wanyu/IteraTeR_v2 huggingface dataset:
Original:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN)[1] and a recurrent neural network (RNN) [2] to recognizecultural /anthropological Italian sign language gestures fromvideos .
Revised:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN)[1] and a recurrent neural network (RNN) [2] to recognize cultural /anthropological Italian sign language gestures fromvideos .
Edit intent: fluency
Original:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN)[1] and a recurrent neural network (RNN) [2] to recognizecultural /anthropological Italian sign language gestures fromvideos .
Revised:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN)[1] and a recurrent neural network (RNN) [2] to recognizecultural /anthropological Italian sign language gestures from videos .
Edit intent: fluency
Original:  The CNN extracts important features that later areused by the RNN. With RNNs we are able to store temporalinformation inside the model to provide contextual informationfrom previous frames to enhance the prediction accuracy.
Revised:  The CNN extracts important features that later areused by the RNN. With RNNs we are able to store temporal information inside the model to provide contextual informationfrom previous frames to enhance the prediction accuracy.
Edit intent: clarity
Original:  The CNN extracts important features that later areused by the RNN. With RNNs we are able to store temporalinformation inside the model to provide contextual informationfrom previous frames to enhance the prediction accuracy.
Revised:  The CNN extracts important features that later areused by the RNN. With RNNs we are able to store temporalinformation inside the model to provide contextual information from previous frames to enhance the prediction accuracy.
Edit intent: fluency
Original:  Ournovel approach uses different data augmentation techniquesand regularization methods from only RGB frames to avoidoverfitting and provide a small generalization error.
Revised:  Our novel approach uses different data augmentation techniquesand regularization methods from only RGB frames to avoidoverfitting and provide a small generalization error.
Edit intent: fluency
Original:  Ournovel approach uses different data augmentation techniquesand regularization methods from only RGB frames to avoidoverfitting and provide a small generalization error.
Revised:  Ournovel approach uses different data augmentation techniquesand regularization methods from only RGB frames to avoid overfitting and provide a small generalization error.
Edit intent: fluency
Original:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN) 1 and a recurrent neural network (RNN) 2 to recognize cultural/anthropological Italian sign language gestures from videos.
Revised:  The aim of this paper is to build a symbiosis between a convolutional neural network (CNN) 1 and a recurrent neural network (RNN) 2 to recognize cultural/anthropological Italian sign language gestures from videos.
Edit intent: clarity
Original:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN) 1 and a recurrent neural network (RNN) 2 to recognize cultural/anthropological Italian sign language gestures from videos.
Revised:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN) and a recurrent neural network (RNN) 2 to recognize cultural/anthropological Italian sign language gestures from videos.
Edit intent: fluency
Original:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN) 1 and a recurrent neural network (RNN) 2 to recognize cultural/anthropological Italian sign language gestures from videos.
Revised:  The aim of this project is to builda symbiosis between a convolutional neural network (CNN) 1 and a recurrent neural network (RNN) to recognize cultural/anthropological Italian sign language gestures from videos.
Edit intent: coherence
Original:  The CNN extracts important features that later areused by the RNN. With RNNs we are able to store temporal information inside the model to provide contextual information from previous frames to enhance the prediction accuracy.
Revised:  The CNN extracts important features that later are used by the RNN. With RNNs we are able to store temporal information inside the model to provide contextual information from previous frames to enhance the prediction accuracy.
Edit intent: fluency
Original:  Our novel approach uses different data augmentation techniquesand regularization methods from only RGB frames to avoid overfitting and provide a small generalization error.
Revised:  Our novel approach uses different data augmentation techniques and regularization methods from only RGB frames to avoid overfitting and provide a small generalization error.
Edit intent: fluency
Original:  In this study , we extract temporal features with continuous values from the hidden states of neural networks predicting customers' spending personality from their financial transactions.
Revised:  In a novel approach , we extract temporal features with continuous values from the hidden states of neural networks predicting customers' spending personality from their financial transactions.
