wanyu/IteraTeR_v2 huggingface dataset:
Original:  Moreover, we show that this model reproduces the statistics of the famous Clinton-Gore Poll data almost faithfully with a prior belief state independent of the question order.
Revised:  Moreover, we show that this model reproduces the statistics of the well-known Clinton-Gore Poll data almost faithfully with a prior belief state independent of the question order.
Edit intent: clarity
Original:  Moreover, we show that this model reproduces the statistics of the famous Clinton-Gore Poll data almost faithfully with a prior belief state independent of the question order.
Revised:  Moreover, we show that this model reproduces the statistics of the famous Clinton-Gore Poll data with a prior belief state independent of the question order.
Edit intent: clarity
Original:  This model successfully removes the order effect from the data to determine the genuine distribution of the opinion to the Poll.
Revised:  This model successfully corrects for the order effect from the data to determine the genuine distribution of the opinion to the Poll.
Edit intent: clarity
Original:  This model successfully removes the order effect from the data to determine the genuine distribution of the opinion to the Poll.
Revised:  This model successfully removes the order effect in the data to determine the genuine distribution of the opinion to the Poll.
Edit intent: fluency
Original:  This model successfully removes the order effect from the data to determine the genuine distribution of the opinion to the Poll.
Revised:  This model successfully removes the order effect from the data to determine the "genuine" distribution of the opinion to the Poll.
Edit intent: fluency
Original:  This model successfully removes the order effect from the data to determine the genuine distribution of the opinion to the Poll.
Revised:  This model successfully removes the order effect from the data to determine the genuine distribution of the opinions in the Poll.
Edit intent: fluency
Original:  Independently of modeling of the concrete psychological effects, the paper provides psychologist-friendly presentation of the theory of quantum instruments - the most general mathematical framework for quantum measurements .
Revised:  The paper also provides an accessible introduction to the theory of quantum instruments - the most general mathematical framework for quantum measurements .
Edit intent: clarity
Original: Recent years have witnessed remarkable progress made towards the computational detection of fake news .
Revised: Recent years have witnessed remarkable progress towards computational fake news detection .
Edit intent: clarity
Original:  To mitigate its negative impact, however, we argue that a critical element is to understand why people spread fake news.
Revised:  To mitigate its negative impact, we argue that a critical element is to understand why people spread fake news.
Edit intent: coherence
Original:  To mitigate its negative impact, however, we argue that a critical element is to understand why people spread fake news.
Revised:  To mitigate its negative impact, however, we argue that it is critical to understand what user attributes potentially cause users to share fake news.
Edit intent: clarity
Original:  One obstacle in learning such user behavior is that most data is subject to selection bias , rendering partially observed fake news dissemination among users.
Revised:  One obstacle in learning such user behavior is typically subject to selection bias , rendering partially observed fake news dissemination among users.
Edit intent: clarity
Original:  To discover causal user attributes, we confront another obstacle of finding the confounders in fake news dissemination . Drawing on theories in causal inference , in this work, we first propose a principled approach to unbiased modelings of fake news dissemination under selection bias .
Revised:  To discover causal user attributes, we confront another obstacle of finding the confounders in fake news dissemination . Drawing on causal inference theories, we first propose a principled approach to unbiased modelings of fake news dissemination under selection bias .
Edit intent: clarity
Original:  To discover causal user attributes, we confront another obstacle of finding the confounders in fake news dissemination . Drawing on theories in causal inference , in this work, we first propose a principled approach to unbiased modelings of fake news dissemination under selection bias .
Revised:  To discover causal user attributes, we confront another obstacle of finding the confounders in fake news dissemination . Drawing on theories in causal inference , in this work, we first propose a principled approach to alleviating selection bias in fake news dissemination under selection bias .
Edit intent: clarity
Original:  To discover causal user attributes, we confront another obstacle of finding the confounders in fake news dissemination . Drawing on theories in causal inference , in this work, we first propose a principled approach to unbiased modelings of fake news dissemination under selection bias .
Revised:  To discover causal user attributes, we confront another obstacle of finding the confounders in fake news dissemination . Drawing on theories in causal inference , in this work, we first propose a principled approach to unbiased modelings of fake news dissemination .
Edit intent: clarity
Original:  We then consider the learned  fake news sharing behavior as the measured confounder and further identify the user attributes that potentially cause users to spread fake news .
Revised:  We then consider the learned fake news sharing behavior as the surrogate confounder that can fully capture the causal links between user attributes and user susceptibility .
