wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We find that YouTube 's recommendation algorithm is more aggressive in suggesting pseudoscientific content when users are searching for specific topics , while these recommendations are less common on a user's homepage or when actively watching pseudoscientific videos.
Revised:  We find that YouTube 's recommendation algorithm is more aggressive in suggesting pseudoscientific content when users are searching for specific topics , while these recommendations are more common on the search results page than on a user's homepage or when actively watching pseudoscientific videos.
Edit intent: clarity
Original:  We find that YouTube 's recommendation algorithm is more aggressive in suggesting pseudoscientific content when users are searching for specific topics , while these recommendations are less common on a user's homepage or when actively watching pseudoscientific videos.
Revised:  We find that YouTube 's recommendation algorithm is more aggressive in suggesting pseudoscientific content when users are searching for specific topics , while these recommendations are less common on a user's homepage or when actively watching videos.
Edit intent: fluency
Original:  A well trained neural network based speech enhancement model is used for pre-processing and a neural network based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system.
Revised:  Our diarisation system consists of a well-trained neural network based speech enhancement model is used for pre-processing and a neural network based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system.
Edit intent: clarity
Original:  A well trained neural network based speech enhancement model is used for pre-processing and a neural network based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system.
Revised:  A well trained neural network based speech enhancement model as pre-processing and a neural network based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system.
Edit intent: clarity
Original:  A well trained neural network based speech enhancement model is used for pre-processing and a neural network based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system.
Revised:  A well trained neural network based speech enhancement model is used for pre-processing front-end of input speech signals. We replace conventional energy-based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system.
Edit intent: meaning-changed
Original:  A well trained neural network based speech enhancement model is used for pre-processing and a neural network based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system. The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering  . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Revised:  A well trained neural network based speech enhancement model is used for pre-processing and a neural network based voice activity detection (VAD) with a neural network based VAD. The neural network based VAD provides more accurate annotation of speech segments containing only background music, noise, and other interference, which is crucial to diarisation performance. We apply agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Edit intent: clarity
Original:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering  . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Revised:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Edit intent: fluency
Original:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering  . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Revised:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov model (VB-HMM) based iterative clustering . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Edit intent: fluency
Original:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering  . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Revised:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering for speaker clustering . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Edit intent: clarity
Original:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering  . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
Revised:  The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering . Experimental results demonstrate that our proposed system achieves substantial improvements over the baseline system, yielding diarisation error rate (DER) of 10.45\%, and Jacard error rate (JER) of 22.46\% on the evaluation set.
Edit intent: meaning-changed
Original:  Finally, we demonstrate our idea on a 7 DOF TIAGo robot in the 3D Gazebo environment, which shows that the robot becomes sense motive, and responds to human action and emotion changes quickly and efficiently.
Revised:  Finally, we demonstrate our idea using a 7 DOF TIAGo robot in the 3D Gazebo environment, which shows that the robot becomes sense motive, and responds to human action and emotion changes quickly and efficiently.
Edit intent: clarity
Original:  Finally, we demonstrate our idea on a 7 DOF TIAGo robot in the 3D Gazebo environment, which shows that the robot becomes sense motive, and responds to human action and emotion changes quickly and efficiently.
Revised:  Finally, we demonstrate our idea on a 7 DOF TIAGo robot in a dynamic HRIC environment, which shows that the robot becomes sense motive, and responds to human action and emotion changes quickly and efficiently.
Edit intent: meaning-changed
Original: Fusing vision, Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS) information is a promising solution for accurate global positioning in complex urban scenes, because of the complementarity of the different sensors. Unlike the loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw GNSS measurements, including pseudoranges and Doppler shift, which is the first of such approaches to our knowledge.
Revised: Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw GNSS measurements, including pseudoranges and Doppler shift, which is the first of such approaches to our knowledge.
Edit intent: coherence
Original:  Unlike the loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw GNSS measurements, including pseudoranges and Doppler shift, which is the first of such approaches to our knowledge.
Revised:  Unlike the loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge.
Edit intent: clarity
Original:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is considered.
Revised:  More specifically, reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is considered.
Edit intent: coherence
Original:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is considered.
Revised:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly minimized within a sliding window, and the asynchronism between images and raw GNSS measurements is considered.
