wanyu/IteraTeR_full_sent huggingface dataset:
Original:  and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Revised:  and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features , specifically, we encode user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Edit intent: clarity
Original:  and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features . Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts).
Revised:  and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features . Specifically, we encode words in user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words' importance, and capture semantic sequence features from the user timelines (posts).
Edit intent: meaning-changed
Original: We extend the WSINDy (Weak SINDy) method of sparse recovery introduced previously by the authors ( arXiv:2005.04339) to the setting of partial differential equations (PDEs).
Revised: Sparse Identification of Nonlinear Dynamics (SINDy) is a method of system discovery that has been shown to successfully recover governing dynamical systems from data (Brunton et al., PNAS, '16; Rudy et al., Sci. Adv. '17). Recently, several groups have independently discovered that the weak formulation provides orders of magnitude better robustness to noise. Here we extend our Weak SINDy (WSINDy) framework introduced in ( arXiv:2005.04339) to the setting of partial differential equations (PDEs).
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Original:  As in the case of ODE discovery, the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering .
Revised:  The elimination of pointwise derivative approximations via the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering .
Edit intent: clarity
Original:  As in the case of ODE discovery, the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering .
Revised:  As in the case of ODE discovery, the weak form enables effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering .
Edit intent: clarity
Original:  As in the case of ODE discovery, the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering .
Revised:  As in the case of ODE discovery, the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of model coefficients from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering .
Edit intent: clarity
Original:  As in the case of ODE discovery, the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering .
Revised:  As in the case of ODE discovery, the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as robust identification of PDEs in the large noise regime (with signal-to-noise ratio approaching one in many well-known cases). This is accomplished by discretizing a convolutional weak form of the PDE and exploiting separability of test functions for efficient model identification using the Fast Fourier Transform .
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Original:  The resulting WSINDy _PDE algorithm uses separable test functions implemented efficiently via convolutions for discovery of PDE models with computational complexity O(NM)from data points with M = N ^{D+1(N^{D+1}\log(N)) for datasets with N points } in each of D+1 dimensions .
Revised:  The resulting WSINDy algorithm for PDEs has a worst-case computational complexity of \mathcal{O(N^{D+1}\log(N)) for datasets with N points } in each of D+1 dimensions .
Edit intent: meaning-changed
Original:  The resulting WSINDy _PDE algorithm uses separable test functions implemented efficiently via convolutions for discovery of PDE models with computational complexity O(NM)from data points with M = N ^{D+1(N^{D+1}\log(N)) for datasets with N points } in each of D+1 dimensions . We demonstrate on several notoriously challenging PDEsthe speed and accuracy with which WSINDy_PDE recovers the correct models from datasets with surprisingly large levels noise (often with levels of noise much greater than 10\%)(\log(N)) operations per datapoint).
Revised:  The resulting WSINDy _PDE algorithm uses separable test functions implemented efficiently via convolutions for discovery of PDE models with computational complexity O(NM)from data points with M = N ^{D+1(N^{D+1}\log(N)) for datasets with N points } in each of D+1 dimensions (i.e. \mathcal{O(\log(N)) operations per datapoint).
Edit intent: meaning-changed
Original:  Furthermore, our Fourier-based implementation reveals a connection between robustness to noise and the spectra of test functions, which we utilize in an }  .
Revised:  Furthermore, our Fourier-based implementation reveals a connection between robustness to noise and the spectra of test functions, which we utilize in an } selection algorithm for test functions. Finally, we introduce a learning algorithm for the threshold in sequential-thresholding least-squares (STLS) that enables model identification from large libraries, and we utilize scale-invariance at the continuum level to identify PDEs from poorly-scaled datasets. We demonstrate WSINDy's robustness, speed and accuracy on several challenging PDEs .
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Original: We introduce DART, a large dataset for open-domain structured data record to text generation . We consider the structured data record input as a set of RDF entity-relation triples, a format widely used for knowledge representation and semantics description. DART consists of 82, 191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set.
Revised: We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set.
Edit intent: clarity
Original:  DART consists of 82, 191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. This hierarchical, structured format with its open-domain nature differentiates DART from other existing table-to-text corpora. We conduct an analysis of DART on several state-of-the-art text generation models, showing that it introduces new and interesting challenges compared to existing datasets.
Revised:  DART consists of 82, 191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art text generation models, showing that it introduces new and interesting challenges compared to existing datasets.
