertainty estimation can lead to better performance, especially in a more difficult task . As a consequence, the proposed method has shown its great potential to facilitate the applications of DRL-enabled human-like autonomous driving in practice .
Revised:  We also demonstrate that the imitative expert policy with deep ensemble-based uncertainty estimation can lead to better performance, especially in a more difficult task .
Edit intent: coherence
Original: Structural inequalities persist in society, conferring systematic advantages to one group of people , for example, by giving them substantially more influence and opportunities than others . Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of citation inequalities .
Revised: Structural inequalities persist in society, conferring systematic advantages to some people at the expense of others , for example, by giving them substantially more influence and opportunities than others . Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of citation inequalities .
Edit intent: clarity
Original: Structural inequalities persist in society, conferring systematic advantages to one group of people , for example, by giving them substantially more influence and opportunities than others . Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of citation inequalities .
Revised: Structural inequalities persist in society, conferring systematic advantages to one group of people , for example, by giving them substantially more influence and opportunities . Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of citation inequalities .
Edit intent: clarity
Original: Structural inequalities persist in society, conferring systematic advantages to one group of people , for example, by giving them substantially more influence and opportunities than others . Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of citation inequalities .
Revised: Structural inequalities persist in society, conferring systematic advantages to one group of people , for example, by giving them substantially more influence and opportunities than others . Using bibliometric data about authors of scientific publications , we identify two types of citation inequalities .
Edit intent: clarity
Original: Structural inequalities persist in society, conferring systematic advantages to one group of people , for example, by giving them substantially more influence and opportunities than others . Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of citation inequalities .
Revised: Structural inequalities persist in society, conferring systematic advantages to one group of people , for example, by giving them substantially more influence and opportunities than others . Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of structural inequalities in scientific citations .
Edit intent: clarity
Original:  We then present a dynamic model of the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors in the same group (homophily) or the other group (heterophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Revised:  We present a model for the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors in the same group (homophily) or the other group (heterophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Edit intent: clarity