wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As a result , the models may miss essential points conveyed in a document. Moreover, most of the existing methods are either extractive (identify important phrases from the document) or generative (generate phrases word by word), and hence they do not benefit from the advantages of both modeling techniques. To address these challenges , we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document , and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.
Revised:  As a result , the models may miss essential points conveyed in the target document. To overcome this limitation , we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document , and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.
Edit intent: clarity
Original:  To address these challenges , we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document , and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.
Revised:  To address these challenges , we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.
Edit intent: fluency
Original:  SEG-Net uses  a self-attentive architecture, known as ,\emph{Transformer as the building block with a couple of uniqueness. First, SEG-Net incorporates a novel \emph{layer-wise} coverage attention to summarize most of the points discussed in the target document.
Revised:  SEG-Net uses a self-attentive architecture, known as ,\emph{Transformer as the building block with a novel \emph{layer-wise} coverage attention to summarize most of the points discussed in the target document.
Edit intent: coherence
Original:  First, SEG-Net incorporates a novel \emph{layer-wise} coverage attention to summarize most of the points discussed in the target document. Second, it uses an\emph{informed copy attention mechanism to encourage focusing on different segments of the document during keyphrase extraction and generation.
Revised:  First, SEG-Net incorporates a novel \emph{layer-wise} coverage attention to summarize most of the points discussed in the \emph{informed copy attention mechanism to encourage focusing on different segments of the document during keyphrase extraction and generation.
Edit intent: clarity
Original:  We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death  from patients' past (before COVID-19 infection) medical records.
Revised:  We developed MLHO (pronounced as melo) , an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting the patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death from patients' past (before COVID-19 infection) medical records.
Edit intent: meaning-changed
Original:  We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death  from patients' past (before COVID-19 infection) medical records.
Revised:  We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death . It bases this prediction on data from patients' past (before COVID-19 infection) medical records.
Edit intent: coherence
Original:  We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death  from patients' past (before COVID-19 infection) medical records.
Revised:  We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death from patients' past medical records (before their COVID-19 infection) medical records.
Edit intent: clarity
Original:  We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death  from patients' past (before COVID-19 infection) medical records. MLHOis an end-to-end Machine Learning pipeline that implements iterative sequential representation mining and feature and model selection to predict health outcomes. MLHO 's architecture enables a parallel and outcome-oriented  calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested and leveraged to improve prediction of health outcomes.
Revised:  We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death from patients' past (before COVID-19 infection) . MLHO 's architecture enables a parallel and outcome-oriented calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested and leveraged to improve prediction of health outcomes.
Edit intent: coherence
Original:  Using clinical  data from a large cohort of over 14 ,000  patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' before-COVID health records .
Revised:  Using clinical data from a large cohort of over 14 ,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' before-COVID health records .
Edit intent: meaning-changed
Original:  Using clinical  data from a large cohort of over 14 ,000  patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' before-COVID health records . Overall, the best predictions were obtained from extreme and gradient boosting models. The median AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.79 and 0.83 for ICU, hospitalization, and ventilation.
Revised:  Using clinical data from a large cohort of over 14 ,000 patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.79 and 0.83 for ICU, hospitalization, and ventilation.
Edit intent: meaning-changed
Original:  The median AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.79 and 0.83 for ICU, hospitalization, and ventilation.
Revised:  The median AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation.
Edit intent: meaning-changed
Original:  As COVID-19 cases are re-surging in the U. S. and around the world, a Machine Learning pipeline like MLHOis crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future .
Revised:  Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 cases are re-surging in the U. S. and around the world, a Machine Learning pipeline like MLHOis crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future .
Edit intent: meaning-changed
Original:  As COVID-19 cases are re-surging in the U. S. and around the world, a Machine Learning pipeline like MLHOis crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future .
Revised:  As COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, a Machine Learning pipeline like MLHOis crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future .
Edit intent: meaning-changed
Original:  As COVID-19 cases are re-surging in the U. S. and around the world, a Machine Learning pipeline like MLHOis crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future .
Revised:  As COVID-19 cases are re-surging in the U. S. and around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future .
Edit intent: clarity
Original:  As COVID-19 cases are re-surging in the U. S. and around the world, a Machine Learning pipeline like MLHOis crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future .
Revised:  As COVID-19 cases are re-surging in the U. S. and around the world, a Machine Learning pipeline like MLHOis crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge .
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanksto deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [ [ [ . But these models are humongous in size.
Edit intent: fluency
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer 120 based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [ [ [ . But these models are humongous in size.
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) 24 [ [ [ [ [ [ . But these models are humongous in size.
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) , GenerativePre-training Transformer (GPT-2)[ [ [ [ [ [ . But these models are humongous in size.
Edit intent: meaning-changed
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [94 [ [ [ [ [ . But these models are humongous in size.
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ , Multi-task Deep Neural Network (MT-DNN)[ [ [ [ [ . But these models are humongous in size.
Edit intent: meaning-changed
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [73 [ [ [ [ . But these models are humongous in size.
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ , Extra-Long Network (XLNet)[ [ [ [ . But these models are humongous in size.
Edit intent: meaning-changed
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [134 [ [ [ . But these models are humongous in size.
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ , Text-to-text transfer transformer (T5)[ [ [ . But these models are humongous in size.
Edit intent: meaning-changed
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [95 [ [ . But these models are humongous in size.
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [ , T-NLG[ [ . But these models are humongous in size.
Edit intent: meaning-changed
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [ [98 [ . But these models are humongous in size.
Edit intent: clarity
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [ [ and GShard[ . But these models are humongous in size.
Edit intent: meaning-changed
Original: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer  based models like Bidirectional Encoder Representations from Transformers (BERT)  [ [ [ [ [ [ . But these models are humongous in size.
Revised: In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) [ [ [ [ [ [63 . But these models are humongous in size.
Edit intent: meaning-changed
Original:  In this survey, we discuss six different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition, and Linear Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects.
Revised:  In this survey, wediscuss six different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition, and Linear Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects.
Edit intent: fluency