Edit intent: meaning-changed
Original:  We conduct an analysis of DART on several state-of-the-art text generation models, showing that it introduces new and interesting challenges compared to existing datasets. Furthermore, we demonstrate that finetuning pretrained language models on DART facilitates out-of-domain generalization on the WebNLG 2017 dataset.
Revised:  We conduct an analysis of DART on several state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization on the WebNLG 2017 dataset.
Edit intent: clarity
Original: A lot of work has been done recently to build sound language models for the textual data , but not much such has been done in the case of speech/audio type data. In the case of text, wordscan be represented by a unique fixed-length vector. Such models for audio type data can not only lead to great advances in the speech-related natural language processing tasks but can also reduce the need for converting speech to text for performing the same.
Revised: A lot of work has been done recently to build sound language models for the textual data , but not much such has been done in the case of audio-based language models. This paper proposes a Convolutional Autoencoder based neural architecture to model syntactically and semantically adequate contextualized representations of varying length spoken words. The use of such representations can not only lead to great advances in the speech-related natural language processing tasks but can also reduce the need for converting speech to text for performing the same.
Edit intent: clarity
Original:  Such models for audio type data can not only lead to great advances in the speech-related natural language processing tasks but can also reduce the need for converting speech to text for performing the same. This paper proposes a novel model architecture that produces syntactically, contextualized, and semantically adequate representation of varying length spoken words.
Revised:  Such models for audio type data can not only lead to great advances in the speech-related natural language processing tasks but can also reduce the need for converting speech to text to perform these tasks.
Edit intent: clarity
Original:  The performance of the spoken word embeddings generated by the proposed model was validated by (1) inspecting the vector space generated , and (2) evaluating its performance on the downstream task of next spoken word prediction in a speech .
Revised:  The performance of the spoken word embeddings generated by the proposed model was validated by (1) examining the generated vector space , and (2) evaluating its performance on the downstream task of next spoken word prediction in a speech .
Edit intent: clarity
Original:  The performance of the spoken word embeddings generated by the proposed model was validated by (1) inspecting the vector space generated , and (2) evaluating its performance on the downstream task of next spoken word prediction in a speech .
Revised:  The performance of the spoken word embeddings generated by the proposed model was validated by (1) inspecting the vector space generated , and (2) evaluating its performance on three benchmark datasets for measuring word similarities, against existing widely used text-based language models that are trained on the transcriptions. The proposed model was able to demonstrate its robustness when compared to the other two language-based models .
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Original: We discuss a method for estimating the convergence speed of Anderson Acceleration (AA) applied to the Alternating Direction Method of Multipliers (ADMM), for the case where ADMM by itself converges linearly.
Revised: We discuss a method for estimating the convergence speed of Anderson Acceleration (AA) speeds up the Alternating Direction Method of Multipliers (ADMM), for the case where ADMM by itself converges linearly.
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Original:  It has been observed empirically that AA may speed up asymptotic ADMM convergence by a large amount for various applications in data science and machine learning, but there are no known convergence bounds for AA with finite window size that would allow quantification of this improvement in asymptotic convergence speed. We discuss an approach to quantify the improved linear asymptotic convergence factor that is based on computing the optimalasymptotic convergence factor for a stationary version of AA applied to ADMM . Numerical tests show that this allows us to estimate the asymptotic linear convergence factor of AA with finite window size applied to ADMM.
Revised:  We do so by considering the spectral properties of the Jacobians of ADMM and a stationary version of AA evaluated at the fixed point, where the coefficients of the stationary version are computed such that its asymptotic linear convergence factor is optimal . Numerical tests show that this allows us to estimate the asymptotic linear convergence factor of AA with finite window size applied to ADMM.
Edit intent: meaning-changed
Original:  We discuss an approach to quantify the improved linear asymptotic convergence factor that is based on computing the optimalasymptotic convergence factor for a stationary version of AA applied to ADMM . Numerical tests show that this allows us to estimate the asymptotic linear convergence factor of AA with finite window size applied to ADMM.
Revised:  We discuss an approach to quantify the improved linear asymptotic convergence factor that is based on computing the optimalasymptotic convergence factor for a stationary version of AA applied to ADMM . Numerical tests show that this allows us to quantify the improved linear asymptotic convergence speed of AA-ADMM as compared to the convergence factor of ADMM used by itself. This way of estimating AA-ADMM convergence speed is useful because there are no known convergence bounds for AA with finite window size applied to ADMM.