Edit intent: clarity
Original:  Sign Language Recognition is a challenging area in the field of computer vision and recent developments have been able to achieve near perfect results for the task, though some challenges are yet to be solved.
Revised:  Sign Language identification is a challenging area in the field of computer vision and recent developments have been able to achieve near perfect results for the task, though some challenges are yet to be solved.
Edit intent: clarity
Original:  In this paper we propose a novel machine learning based pipeline for American Sign Language recognition using hand track points.
Revised:  In this paper we propose a novel machine learning based pipeline for American Sign Language identification using hand track points.
Edit intent: fluency
Original:  Our system achieved an Accuracy of 95.66\% to recognize American sign language gestures.
Revised:  Our system achieved an Accuracy of 95.66\% to identify American sign language gestures.
Edit intent: fluency
Original:  Their performance was contrasted to determine the best pre-processing scheme and Algorithm Pair .
Revised:  Their performance was contrasted to determine the best pre-processing scheme and algorithm pair .
Edit intent: fluency
Original: This paper targets to explore the inter-subject variations eliminated facial expression representation in the compressed video domain .
Revised: How to extract effective expression representations that invariant to the identity-specific attributes is a long-lasting problem for facial expression recognition (FER) .
Edit intent: coherence
Original:  Most of the previous methods process the RGB images of a sequence, while  the off-the-shelf and valuable expression-related muscle movement  already embedded in the compression format.
Revised:  Most of the previous methods process the RGB images of a sequence, while we argue that the off-the-shelf and valuable expression-related muscle movement already embedded in the compression format.
Edit intent: clarity
Original:  Most of the previous methods process the RGB images of a sequence, while  the off-the-shelf and valuable expression-related muscle movement  already embedded in the compression format.
Revised:  Most of the previous methods process the RGB images of a sequence, while the off-the-shelf and valuable expression-related muscle movement is already embedded in the compression format.
Edit intent: coherence
Original:  In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network.
Revised:  In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possibly extract identity factors from the I frame with a pre-trained face recognition network.
Edit intent: fluency
Original:  By enforcing the marginal independent of them, the expression feature is expected to be purer for the expression and be robust to identity shifts.
Revised:  By enforcing the marginal independence of them, the expression feature is expected to be purer for the expression and be robust to identity shifts.
Edit intent: fluency
Original: In the past few years, we observed a wide adoption of practical systems that use Automatic Speech Recognition (ASR) systems to improve human-machine interaction.
Revised: The wide adoption of practical systems that use Automatic Speech Recognition (ASR) systems to improve human-machine interaction.
Edit intent: coherence
Original: In the past few years, we observed a wide adoption of practical systems that use Automatic Speech Recognition (ASR) systems to improve human-machine interaction.
Revised: In the past few years, we observed a wide adoption of Automatic Speech Recognition (ASR) systems to improve human-machine interaction.
Edit intent: clarity
Original: In the past few years, we observed a wide adoption of practical systems that use Automatic Speech Recognition (ASR) systems to improve human-machine interaction.
Revised: In the past few years, we observed a wide adoption of practical systems that use Automatic Speech Recognition (ASR) remarkably enhanced human-machine interaction.
Edit intent: clarity
Original:  Modern ASR systems are based on neural networks and prior research demonstrated that these systems are susceptible to adversarial examples, i.e., malicious audio inputs that lead to misclassification by the victim's network during the system's run time.
Revised:  Prior research has demonstrated that modern ASR systems are susceptible to adversarial examples, i.e., malicious audio inputs that lead to misclassification by the victim's network during the system's run time.
Edit intent: clarity
Original:  Modern ASR systems are based on neural networks and prior research demonstrated that these systems are susceptible to adversarial examples, i.e., malicious audio inputs that lead to misclassification by the victim's network during the system's run time.
Revised:  Modern ASR systems are based on neural networks and prior research demonstrated that these systems are susceptible to adversarial examples, i.e., malicious audio inputs that lead to misclassification by the victim's model at run time.
Edit intent: clarity
Original:  The research question if ASR systems are also vulnerable to data poisoning attacks is still unanswered.
Revised:  The research question of whether ASR systems are also vulnerable to data poisoning attacks is still unanswered.
Edit intent: fluency
Original:  The research question if ASR systems are also vulnerable to data poisoning attacks is still unanswered.
Revised:  The research question if ASR systems are also vulnerable to data-poisoning attacks is still unanswered.
Edit intent: fluency
Original:  In such an attack, a manipulation happens during the training phase of the neural network : an adversary injects malicious inputs into the training set such that the neural network's integrity and performance are compromised.