Edit intent: clarity
Original:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Revised:  Meanwhile, weakly-supervised instance segmentation (WSIS) has been considered as a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Edit intent: clarity
Original:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Revised:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels. To tackle the problem , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Edit intent: clarity
Original:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Revised:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches use off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Edit intent: style
Original:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Revised:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal techniques that require pre-training with high-level labels, deviating a fully image-level supervised setting.
Edit intent: fluency
Original:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with high-level labels, deviating a fully image-level supervised setting.
Revised:  Meanwhile, weakly-supervised instance segmentation (WSIS) is a more challenging task because instance-wise localization using only image-level labels is quite difficult . Consequently , most WSIS approaches exploit off-the-shelf proposal technique that requires pre-training with instance or object level labels, deviating a fully image-level supervised setting.
Edit intent: clarity
Original:  To this end , we propose a novel approach that consists of two innovative components.
Revised:  To this end , we propose a novel approach including two innovative components.
Edit intent: clarity
Original:  First, we design a semantic knowledge transfer to obtain pseudo instance labels by transferring the knowledge of WSSS to WSIS while eliminating the need for  off-the-shelf proposals.
Revised:  First, we propose a semantic knowledge transfer to obtain pseudo instance labels by transferring the knowledge of WSSS to WSIS while eliminating the need for off-the-shelf proposals.
Edit intent: clarity
Original:  First, we design a semantic knowledge transfer to obtain pseudo instance labels by transferring the knowledge of WSSS to WSIS while eliminating the need for  off-the-shelf proposals.
Revised:  First, we design a semantic knowledge transfer to obtain pseudo instance labels by transferring the knowledge of WSSS to WSIS while eliminating the need for the off-the-shelf proposals.
Edit intent: fluency
Original:  Second, we propose a self-refinement method that refines the pseudo instance labels in a self-supervised scheme and employs them to the training in an online manner while resolving the semantic drift problem .
Revised:  Second, we propose a self-refinement method to refine the pseudo instance labels in a self-supervised scheme and employs them to the training in an online manner while resolving the semantic drift problem .
Edit intent: fluency
Original:  Second, we propose a self-refinement method that refines the pseudo instance labels in a self-supervised scheme and employs them to the training in an online manner while resolving the semantic drift problem .
Revised:  Second, we propose a self-refinement method that refines the pseudo instance labels in a self-supervised scheme and to use the refined labels for training in an online manner while resolving the semantic drift problem .
Edit intent: clarity
Original:  The extensive experiments  demonstrate the effectiveness of our approach, and we outperform existing works on PASCAL VOC2012 without any off-the-shelf proposal techniques .
Revised:  The extensive experiments demonstrate the effectiveness of our approach, and we achieve a considerable performance without off-the-shelf proposal techniques .
Edit intent: clarity
Original:  Experimental results on four public read-world datasets show that our method reduces state-of-the-art baseline mean absolute errors by 5\%~12\%.
Revised:  Experimental results on four public real-world datasets show that our method reduces state-of-the-art baseline mean absolute errors by 5\%~12\%.
Edit intent: clarity
Original:  From the mathematical dictionaries, one can observe that most of the known closed-form formulas of non-holonomic power series involve another sequence whose evaluation linearly depends on some finite summations.
Revised:  From the mathematical dictionaries, one can observe that most of the known closed-form formulas of non-holonomic power series involve another sequence whose evaluation depends on some finite summations.
Edit intent: clarity
Original:  Thus providing a symbolic approach that yields explicit representations when linear summations for power series coefficients of non-holonomic functions appear, might be seen as a step forward towards the representation of non-holonomic power series.
Revised:  Thus providing a symbolic approach that yields complete representations when linear summations for power series coefficients of non-holonomic functions appear, might be seen as a step forward towards the representation of non-holonomic power series.
Edit intent: clarity
Original:  To tackle this problem , we propose a new method called Restore to Detect (R2D), where we show that when a deep neural network is trained for restoration (shadow removal), it learns meaningful features to delineate the shadow masks as well.
Revised:  In this work, we attempt to address this problem on two fronts. First , we propose a new method called Restore to Detect (R2D), where we show that when a deep neural network is trained for restoration (shadow removal), it learns meaningful features to delineate the shadow masks as well.