Edit intent: clarity
Original:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is considered.
Revised:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, in which the asynchronism between images and raw GNSS measurements is considered.
Edit intent: coherence
Original:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is considered. Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Revised:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is accounted for. In addition, issues such as marginalization, noisy measurements removal, as well as tackling vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Edit intent: clarity
Original:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Revised:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are also addressed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Edit intent: clarity
Original:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Revised:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes show that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Edit intent: coherence
Original:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Revised:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, including scenes mainly containing low-rise buildings and the scenes that contain urban canyons.
Edit intent: coherence
Original:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
Revised:  Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and those containing canyons.
Edit intent: clarity
Original:  While the research community has coalesced around various benchmarks, we set out to understand generalization performance in acoustic modeling across datasets -- in particular, if models trained on a single dataset transfer to other (possibly out-of-domain) datasets.
Revised:  While the research community has coalesced around various benchmarks, we set out to understand generalization performance in acoustic modeling across datasets - in particular, if models trained on a single dataset transfer to other (possibly out-of-domain) datasets.
Edit intent: fluency
Original:   Further, we demonstrate that when a large enough set of benchmarks is used, average word error rate (WER) performance over them provides a good proxy for performance on real-world  data.
Revised:  We show that, in general, reverberative and additive noise augmentation improves generalization performance across domains. Further, we demonstrate that when a large enough set of benchmarks is used, average word error rate (WER) performance over them provides a good proxy for performance on real-world data.
Edit intent: meaning-changed
Original:  We propose SapBERT, a pre-training scheme based on BERT. It self-aligns the representation space of biomedical entities with a metric learning objective function leveraging UMLS, a  collection of biomedical ontologies with > 4M concepts.
Revised:  We propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities with a metric learning objective function leveraging UMLS, a collection of biomedical ontologies with > 4M concepts.
Edit intent: clarity
Original:  It self-aligns the representation space of biomedical entities with a metric learning objective function leveraging UMLS, a  collection of biomedical ontologies with > 4M concepts. Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Revised:  It self-aligns the representation space of biomedical entities with a metric learning objective function leveraging UMLS, a collection of biomedical ontologies with > 4M + concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Edit intent: meaning-changed
Original:  Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Revised:  Our experimental results on six medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Edit intent: meaning-changed
Original:  Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Revised:  Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific pretrained MLMs such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Edit intent: clarity
Original:  Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Revised:  Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, SciBERTand and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Edit intent: meaning-changed
Original:  Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Revised:  Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, our pretraining scheme proves to be both effective and robust .
Edit intent: coherence
Original:   As technological and social transformation is reshuffling jobs' task profiles at a fast pace, the findings of this study help to clarify skill sets required for mastering new technologies and designing individual training pathways.
Revised:  Likewise, the data illustrate how to reveal valuable skills required for new and opaque technology domains, such as Artificial Intelligence. As technological and social transformation is reshuffling jobs' task profiles at a fast pace, the findings of this study help to clarify skill sets required for mastering new technologies and designing individual training pathways.
Edit intent: meaning-changed
Original:  The results indicate that the added economic value of learning  a new skill strongly depends on the already existing skill bundle but that acquiring a skill from a different domain is often beneficial.
Revised:  The results indicate that the added economic value of learning in-demand skills, such as popular programming languages, is beneficial in general, and that diverse skill sets tend to be profitable, too. However, the economic benefit of a new skill strongly depends on the already existing skill bundle but that acquiring a skill from a different domain is often beneficial.
Edit intent: meaning-changed
Original:  Generalizing a recent beautiful work of Malach and Shalev-Shwartz \mbox{%DIFAUXCMD malach2020hardness who gave the first tight correlational SQ (CSQ) lower bounds for learning DNF formulas, we show that lower bounds on the threshold or approximate degree of any function class directly imply CSQ lower bounds for PAC or agnostic learning respectively.
Revised:  Generalizing a recent beautiful work of Malach and Shalev-Shwartz (2020) who gave the first tight correlational SQ (CSQ) lower bounds for learning DNF formulas, we show that lower bounds on the threshold or approximate degree of any function class directly imply CSQ lower bounds for PAC or agnostic learning respectively.
Edit intent: clarity