wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We design an expressive search space that focuses on a common and critical component of GNNs -- propagation model. Specifically, we search for propagation matrices and the connections between propagation steps. Our search space covers various graph types, e.g., homogeneous graphs, heterogeneous graphs, and can be naturally extended to higher-dimensional recommender systems and spatial-temporal data. We propose a sampling-based one-shot NAS algorithm to search for appropriate propagation patterns efficiently. Extensive experiments in three different scenarios are used to evaluate the proposed framework. We show that the performance of the models obtained by our framework is better than state-of-the-art GNN methods .
Revised:  We design an expressive search space in the form of a directed acyclic graph (DAG) to represent candidate meta graphs for a HIN, and we propose task-dependent type constraint to filter out those edge types along which message passing has no effect on the representations of nodes that are related to the downstream task. The size of the search space we define is huge, so we further propose a novel and efficient search algorithm to make the total search cost on a par with training a single GNN once. Compared with existing popular NAS algorithms, our proposed search algorithm improves the search efficiency. We conduct extensive experiments on different HINs and downstream tasks to evaluate our method, and experimental results show that our method can outperform state-of-the-art GNN methods .
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Original:  We show that the performance of the models obtained by our framework is better than state-of-the-art GNN methods . Furthermore, our framework can discover explainable meta-graphs in heterogeneous graphs .
Revised:  We show that the performance of the models obtained by our framework is better than state-of-the-art heterogeneous GNNs and also improves efficiency compared with those methods which can implicitly learn meta paths .
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Original:  The recent remarkable progress of DRL stands on regularization of policy . By constraining the update of policy, DRL allows the policy to improve stably and efficiently. Among them, a popular method, named proximal policy optimization (PPO), has been introduced  . PPO clips density ratio of the latest and baseline policies with a threshold, while its minimization target is unclear.
Revised:  The recent remarkable progress of DRL stands on regularization of policy for stable and efficient learning. A popular method, named proximal policy optimization (PPO), has been introduced . PPO clips density ratio of the latest and baseline policies with a threshold, while its minimization target is unclear.
Edit intent: clarity
Original:  Among them, a popular method, named proximal policy optimization (PPO), has been introduced  . PPO clips density ratio of the latest and baseline policies with a threshold, while its minimization target is unclear.
Revised:  Among them, a popular method, named proximal policy optimization (PPO), has been introduced for this purpose . PPO clips density ratio of the latest and baseline policies with a threshold, while its minimization target is unclear.
Edit intent: clarity
Original:  Overall, vacancies were down by 60 to 70 \% in the first weeks of lockdown. By mid September numbers had recovered slightly, though were still down by 40 to 50\%. Broken down by sector, vacancies for hospitality and graduate jobs are greatly reduced, while there were more care work and nursing vacancies during lockdown. Differences by geography are less significant than between sectors, though there is some indication that local lockdowns stall recovery and less badly hit areas may have experienced a smaller reduction in vacancies. There are also small but significant changes in the median salary and number of full time and permanent jobs. In addition to these results, this work presents an open methodology that enables a rapid and detailed survey of the job market in these unsettled conditions.
Revised:  Overall, vacancies were down by 60 to 70 
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Original:  This paper considers the following questions: (1) Why should learning the distribution of natural language help with downstream classificationtasks ? (2) Why do features learned using language modelinghelp solve downstream tasks with linear classifiers ?
Revised:  This paper initiates a mathematical study of this phenomenon for the downstream task of text classification by considering the following questions: (1) Why should learning the distribution of natural language help with downstream classificationtasks ? (2) Why do features learned using language modelinghelp solve downstream tasks with linear classifiers ?
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Original:  This paper considers the following questions: (1) Why should learning the distribution of natural language help with downstream classificationtasks ? (2) Why do features learned using language modelinghelp solve downstream tasks with linear classifiers ?
Revised:  This paper considers the following questions: (1) What is the intuitive connection between the pretraining task of next word prediction and text classification ? (2) Why do features learned using language modelinghelp solve downstream tasks with linear classifiers ?
Edit intent: meaning-changed
Original:  This paper considers the following questions: (1) Why should learning the distribution of natural language help with downstream classificationtasks ? (2) Why do features learned using language modelinghelp solve downstream tasks with linear classifiers ?
