wanyu/IteraTeR_full_sent huggingface dataset:
Original:  with comparison to the traditional trust-region method (the built-in subroutine fsolve.m of the MATLAB environment \mbox{%DIFAUXCMD MATLAB,More1978 ) and the classical homotopy continuation methods (HOMPACK90 \mbox{%DIFAUXCMD \cite{WSMMW1997 }\hspace{0pt}%DIFAUXCMD and the built-in subroutines psolve. m for polynomial systems, GaussNewton.m for non-polynomial systems of the NAClab environment \mbox{%DIFAUXCMD \cite{ZL2013}}\hspace{0pt}%DIFAUXCMD )}  .
Revised:  with comparison to the traditional trust-region method (the built-in subroutine fsolve.m of the MATLAB environment \mbox{%DIFAUXCMD MATLAB,More1978 ) and the classical homotopy continuation methods (HOMPACK90 \mbox{%DIFAUXCMD \cite{WSMMW1997 }\hspace{0pt}%DIFAUXCMD and the built-in subroutines psolve. m for polynomial systems, GaussNewton.m for non-polynomial systems of the NAClab environment \mbox{%DIFAUXCMD \cite{ZL2013}}\hspace{0pt}%DIFAUXCMD )} and NAClab). Furthermore, we analyze the global convergence and the local superlinear convergence of the new method .
Edit intent: coherence
Original:  According to our numerical experiments, it is more robust and faster to find the required solution of the real-world problem than the traditional optimization method (the built-in subroutine fsolve.m of the MATLAB environment) and the homotopy continuation methods(HOMPACK90 and NAClab).
Revised:  According to our numerical experiments, the new method is more robust and faster to find the required solution of the real-world problem than the traditional optimization method (the built-in subroutine fsolve.m of the MATLAB environment) and the homotopy continuation methods(HOMPACK90 and NAClab).
Edit intent: meaning-changed
Original: This paper presents a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and monolingual-multimodal pre-training into a unified framework via multitask learning and weight sharing.
Revised: We present M3P, a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and monolingual-multimodal pre-training into a unified framework via multitask learning and weight sharing.
Edit intent: clarity
Original: This paper presents a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and monolingual-multimodal pre-training into a unified framework via multitask learning and weight sharing.
Revised: This paper presents a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and monolingual-multimodal pre-training into a unified framework via multitask learning and weight sharing.
Edit intent: clarity
Original: This paper presents a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and monolingual-multimodal pre-training into a unified framework via multitask learning and weight sharing.
Revised: This paper presents a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and multimodal pre-training into a unified framework via multitask learning and weight sharing.
Edit intent: fluency
Original: This paper presents a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and monolingual-multimodal pre-training into a unified framework via multitask learning and weight sharing. The model learns universal representations that can map objects that occurred in different modalities or  expressed in different languages to vectors in a common semantic space.
Revised: This paper presents a Multitask Multilingual Multimodal Pre-trained model (M3P) that combines multilingual-monomodal pre-training and monolingual-multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects that occurred in different modalities or expressed in different languages to vectors in a common semantic space.
Edit intent: clarity
Original:  To verify the generalization capability of M3P, we fine-tune the pre-trained model for different types of downstream tasks: multilingual image-text retrieval , multilingual image captioning, multimodal machine translation, multilingual natural language inference and multilingual text generation. Evaluation shows that M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting.
Revised:  In addition, to explicitly encourage fine-grained alignment between images and non-English languages, we also propose Multimodal Code-switched Training (MCT) to combine monolingual pre-training and multimodal pre-training via a code-switch strategy. Experiments are performed on the multilingual image retrieval task across two benchmark datasets, including MSCOCO and Multi30K. M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting.
Edit intent: meaning-changed
Original:  Evaluation shows that M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting.
Revised:  Evaluation shows that M3P can achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting.
Edit intent: fluency
Original:  Evaluation shows that M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting.
Revised:  Evaluation shows that M3P can (i) achieve comparable results for English and new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting.
Edit intent: clarity
Original:  Evaluation shows that M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting.
Revised:  Evaluation shows that M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results for non-English multimodal tasks in the zero-shot or few-shot setting.
Edit intent: fluency
Original:  Evaluation shows that M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English multimodal tasks in the zero-shot or few-shot setting. We also build a new Multilingual Image-Language Dataset (MILD) by collecting large amounts of (text-query, image, context) triplets in 8 languagesfrom the logs of a commercial search engine
Revised:  Evaluation shows that M3P can (i) achieve comparable results on multilingual tasks and English multimodal tasks, compared to the state-of-the-art models pre-trained for these two types of tasks separately, and (ii) obtain new state-of-the-art results on non-English languages.
Edit intent: meaning-changed
Original: Infection by the SARS-CoV-2 virus is mediated by the binding of its spike protein to the membrane-bound angiotensin converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS).
Revised:  SARS-CoV-2 virus is mediated by the binding of its spike protein to the membrane-bound angiotensin converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS).