Edit intent: clarity
Original:  For the detection networkin R2D, we propose a Fine Context-aware Shadow Detection Network ( FCSD-Net ) where we constraint the receptive field size and focus on low-level features to learn fine context features better . Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our proposed method R2D improves the shadow detection performance while being able to detect fine context better compared to the other recent methods.
Revised:  We train the proposed network, FCSD-Net ) where we constraint the receptive field size and focus on low-level features to learn fine context features better . Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our proposed method R2D improves the shadow detection performance while being able to detect fine context better compared to the other recent methods.
Edit intent: clarity
Original:  For the detection networkin R2D, we propose a Fine Context-aware Shadow Detection Network ( FCSD-Net ) where we constraint the receptive field size and focus on low-level features to learn fine context features better . Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our proposed method R2D improves the shadow detection performance while being able to detect fine context better compared to the other recent methods.
Revised:  For the detection networkin R2D, we propose a Fine Context-aware Shadow Detection Network ( FCSD-Net , using the R2D learning strategy across multiple datasets . Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our proposed method R2D improves the shadow detection performance while being able to detect fine context better compared to the other recent methods.
Edit intent: clarity
Original:  For the detection networkin R2D, we propose a Fine Context-aware Shadow Detection Network ( FCSD-Net ) where we constraint the receptive field size and focus on low-level features to learn fine context features better . Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our proposed method R2D improves the shadow detection performance while being able to detect fine context better compared to the other recent methods.
Revised:  For the detection networkin R2D, we propose a Fine Context-aware Shadow Detection Network ( FCSD-Net ) where we constraint the receptive field size and focus on low-level features to learn fine context features better . Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our method improves the shadow detection performance while being able to detect fine context better compared to the other recent methods.
Edit intent: fluency
Original: This document sums up our results forthe NLP lecture at ETH in the springsemester 2021.
Revised: This document sums up our results forthe NLP lecture at ETH in the spring semester 2021.
Edit intent: fluency
Original:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Revised:  In this work, a BERT based neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Edit intent: fluency
Original:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Revised:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in order to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Edit intent: fluency
Original:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Revised:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hateful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Edit intent: fluency
Original:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Revised:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seperated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Edit intent: fluency
Original:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in onlinesocial platforms (English language), inthis case Twitter.
Revised:  In this work, a BERTbased neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in or-der to create a model identifying hate-ful and toxic comments (strictly seper-ated from offensive language) in online social platforms (English language), inthis case Twitter.
Edit intent: fluency
Original:  Three other neural net-work architectures and a GPT-2 (Radfordet al., 2019) model are also applied onthe provided data set in order to com-pare these different models.
Revised:  Three other neural network architectures and a GPT-2 (Radfordet al., 2019) model are also applied onthe provided data set in order to com-pare these different models.
Edit intent: fluency
Original:  Three other neural net-work architectures and a GPT-2 (Radfordet al., 2019) model are also applied onthe provided data set in order to com-pare these different models.
Revised:  Three other neural net-work architectures and a GPT-2 (Radfordet al., 2019) model are also applied on the provided data set in order to com-pare these different models.
Edit intent: fluency
Original:  Three other neural net-work architectures and a GPT-2 (Radfordet al., 2019) model are also applied onthe provided data set in order to com-pare these different models.
Revised:  Three other neural net-work architectures and a GPT-2 (Radfordet al., 2019) model are also applied onthe provided data set in order to compare these different models.
Edit intent: fluency
Original:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Revised:  The trained BERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Edit intent: fluency
Original:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Revised:  The trainedBERT model is then applied on two different data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Edit intent: fluency
Original:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Revised:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generalisation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Edit intent: fluency
Original:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Revised:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitter data set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Edit intent: fluency
Original:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and alsoFacebook comments;
Revised:  The trainedBERT model is then applied on two dif-ferent data sets to evaluate its generali-sation power, namely on another Twitterdata set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and also Facebook comments;
Edit intent: fluency
Original:  we focus on the En-glish HASOC 2019 data.
Revised:  we focus on the English HASOC 2019 data.
Edit intent: fluency