Revised:  This paper considers the following questions: (1) Why should learning the distribution of natural language help with downstream classificationtasks ? (2) How can we mathematically formalize this connection and quantify the benefit of language modeling ?
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Original:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Revised:  With a mathematical formalization of this hypothesis, we make progress towards (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
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Original:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Revised:  For (2) and show that language models that are \epsilon-optimal in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Edit intent: clarity
Original:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Revised:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that can linearly solve such classification tasks with \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Edit intent: meaning-changed
Original:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Revised:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) error , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Edit intent: clarity
Original:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks.
Revised:  For (2) , we analyze properties of the cross-entropy objective to show that \epsilon-optimal language models in cross-entropy (log-perplexity) learn features that are \mathcal{O}(\sqrt{\epsilon}) -good on natural linear classification tasks , thus demonstrating that doing well on language modeling can be beneficial for downstream tasks.
Edit intent: clarity
Original:  However, adding the temporal dimension into patterns results in an exponential growth of the search space, significantly increasing the mining process complexity. Current TPM approaches either cannot scale to large datasets, or typically work on pre-processed event sequences rather than directly on time series.
Revised:  However, adding the temporal dimension into patterns results in an exponential growth of the search space, increases significantly the mining complexity. This raises an imperative need to have a more efficient and scalable method for temporal pattern mining. Existing TPM methods either cannot scale to large datasets, or typically work on pre-processed event sequences rather than directly on time series.
Edit intent: clarity
Original:  This paper presents our comprehensive Frequent Temporal Pattern Mining from Time Series (FTPMfTS) approach which provides the following contributions: (1) The end-to-end FTPMfTS process that directly takes time series as input and produces frequent temporal patterns as output.
Revised:  This paper presents our comprehensive Frequent Temporal Pattern Mining from Time Series (FTPMfTS) with the following contributions: (1) The end-to-end FTPMfTS process that directly takes time series as input and produces frequent temporal patterns as output.
Edit intent: clarity
Original:  (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures to enable fast computations for support and confidence . (3) A number of pruning techniques for HTPGM that yield significantly faster mining.
Revised:  (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures to enable fast computations of support and confidence . (3) A number of pruning techniques for HTPGM that yield significantly faster mining.
Edit intent: clarity
Original:  (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures to enable fast computations for support and confidence . (3) A number of pruning techniques for HTPGM that yield significantly faster mining.
Revised:  (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures to enable fast computations for support and confidence , and employs effective pruning techniques to achieve significantly faster mining.
Edit intent: clarity
Original:  ( 5 ) An extensive experimental evaluation on  real-world datasets from the energy and smart city domains which shows that HTPGM outperforms the baselines and can scale to large datasets.
Revised:  ( 5 ) An extensive experimental evaluation on synthetic and real-world datasets from the energy and smart city domains which shows that HTPGM outperforms the baselines and can scale to large datasets.
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Original:  ( 5 ) An extensive experimental evaluation on  real-world datasets from the energy and smart city domains which shows that HTPGM outperforms the baselines and can scale to large datasets.
Revised:  ( 5 ) An extensive experimental evaluation on real-world datasets from the energy and smart city domains which shows that HTPGM outperforms the baselines in runtime and memory consumption, and can scale to large datasets.
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Original:  The approximate HTPGM achieves up to 3 orders of magnitude speedup compared to the baselines and consumes significantly less memory, while obtaining high accuracy compared to the exact HTPGM.
Revised:  The approximate HTPGM achieves up to 2 orders of magnitude speedup compared to the baselines and consumes significantly less memory, while obtaining high accuracy compared to the exact HTPGM.
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Original:  Specifically, we develop self-training and meta-learning techniques for few-shot training of neural sequence taggers , namely MetaST . While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
Revised:  Specifically, we develop self-training and meta-learning techniques for training neural sequence taggers , namely MetaST . While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
Edit intent: clarity
Original:  Specifically, we develop self-training and meta-learning techniques for few-shot training of neural sequence taggers , namely MetaST . While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
Revised:  Specifically, we develop self-training and meta-learning techniques for few-shot training of neural sequence taggers with few labels . While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
Edit intent: clarity
Original:  Extensive experiments on six benchmark datasets including two  massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10  \% improvement over state-of-the-art systems for the 10-shot setting.
