wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: fluency
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer- ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: fluency
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: fluency
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder- ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: fluency
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D LUT, Listen-Understand-Translate , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: meaning-changed
Original:  In addition to the target  sentence translation loss, %DIFDELCMD < \method %%%  includes two auxiliary supervising signals to guide the acoustic transducer that extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text.
Revised:  In addition to the target language sentence translation loss, %DIFDELCMD < \method %%% includes two auxiliary supervising signals to guide the acoustic transducer that extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text.
Edit intent: meaning-changed
Original:  In addition to the target  sentence translation loss, %DIFDELCMD < \method %%%  includes two auxiliary supervising signals to guide the acoustic transducer that extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text.
Revised:  In addition to the target sentence translation loss, %DIFDELCMD < \method %%% LUT includes two auxiliary supervising signals to guide the acoustic transducer that extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text.
Edit intent: meaning-changed
Original:  In addition to the target  sentence translation loss, %DIFDELCMD < \method %%%  includes two auxiliary supervising signals to guide the acoustic transducer that extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text.
Revised:  In addition to the target sentence translation loss, %DIFDELCMD < \method %%% includes two auxiliary supervising signals to guide the acoustic encoder to extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text.
Edit intent: clarity
Original:  To deal with these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment .
Revised:  To address these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment .
Edit intent: clarity
Original:  To deal with these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment .
Revised:  To deal with these problems, we leverage a line flow to encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment .
Edit intent: clarity
Original:  To deal with these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment .
Revised:  To deal with these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line along the temporal dimension, which has been neglected in prior SLAM systems .
Edit intent: meaning-changed
Original:  Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
Revised:  Extensive experimental results demonstrate that the proposed LF-SLAM method achieves state-of-the-art direct and indirect SLAM approaches . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
Edit intent: clarity
Original:  Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
Revised:  Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art results due to the utilization of line flows . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
Edit intent: meaning-changed
Original:  Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
Revised:  Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches . Specifically, LF-SLAM obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
Edit intent: meaning-changed
Original:  Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
Revised:  Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, blurred images , and repetitive textures.
Edit intent: fluency
Original:  Here, we  proposed DTI-SNNFRA, a framework for identifying drug-target interaction (DTI), based on shared nearest neighbor (SNN) and fuzzy-rough approximation (FRA).
Revised:  Here, we have proposed DTI-SNNFRA, a framework for identifying drug-target interaction (DTI), based on shared nearest neighbor (SNN) and fuzzy-rough approximation (FRA).
Edit intent: coherence
Original:  Here, we  proposed DTI-SNNFRA, a framework for identifying drug-target interaction (DTI), based on shared nearest neighbor (SNN) and fuzzy-rough approximation (FRA).
Revised:  Here, we proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbor (SNN) and fuzzy-rough approximation (FRA).
Edit intent: clarity
Original:  Here, we  proposed DTI-SNNFRA, a framework for identifying drug-target interaction (DTI), based on shared nearest neighbor (SNN) and fuzzy-rough approximation (FRA).
Revised:  Here, we proposed DTI-SNNFRA, a framework for identifying drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA).
Edit intent: fluency
Original:  Next, it computes  degree of fuzzy-rough approximations and with proper degree threshold selection for undersampling of the negative samples  from all possible interaction pairs between drugs and targets obtained in the first stage.
Revised:  Next, it computes the degree of fuzzy-rough approximations and with proper degree threshold selection for undersampling of the negative samples from all possible interaction pairs between drugs and targets obtained in the first stage.
Edit intent: fluency
Original:  Next, it computes  degree of fuzzy-rough approximations and with proper degree threshold selection for undersampling of the negative samples  from all possible interaction pairs between drugs and targets obtained in the first stage.
Revised:  Next, it computes degree of fuzzy-rough approximations and with proper degree threshold selection for undersampling of the negative samples ' undersampling from all possible interaction pairs between drugs and targets obtained in the first stage.
Edit intent: fluency
Original: Hierarchical multi-label text classification (HMTC) problems become popular recently because of its practicality. Most existing algorithms for HMTC focus on the design of classifiers, and are largely referred to as local, global, or a combination of local/global approaches .
Revised: Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the design of classifiers, and are largely referred to as local, global, or a combination of local/global approaches .
Edit intent: clarity
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, N eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: fluency
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, a eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: fluency
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN LA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: clarity
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method called is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: coherence
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN \textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: coherence
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{ abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: fluency
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L \textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: fluency
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{ ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: fluency
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: meaning-changed
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels . Besides, local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: meaning-changed
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, hierarchical information is shared across levels while preserving the hierarchical label-based information. Separate local and global document embeddings are separately generated to support the respective local and global classifications.
Edit intent: meaning-changed
Original:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides,  local and global document embeddings are separately generated to support the respective local and global classifications.
Revised:  In this paper, aN eural network-based method calledLA-HCN is proposed where a novel\textbf{L abel-based\textbf{A ttention module is designed to hierarchically extract important information from the text based on different labels . Besides, local and global document embeddings are obtained and used to facilitate the respective local and global classifications.
Edit intent: clarity
Original:  In our experiments, LA-HCN achieves the top performance on the four public HMTC datasets when compared with other neural network-based state-of-the-art algorithms.
Revised:  In our experiments, LA-HCN outperforms other state-of-the-art neural network-based HMTC algorithms on four public HMTC datasets when compared with other neural network-based state-of-the-art algorithms.
Edit intent: clarity
Original:  In our experiments, LA-HCN achieves the top performance on the four public HMTC datasets when compared with other neural network-based state-of-the-art algorithms. The comparison between LA-HCN with its variants also demonstrates the effectiveness of the proposed label-based attention module as well as the use of the combination of local and global  classifications.
Revised:  In our experiments, LA-HCN achieves the top performance on the four public HMTC datasets . The ablation study also demonstrates the effectiveness of the proposed label-based attention module as well as the use of the combination of local and global classifications.
Edit intent: clarity
Original:  The  free energy change in conformation transition of S protein is introduced and we demonstrated that it includes two parts, one from the difference of conformation potential and another from the variation of structural elasticity.
Revised:  The conformational state population is deduced from the free energy change in conformation transition of S protein is introduced and we demonstrated that it includes two parts, one from the difference of conformation potential and another from the variation of structural elasticity.
Edit intent: meaning-changed
Original:  When the amino acid mutation of S protein causes a substantial reduction of elastic energy the equilibrium is biased to the open conformation.
Revised:  The former is related to the change of affinity of RBD to ACE 2 due to the mutation in the subdomain RBM (receptor binding motif) of RBD. The latter is caused by the change of elastic energy of S protein. When the affinity has increased significantly and/or the elastic energy has been reduced substantially the equilibrium is biased to the open conformation.
Edit intent: meaning-changed
Original:  That both the D614G mutation and the K986P mutation increase the COVID-19 infectivity and why a large number of mutations, including those at interface residues, have not been selected in current SARS-CoV-2 pandemic are interpreted from the presented theory.
Revised:  Possible new SARS-CoV-2 variants from amino acid mutations in 5-9 sites on RBD interface are predicted. The elastic energy variation needed for conformation transition is estimated quantitatively. Taking the elastic-structural change into account more virus variants are possible. Both the D614G mutation and the K986P mutation increase the COVID-19 infectivity and why a large number of mutations, including those at interface residues, have not been selected in current SARS-CoV-2 pandemic are interpreted from the presented theory.
Edit intent: meaning-changed