wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We find that model architecture clearly influences syntactic generalization performance : Transformer models and models with explicit hierarchical structure reliably outperform pure sequence models in their predictions. In contrast , we find no clear influence of the scale of training data on these syntactic generalization tests.
Revised:  We find that model architecture clearly influences syntactic generalization performance by model architecture, with sequential models underperforming other architectures. Factorially manipulating model architecture and training dataset size (1M--40M words) , we find no clear influence of the scale of training data on these syntactic generalization tests.
Edit intent: meaning-changed
Original:  In contrast , we find no clear influence of the scale of training data on these syntactic generalization tests. We also find no clear relation between a model's perplexity and its syntactic generalization performance.
Revised:  In contrast , we find that variability in syntactic generalization performance is substantially greater by architecture than by dataset size for the corpora tested in our experiments. Our results also reveal a dissociation between perplexity and syntactic generalization performance.
Edit intent: clarity
Original:  A natural procedure for determining the collective preferences of a group then is to average its members' beliefs and add up their (0,1)-normalized utility functions . This procedure extends the well-known relative utilitarianism to decision making under uncertainty. We show that it is the only aggregation function that gives tie-breaking rights to agents who join a group and satisfies an independence condition in the spirit of Arrow's independence of irrelevant alternatives as well as four undiscriminating axioms .
Revised:  A natural procedure for determining the collective preferences of a group then is to average its members' beliefs and add up their (0,1)-normalized utility functions and average their beliefs. I show that it is the only aggregation function that gives tie-breaking rights to agents who join a group and satisfies an independence condition in the spirit of Arrow's independence of irrelevant alternatives as well as four undiscriminating axioms .
Edit intent: coherence
Original:  We show that it is the only aggregation function that gives tie-breaking rights to agents who join a group and satisfies an independence condition in the spirit of Arrow's independence of irrelevant alternatives as well as four undiscriminating axioms .
Revised:  We show that it is the only way to aggregate preferences that is monotonic in the agents' preferences and ignores preferences over redundant actions. The monotonicity condition ensures that an agent who joins a group gets to break ties in her favor if differences in preferences are due solely to differences in the utility functions .
Edit intent: clarity
Original:  Although each language has its own linguistic and pronunciation features , the lip movements of all languages share similar patterns  . Based on this idea, in this paper, we try to explore the synergized learning of multilingual lip reading  , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Revised:  Although each language has its own linguistic and pronunciation rules , the lip movements of all languages share similar patterns . Based on this idea, in this paper, we try to explore the synergized learning of multilingual lip reading , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Edit intent: clarity
Original:  Although each language has its own linguistic and pronunciation features , the lip movements of all languages share similar patterns  . Based on this idea, in this paper, we try to explore the synergized learning of multilingual lip reading  , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Revised:  Although each language has its own linguistic and pronunciation features , the lip movements of all languages share similar patterns due to the common structures of URLans . Based on this idea, in this paper, we try to explore the synergized learning of multilingual lip reading , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Edit intent: meaning-changed
Original:  Although each language has its own linguistic and pronunciation features , the lip movements of all languages share similar patterns  . Based on this idea, in this paper, we try to explore the synergized learning of multilingual lip reading  , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Revised:  Although each language has its own linguistic and pronunciation features , the lip movements of all languages share similar patterns . Based on this idea, we try to explore the synergized learning of multilingual lip reading , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Edit intent: coherence
Original:  Although each language has its own linguistic and pronunciation features , the lip movements of all languages share similar patterns  . Based on this idea, in this paper, we try to explore the synergized learning of multilingual lip reading  , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Revised:  Although each language has its own linguistic and pronunciation features , the lip movements of all languages share similar patterns . Based on this idea, in this paper, we try to explore the synergized learning of multilingual lip reading in this paper , and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Edit intent: meaning-changed
Original:  Firstly, we introduce the phonemes as our modeling units for the multilingual setting . Similar phoneme always leads to similar visual patterns . The multilingual setting would increase both the quantity and the diversity of each phoneme shared among different languages. So the learning for the multilingual target should bring improvement to the prediction of phonemes .
Revised:  Firstly, we introduce the phonemes as our modeling units for the multilingual setting . Similar phoneme always leads to similar visual patterns no matter which type the target language is .
Edit intent: coherence
Original:  Then, a  SBL block is proposed to  infer the target unit when given its previous and later context. The rules for each specific languagewhich the model itself judges to be is learned in this fill-in-the-blank manner .
Revised:  Then, a SBL block is proposed to infer the target unit given its bidirectional context, which could represent the composition rules of phonemes for each language .
Edit intent: meaning-changed
Original:  Finally, we perform a thorough comparison on LRW (English) and LRW-1000 (Mandarin) . The results outperform the existing state of the art by a large margin, and show the promising benefits from the synergized learning of different languages  .
Revised:  Finally, we perform a thorough comparison on LRW (English) and LRW-1000 (Mandarin) is performed, which shows the promising benefits from the synergized learning of different languages .
Edit intent: clarity
Original:  To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), where there are multiple sequential dialogs for a pair of a recommendation seeker (user) and a recommender (bot).
Revised:  To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for a pair of a recommendation seeker (user) and a recommender (bot).
Edit intent: clarity
Original:  To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), where there are multiple sequential dialogs for a pair of a recommendation seeker (user) and a recommender (bot).
Revised:  To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), where there are multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot).
Edit intent: fluency
Original:  Results: We investigated whether there is a relationship between interaction networks and gene expression values by using a graph convolutional auto-encoder and two end-to-end learning approaches for three interaction networks and hundreds of experimental conditions in the URLanismE.\ coli . Graph neural networks use a message passing framework to learn an embedding of a graph in a continuous space , either using network topology alone, or including additional node features.