Revised:  In such an attack, a manipulation happens during the training phase : an adversary injects malicious inputs into the training set such that the neural network's integrity and performance are compromised.
Edit intent: clarity
Original:  In such an attack, a manipulation happens during the training phase of the neural network : an adversary injects malicious inputs into the training set such that the neural network's integrity and performance are compromised.
Revised:  In such an attack, a manipulation happens during the training phase of the neural network : an adversary injects malicious inputs into the training set to compromise the neural network's integrity and performance are compromised.
Edit intent: clarity
Original:  In this paper, we present the first data poisoning attack in the audio domain, called VENOMAVE. Prior work in the image domain demonstrated several types of data poisoning attacks, but they cannot be applied to the audio domain.
Revised:  In this paper, we present the first data poisoning attack in the audio domain, called VENOMAVE. Prior work in the image domain demonstrated several types of data-poisoning attacks, but they cannot be applied to the audio domain.
Edit intent: style
Original:  In this paper, we present the first data poisoning attack in the audio domain, called VENOMAVE. Prior work in the image domain demonstrated several types of data poisoning attacks, but they cannot be applied to the audio domain.
Revised:  In this paper, we present the first data poisoning attack in the audio domain, called VENOMAVE. Prior work in the image domain demonstrated several types of data poisoning attacks, but these results cannot directly be applied to the audio domain.
Edit intent: clarity
Original:  To demonstrate the practical feasibility of our attack , we evaluate VENOMAVE on an ASR system that detects sequences of digits from 0 to 9.
Revised:  To demonstrate the practical feasibility of our attack , we evaluate VENOMAVE on an ASR system that detects sequences of digits .
Edit intent: clarity
Original:  We conclude that data poisoning attacks against ASR systems represent a real threat that needs to be considered .
Revised:  We conclude that data poisoning attacks against ASR represent a real threat that needs to be considered .
Edit intent: clarity
Original:  We devise four operators to generate the views -- embedding masking, embedding dropout, node dropout, and edge dropout -- that augment node representation from two perspectives of ID embedding and graph structure .
Revised:  We devise four operators to generate the views -- node dropout, and edge dropout -- that augment node representation from two perspectives of ID embedding and graph structure .
Edit intent: coherence
Original:  We devise four operators to generate the views -- embedding masking, embedding dropout, node dropout, and edge dropout -- that augment node representation from two perspectives of ID embedding and graph structure .
Revised:  We devise four operators to generate the views -- embedding masking, embedding dropout, node dropout, and edge dropout -- that change the graph structure in different manners .
Edit intent: clarity
Original: Embedding learning for large-vocabulary categorical features (e.g. user/item IDs , and words) is crucial for deep learning, and especially neural models for recommendation systems and natural language understanding tasks.
Revised: Embedding learning of categorical features (e.g. user/item IDs , and words) is crucial for deep learning, and especially neural models for recommendation systems and natural language understanding tasks.
Edit intent: clarity
Original:  In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), with non-one-hot encodings and a deep neural network (embedding network ) to compute embeddings on the fly without having to store them . DHE first encodes the feature value to a dense vector with multiple hashing functions and  then applies a DNN to generate the embedding.
Revised:  In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), with non-one-hot encodings and a deep neural network (embedding network ) to compute embeddings on the fly . DHE first encodes the feature value to a dense vector with multiple hashing functions and then applies a DNN to generate the embedding.
Edit intent: coherence
Original:  In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), with non-one-hot encodings and a deep neural network (embedding network ) to compute embeddings on the fly without having to store them . DHE first encodes the feature value to a dense vector with multiple hashing functions and  then applies a DNN to generate the embedding.
Revised:  In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), with non-one-hot encodings and a deep neural network (embedding network ) to compute embeddings on the fly without having to store them . DHE first encodes the feature value to a unique identifier vector with multiple hashing functions and then applies a DNN to generate the embedding.
Edit intent: clarity
Original:  In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), with non-one-hot encodings and a deep neural network (embedding network ) to compute embeddings on the fly without having to store them . DHE first encodes the feature value to a dense vector with multiple hashing functions and  then applies a DNN to generate the embedding.
Revised:  In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), with non-one-hot encodings and a deep neural network (embedding network ) to compute embeddings on the fly without having to store them . DHE first encodes the feature value to a dense vector with multiple hashing functions and transformations, and then applies a DNN to generate the embedding.