Edit intent: meaning-changed
Original:  We discuss an approach to quantify the improved linear asymptotic convergence factor that is based on computing the optimalasymptotic convergence factor for a stationary version of AA applied to ADMM . Numerical tests show that this allows us to estimate the asymptotic linear convergence factor of AA with finite window size applied to ADMM. The numerical results also explain how and by how much nonlinear acceleration methods like AA can accelerate ADMM, in terms of the spectral properties of the Jacobians of ADMM and stationary AA evaluated at the fixed point .
Revised:  We discuss an approach to quantify the improved linear asymptotic convergence factor that is based on computing the optimalasymptotic convergence factor for a stationary version of AA applied to ADMM . Numerical tests show that this allows us to estimate the asymptotic linear convergence factor of AA with finite window size that would allow quantification of this improvement in asymptotic convergence speed .
Edit intent: clarity
Original: We explain how Anderson Acceleration (AA) speeds up the Alternating Direction Method of Multipliers (ADMM) , for the case where ADMM by itself converges linearly.
Revised: We explain how Anderson Acceleration (AA) can be a powerful mechanism to improve the asymptotic linear convergence speed of the Alternating Direction Method of Multipliers (ADMM) , for the case where ADMM by itself converges linearly.
Edit intent: clarity
Original:   We do so by considering the spectral properties of the Jacobians of ADMM and a stationary version of AA evaluated at the fixed point, where the coefficients of the stationary version are computed such that its asymptotic linear convergence factor is optimal.
Revised:  However, theoretical results to quantify this improvement do not exist yet. In this paper we explain and quantify this improvement in linear asymptotic convergence speed for the special case of a stationary version of AA applied to ADMM. We do so by considering the spectral properties of the Jacobians of ADMM and a stationary version of AA evaluated at the fixed point, where the coefficients of the stationary version are computed such that its asymptotic linear convergence factor is optimal.
Edit intent: meaning-changed
Original:   We do so by considering the spectral properties of the Jacobians of ADMM and a stationary version of AA evaluated at the fixed point, where the coefficients of the stationary version are computed such that its asymptotic linear convergence factor is optimal.
Revised:  We do so by considering the spectral properties of the Jacobians of ADMM and the stationary version of AA evaluated at the fixed point, where the coefficients of the stationary version are computed such that its asymptotic linear convergence factor is optimal.
Edit intent: fluency
Original:   We do so by considering the spectral properties of the Jacobians of ADMM and a stationary version of AA evaluated at the fixed point, where the coefficients of the stationary version are computed such that its asymptotic linear convergence factor is optimal.
Revised:  We do so by considering the spectral properties of the Jacobians of ADMM and a stationary version of AA evaluated at the fixed point, where the coefficients of the stationary AA method are computed such that its asymptotic linear convergence factor is optimal.
Edit intent: clarity
Original:  Numerical tests show that this allows us to quantify the improved linear asymptotic convergence speed of AA-ADMM as compared to the convergence factor of ADMM used by itself.
Revised:  Numerical tests show that this allows us to quantify the improved linear asymptotic convergence speed of AA-ADMM method are computed analytically or by optimization, based on previous work on optimal stationary AA acceleration. Using this spectral picture and those analytical results, our approach provides new insight into how and by how much the stationary AA method can improve the asymptotic linear convergence factor of ADMM used by itself.
Edit intent: meaning-changed
Original:  Numerical tests show that this allows us to quantify the improved linear asymptotic convergence speed of AA-ADMM as compared to the convergence factor of ADMM used by itself. This way of estimating AA-ADMM convergence speed is useful because there are no known convergence bounds for AA with finite window size that would allow quantification of this improvement in asymptotic convergence speed .
Revised:  Numerical tests show that this allows us to quantify the improved linear asymptotic convergence speed of AA-ADMM as compared to the convergence factor of ADMM . Numerical results also indicate that the optimal linear convergence factor of the stationary AA methods gives a useful estimate for the asymptotic linear convergence speed of the non-stationary AA method that is used in practice .
Edit intent: meaning-changed
Original: To explore heterogeneous information networks (HINs), network representation learning (NRL) is proposed, which represents a network in a low-dimension space . Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue.
Revised: In network representation learning (NRL) is proposed, which represents a network in a low-dimension space . Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue.
Edit intent: clarity
Original: To explore heterogeneous information networks (HINs), network representation learning (NRL) is proposed, which represents a network in a low-dimension space . Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue. In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised: To explore heterogeneous information networks (HINs), network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework PF-HIN to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Edit intent: meaning-changed
Original:  In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised:  In this paper, we propose a pre-training and fine-tuning framework , PF-HIN to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Edit intent: fluency