Edit intent: clarity
Original: Infection by the SARS-CoV-2 virus is mediated by the binding of its spike protein to the membrane-bound angiotensin converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS).
Revised: Infection by the SARS-CoV-2 coronavirus infection is mediated by the binding of its spike protein to the membrane-bound angiotensin converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS).
Edit intent: meaning-changed
Original: Infection by the SARS-CoV-2 virus is mediated by the binding of its spike protein to the membrane-bound angiotensin converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS).
Revised: Infection by the SARS-CoV-2 virus is mediated by the binding of its spike protein to the angiotensin-converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS).
Edit intent: clarity
Original:  The understanding of RAS dysregulation due to this viral infection is of fundamental importance to better understand the pathogenic mechanisms and risk factors of the coronavirus diseaseCOVID-19 , and to design effective therapeutic strategies.
Revised:  The study of RAS dysregulation due to this viral infection is of fundamental importance to better understand the pathogenic mechanisms and risk factors of the coronavirus diseaseCOVID-19 , and to design effective therapeutic strategies.
Edit intent: clarity
Original:  The understanding of RAS dysregulation due to this viral infection is of fundamental importance to better understand the pathogenic mechanisms and risk factors of the coronavirus diseaseCOVID-19 , and to design effective therapeutic strategies.
Revised:  The understanding of RAS dysregulation due to SARS-CoV-2 infection is fundamentally important for a better understanding of the pathogenic mechanisms and risk factors of the coronavirus diseaseCOVID-19 , and to design effective therapeutic strategies.
Edit intent: meaning-changed
Original:  The understanding of RAS dysregulation due to this viral infection is of fundamental importance to better understand the pathogenic mechanisms and risk factors of the coronavirus diseaseCOVID-19 , and to design effective therapeutic strategies.
Revised:  The understanding of RAS dysregulation due to this viral infection is of fundamental importance to better understand the pathogenic mechanisms and risk factors associated with COVID-19 coronavirus disease , and to design effective therapeutic strategies.
Edit intent: clarity
Original:  To address this issue, we built a mathematical model of RAS based on data about protein and peptide concentrations in normotensive and hypertensive individuals. We first tested our model on clinical data on the action of antihypertensive RAS-blocking drugs in control individuals.
Revised:  To address this issue, we built a mathematical model of RAS based on data about protein and peptide concentrations ; the model was tested on clinical data on the action of antihypertensive RAS-blocking drugs in control individuals.
Edit intent: clarity
Original:  We first tested our model on clinical data on the action of antihypertensive RAS-blocking drugs in control individuals. Despite the simplicity of our model, it reproduces very well, without any fitting of additional parameters, the impact of a series of drugs, i.e. angiotensin-converting enzyme inhibitors (ACE-I), direct renin inhibitors (DRI) and angiotensin II receptor blockers (ARB). We applied our model to analyze the impact of SARS-CoV-2 infection on the RASsystem , which we modeled through a downregulation of ACE2 related to viral load.
Revised:  We first tested our model on clinical data from healthy normotensive and hypertensive individuals. We then used our model to analyze the impact of SARS-CoV-2 infection on the RASsystem , which we modeled through a downregulation of ACE2 related to viral load.
Edit intent: clarity
Original:  We applied our model to analyze the impact of SARS-CoV-2 infection on the RASsystem , which we modeled through a downregulation of ACE2 related to viral load.
Revised:  We applied our model to analyze the impact of SARS-CoV-2 infection on the RASsystem , which we modeled through a down-regulation of ACE2 related to viral load.
Edit intent: fluency
Original:  We applied our model to analyze the impact of SARS-CoV-2 infection on the RASsystem , which we modeled through a downregulation of ACE2 related to viral load.
Revised:  We applied our model to analyze the impact of SARS-CoV-2 infection on the RASsystem , which we modeled through a downregulation of ACE2 as a function of viral load.
Edit intent: clarity
Original:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Revised:  We also used it to predict the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Edit intent: coherence
Original:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Revised:  Moreover, we analyzed the effect of RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Edit intent: coherence
Original:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Revised:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, such as RAS-blockers, human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Edit intent: clarity
Original:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Revised:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 , and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Edit intent: clarity
Original:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients .
Revised:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide , on COVID-19 patients .
Edit intent: coherence
Original:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients . We found that while ACE-I, DRI, rhACE2 and Ang1-7 tend to improve the clinical outcomes in a tension-dependent manner, the use of ARB appears to worsen it.
Revised:  Moreover, we analyzed the effect of RAS-blockers and other RAS-targeting drugs, i.e. human recombinant ACE2 (rhACE2) and angiotensin 1-7 peptide (Ang1-7), on the RAS system of normotensive and hypertensive COVID-19 patients ; the model predicted an improvement of the clinical outcome for some drugs and Ang1-7 tend to improve the clinical outcomes in a tension-dependent manner, the use of ARB appears to worsen it.