Revised:  Results: We investigated whether there is a relationship between interaction networks and gene expression values by using a graph convolutional auto-encoder and two end-to-end learning approaches for three interaction networks and hundreds of experimental conditions in the URLanismSimultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures . Graph neural networks use a message passing framework to learn an embedding of a graph in a continuous space , either using network topology alone, or including additional node features.
Edit intent: meaning-changed
Original:  We found that graph embeddings trained on transcription and PPI networks can explain more than 50 and 40 percent, respectively, of the variance in gene expression data, thus confirming the relationship between network structure and gene expression value.
Revised:  Results: We studied the representation of transcriptional, protein-protein and genetic interaction networks in E. Coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, thus confirming the relationship between network structure and gene expression value.
Edit intent: clarity
Original:  We found that graph embeddings trained on transcription and PPI networks can explain more than 50 and 40 percent, respectively, of the variance in gene expression data, thus confirming the relationship between network structure and gene expression value. Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an auto-encoder scheme for reconstruction of the feature matrix of expression values .
Revised:  We found that graph embeddings trained on transcription and PPI networks can explain more than 50 and 40 percent, respectively, of the variance in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end graph feature auto-encoder scheme for reconstruction of the feature matrix of expression values .
Edit intent: coherence
Original:  Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an auto-encoder scheme for reconstruction of the feature matrix of expression values .
Revised:  Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an auto-encoder which is trained on the feature reconstruction task, and showed that it performs better at predicting unobserved node features than auto-encoders that are trained on the graph reconstruction task before learning to predict node features. When applied to the problem of imputing missing data in single-cell RNAseq data, our graph feature auto-encoder outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data using graph representation learning .
Edit intent: meaning-changed
Original:  We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation  , that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies  .
Revised:  MSTs constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation , that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies .
Edit intent: clarity
Original:  We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation  , that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies  .
Revised:  We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation . The edge agreement between the Pearson and rank MSTs varies significantly depending on the state of the markets, but the rank MSTs generally show strong agreement at all times. Deviation from univariate normality can be related to changes in the correlation matrices but is more difficult to connect to changes in the MSTs. Irrelevant of coefficient , that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies .
Edit intent: meaning-changed
Original:  We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation  , that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies  .
Revised:  We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation , the trees tend to have similar topologies .
Edit intent: coherence
Original:  We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation  , that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies  .
Revised:  We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation , that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies . Portfolios constructed from the MST correlation matrices have a smaller turnover than those from the full covariance matrix for the larger markets, but not for the smaller German market. Using a bootstrap method we find that the correlation matrices constructed using the rank correlations are more robust, but there is little difference between the robustness of the MSTs .
Edit intent: meaning-changed
Original:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets  will reach a minimum before increasing sometimes in the not-too-distant future.
Revised:  However, in the past we have seen the market recover and we can expect the market to recover again, so on this basis the recover of the markets will reach a minimum before increasing sometimes in the not-too-distant future.
Edit intent: meaning-changed
Original:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets  will reach a minimum before increasing sometimes in the not-too-distant future.
Revised:  However, in the past we have seen markets recover just as we will see the current markets recover again, and on this basis the recover of the markets will reach a minimum before increasing sometimes in the not-too-distant future.
Edit intent: coherence
Original:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets  will reach a minimum before increasing sometimes in the not-too-distant future.
Revised:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis we assume the Standard and Poor's 500 (S will reach a minimum before increasing sometimes in the not-too-distant future.
Edit intent: meaning-changed
Original:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets  will reach a minimum before increasing sometimes in the not-too-distant future.
Revised:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets P500) index will reach a minimum before increasing sometimes in the not-too-distant future.
Edit intent: meaning-changed
Original:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets  will reach a minimum before increasing sometimes in the not-too-distant future.
Revised:  However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets will reach a minimum before rising again in the not-too-distant future.
Edit intent: clarity
Original:  Here we present two forecast models of the S&P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Revised:  Here we present four forecast models of the S&P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA.
Edit intent: meaning-changed
Original:  Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 started , and (b) The uncertainties are fitted from patterns of the S\& P500 for the last 24 years.
Revised:  Our forecast was made on the premise that: (a) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19 started , and (b) The uncertainties are fitted from patterns of the S\& P500 for the last 24 years.
Edit intent: clarity
Original:  Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 started , and (b) The uncertainties are fitted from patterns of the S\& P500 for the last 24 years.
Revised:  Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 , and (b) The uncertainties are fitted from patterns of the S\& P500 for the last 24 years.
Edit intent: clarity
Original:  Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 started , and (b) The uncertainties are fitted from patterns of the S\& P500 for the last 24 years.
Revised:  Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 started , and (b) fluctuations derived from the S P500 for the last 24 years.
Edit intent: clarity
Original:  Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 started , and (b) The uncertainties are fitted from patterns of the S\& P500 for the last 24 years.
Revised:  Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 started , and (b) The uncertainties are fitted from patterns of the S\& P500 over the last 24 years.
Edit intent: fluency
Original: To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization  for mining the available scientific literature.
Revised: The outbreak of COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization for mining the available scientific literature.
Edit intent: clarity
Original: To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization  for mining the available scientific literature.
Revised: To address the need for refined information in COVID-19 raises attention from the researchers from various communities. While many scientific articles have been published, a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. To address the requests, we propose our CAiRE-COVID, a neural-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization for mining the available scientific literature.
Edit intent: meaning-changed
Original: To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization  for mining the available scientific literature.
Revised: To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses open-domain question answering (QA) techniques combined with summarization for mining the available scientific literature.
Edit intent: clarity