Edit intent: clarity
Original:  The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to memorize embedding information . Empirical results show that DHE outperforms state-of-the-art hashing-based embedding learning algorithms, and achieves comparable AUC against the standard one-hot encoding, with significantly smaller model sizes.
Revised:  The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to learn embedding generation . Empirical results show that DHE outperforms state-of-the-art hashing-based embedding learning algorithms, and achieves comparable AUC against the standard one-hot encoding, with significantly smaller model sizes.
Edit intent: clarity
Original:  The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to memorize embedding information . Empirical results show that DHE outperforms state-of-the-art hashing-based embedding learning algorithms, and achieves comparable AUC against the standard one-hot encoding, with significantly smaller model sizes.
Revised:  The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to memorize embedding information . Empirical results show that DHE achieves comparable AUC against the standard one-hot encoding, with significantly smaller model sizes.
Edit intent: coherence
Original:  The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to memorize embedding information . Empirical results show that DHE outperforms state-of-the-art hashing-based embedding learning algorithms, and achieves comparable AUC against the standard one-hot encoding, with significantly smaller model sizes.
Revised:  The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to memorize embedding information . Empirical results show that DHE outperforms state-of-the-art hashing-based embedding learning algorithms, and achieves comparable AUC against the standard one-hot full embedding, with smaller model sizes.
Edit intent: clarity
Original:  Our work sheds light on  design of DNN-based alternative embedding schemes for categorical features without using embedding table lookup.
Revised:  Our work sheds light on the design of DNN-based alternative embedding schemes for categorical features without using embedding table lookup.
Edit intent: fluency
Original:  First, we numerically demonstrate the validity of the homogenization method, and  then perform optimization calculations based on the waveguide settings of the acoustic metasurfaces.
Revised:  First, we provide a numerical example that supports the validity of the homogenization method, and then perform optimization calculations based on the waveguide settings of the acoustic metasurfaces.
Edit intent: clarity
Original:  First, we numerically demonstrate the validity of the homogenization method, and  then perform optimization calculations based on the waveguide settings of the acoustic metasurfaces.
Revised:  First, we numerically demonstrate the validity of the homogenization method, and we then perform optimization calculations based on the waveguide settings of the acoustic metasurfaces.
Edit intent: fluency
Original: We propose a method for joint multichannel speech dereverberation with two spatial-aware multichannel tasks: direction-of-arrival (DOA) estimation and speech separation.
Revised: We propose a method for joint multichannel speech dereverberation with two spatial-aware tasks: direction-of-arrival (DOA) estimation and speech separation.
Edit intent: clarity
Original:  The proposed method addresses  tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech processing tasks.
Revised:  The proposed method addresses tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech enhancement tasks.
Edit intent: clarity
Original:  The proposed method is inspired by the excellent sequence modeling capability of bi-directional encoder representation from transformers (BERT).
Revised:  The proposed method is inspired by the excellent sequence modeling capability of bidirectional encoder representation from transformers (BERT).
Edit intent: fluency
Original:  Instead of utilizing explicit representations from pretraining in a self-supervised way , we utilizes transformer encoded hidden representations in a supervised way .
Revised:  Instead of utilizing explicit representations from pretraining in a self-supervised manner , we utilizes transformer encoded hidden representations in a supervised way .
Edit intent: clarity
Original:  Instead of utilizing explicit representations from pretraining in a self-supervised way , we utilizes transformer encoded hidden representations in a supervised way .
Revised:  Instead of utilizing explicit representations from pretraining in a self-supervised way , we utilizes transformer encoded hidden representations in a supervised manner .
Edit intent: clarity
Original:  We show that this information asymmetry can lead to a stable outcome of population learning, which  does not occur in an environment of general independent learners.
Revised:  We show that this information asymmetry can lead to a stable outcome of population learning, which generally does not occur in an environment of general independent learners.
Edit intent: coherence
Original:  Furthermore, we discuss the resulted post-learning policies , show that they are almost optimal in the underlying game sense, and provide numerical hints of almost welfare-optimal of the resulted policies .
Revised:  The resulting post-learning policies , show that they are almost optimal in the underlying game sense, and provide numerical hints of almost welfare-optimal of the resulted policies .
Edit intent: coherence
Original:  Furthermore, we discuss the resulted post-learning policies , show that they are almost optimal in the underlying game sense, and provide numerical hints of almost welfare-optimal of the resulted policies .
Revised:  Furthermore, we discuss the resulted post-learning policies are almost optimal in the underlying game sense, and provide numerical hints of almost welfare-optimal of the resulted policies .
Edit intent: coherence