Edit intent: meaning-changed
Original:  We found that while ACE-I, DRI, rhACE2 and Ang1-7 tend to improve the clinical outcomes in a tension-dependent manner, the use of ARB appears to worsen it. The mathematical model that we developed offers the interesting possibility of testing in silico the RAS dysregulation upon SARS-CoV-2 infection and of predicting how risk factors as well as different drugs , alone or in combination, impact on RAS and disease severity .
Revised:  We found that while ACE-I, DRI, rhACE2 and a worsening for others .
Edit intent: coherence
Original:  The quality of the backward system - which is trained on the available parallel data and used for the back-translation - has been shown in many studies to affect the performance of the final NMT model. In low resource conditions, the available parallel data is usually not enough to train a backward model that can produce the qualitative synthetic data needed to train a standard translation model .
Revised:  The quality of the backward system - which is trained on the available parallel data and used for the back-translation approach to improve the forward (target) translation model. Whereas the success of the approach heavily relies on the additional parallel data generating model -- the backward model -- the aim of the approach is only targeted at improving the forward model. The back-translation approach was designed primarily to benefit from an additional data whose source-side is synthetic. But research works have shown that translation models can also benefit from additional data whose target-side is synthetic .
Edit intent: meaning-changed
Original:  This work proposes a self-training strategy where the output of the backward model is used to improve the model itself through the forward translation technique. The technique was shown to improve baseline low resource IWSLT'14 English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.
Revised:  This work proposes a self-training strategy where the output of the backward model is used to improve the backward model through forward translation and the forward model through back-translation. Experimental results on English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.
Edit intent: clarity
Original:  The technique was shown to improve baseline low resource IWSLT'14 English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.
Revised:  The technique was shown to improve baseline low resource IWSLT'14 English-German and English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.
Edit intent: clarity
Original:  The technique was shown to improve baseline low resource IWSLT'14 English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively. The synthetic data generated by the improved English-German backward model was used to train a forward model which out-performed another forward model trained using standard back-translation by 2.7 BLEU .
Revised:  The technique was shown to improve baseline low resource IWSLT'14 English-German and IWSLT'15 English-Vietnamese low resource neural machine translation showed that the proposed approach outperforms baselines that use the traditional back-translation by 2.7 BLEU .
Edit intent: meaning-changed
Original:  The target-side side monolingual data has been used in the back-translation approach to improve the forward (target) translation model. Whereas the success of the approach heavily relies on the additional parallel data generating model -- the backward model -- the aim of the approach is only targeted at improving the forward model. The back-translation approach was designed primarily to benefit from an additional data whose source-side is synthetic. But research works have shown that translation models can also benefit from additional data whose target-side is synthetic .
Revised:  The target-side side monolingual data has been used in the back-translation - has been shown in many studies to affect the performance of the final NMT model. In low resource conditions, the available parallel data is usually not enough to train a backward model that can produce the qualitative synthetic data needed to train a standard translation model .
Edit intent: coherence
Original:  This work proposes the use of the target-side data throughout the back-translation approach to improve both the backward and forward models. We explored using only the target-side monolingual data to improve the backward model through forward translation and the forward model through back-translation.
Revised:  This work proposes a self-training strategy where the output of the backward model is used to improve the backward model through forward translation and the forward model through back-translation.
Edit intent: clarity
Original:  We explored using only the target-side monolingual data to improve the backward model through forward translation and the forward model through back-translation. Experimental results on English-German and  English-Vietnamese low resource neural machine translation showed that the proposed approach outperforms baselines that use the traditional back-translation method .
Revised:  We explored using only the target-side monolingual data to improve the model itself through the forward translation technique. The technique was shown to improve baseline low resource IWSLT'14 English-German and English-Vietnamese low resource neural machine translation showed that the proposed approach outperforms baselines that use the traditional back-translation method .
Edit intent: meaning-changed
Original:  Experimental results on English-German and  English-Vietnamese low resource neural machine translation showed that the proposed approach outperforms baselines that use the traditional back-translation method .
Revised:  Experimental results on English-German and English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively. The synthetic data generated by the improved English-German backward model was used to train a forward model which out-performed another forward model trained using standard back-translation method .
Edit intent: meaning-changed
Original:  Our results suggest that blocking virus production from infected cells is more effective than reducing the infection rate in terms of decrease in viral load . Vaccination is also found useful but during the vaccine development phase, blocking virus production from infected cells can be targeted for antiviral drug development.
Revised:  Our results suggest that blocking the production of the virus by infected cells decreases the viral load more than reducing the infection rate in terms of decrease in viral load . Vaccination is also found useful but during the vaccine development phase, blocking virus production from infected cells can be targeted for antiviral drug development.
Edit intent: clarity
Original:  Our results suggest that blocking virus production from infected cells is more effective than reducing the infection rate in terms of decrease in viral load . Vaccination is also found useful but during the vaccine development phase, blocking virus production from infected cells can be targeted for antiviral drug development.
Revised:  Our results suggest that blocking virus production from infected cells is more effective than reducing the infection rate of healthy cells . Vaccination is also found useful but during the vaccine development phase, blocking virus production from infected cells can be targeted for antiviral drug development.
Edit intent: clarity