Revised:  Extensive experiments on six benchmark datasets including two for massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10 \% improvement over state-of-the-art systems for the 10-shot setting.
Edit intent: fluency
Original:  Extensive experiments on six benchmark datasets including two  massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10  \% improvement over state-of-the-art systems for the 10-shot setting.
Revised:  Extensive experiments on six benchmark datasets including two massive multilingual NER and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10 \% improvement over state-of-the-art systems for the 10-shot setting.
Edit intent: fluency
Original:  Extensive experiments on six benchmark datasets including two  massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10  \% improvement over state-of-the-art systems for the 10-shot setting.
Revised:  Extensive experiments on six benchmark datasets including two massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method . With only 10 \% improvement over state-of-the-art systems for the 10-shot setting.
Edit intent: clarity
Original:  Extensive experiments on six benchmark datasets including two  massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10  \% improvement over state-of-the-art systems for the 10-shot setting.
Revised:  Extensive experiments on six benchmark datasets including two massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10 labeled examples for each class for each task, our method obtains 10 \% improvement over state-of-the-art systems for the 10-shot setting.
Edit intent: meaning-changed
Original:  Extensive experiments on six benchmark datasets including two  massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10  \% improvement over state-of-the-art systems for the 10-shot setting.
Revised:  Extensive experiments on six benchmark datasets including two massive multilingual NER datasets and four slot tagging datasets for task-oriented dialog systems demonstrate the effectiveness of our method with around 10 \% improvement over state-of-the-art systems demonstrating its effectiveness for the low-resource setting.
Edit intent: clarity
Original:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis ( Sigmorphon 2018) ---where our approach  enables learning of new constructions and tenses from as few as eight initial examples.
Revised:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems -- instruction following (SCAN) and morphological analysis ( Sigmorphon 2018) ---where our approach enables learning of new constructions and tenses from as few as eight initial examples.
Edit intent: fluency
Original:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis ( Sigmorphon 2018) ---where our approach  enables learning of new constructions and tenses from as few as eight initial examples.
Revised:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis ( SIGMORPHON 2018) ---where our approach enables learning of new constructions and tenses from as few as eight initial examples.
Edit intent: fluency
Original:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis ( Sigmorphon 2018) ---where our approach  enables learning of new constructions and tenses from as few as eight initial examples.
Revised:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis ( Sigmorphon 2018) -- where R enables learning of new constructions and tenses from as few as eight initial examples.
Edit intent: clarity
Original:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis ( Sigmorphon 2018) ---where our approach  enables learning of new constructions and tenses from as few as eight initial examples.
Revised:  Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems---instruction following (SCAN) and morphological analysis ( Sigmorphon 2018) ---where our approach R enables learning of new constructions and tenses from as few as eight initial examples.
Edit intent: fluency
Original: Given a simple request (e.g., Put a washed apple in the kitchen fridge ) , humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle.
Revised: Given a simple request like Put a washed apple in the kitchen fridge ) , humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle.
Edit intent: coherence
Original: Given a simple request (e.g., Put a washed apple in the kitchen fridge ) , humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle.
Revised: Given a simple request (e.g., Put a washed apple in the kitchen fridge , humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle.
Edit intent: fluency
Original:  We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text-based policies in TextWorld (C\^ot\'e et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment.
Revised:  We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text based policies in TextWorld (C\^ot\'e et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment.
Edit intent: fluency
Original:  The proposed adaptive DR estimator only requires the convergence rate conditions of the nuisance estimators and the other mild regularity conditions; that is, we do not impose a specific time-series structure and Donsker's condition. We investigate the effectiveness by using benchmark datasets compared to a past proposed DR estimator with double/debiased machine learning and an adaptive version of an augmented IPW estimator .
Revised:  We first summarize existing and new theoretical results for such OPE estimators. Then, we compare their empirical properties using benchmark datasets compared to a past proposed DR estimator with double/debiased machine learning and an adaptive version of an augmented IPW estimator .
Edit intent: clarity