wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Rather than directly predicting actuator commands, we propose translating the natural language instruction to a Python function which when executed queries the scene by accessing the output of the object detector and controls the robot to perform the specified task.
Revised:  Rather than directly predicting actuator commands, we propose translating the natural language instruction to a Python function which queries the scene by accessing the output of the object detector and controls the robot to perform the specified task.
Edit intent: clarity
Original:  Given some oriented graph G=(V,E), an oriented r-coloring for G is a partition of the vertex set V into r independent sets, such that all the arcs between two of these sets have the same direction.
Revised: In this paper we consider colorings of oriented graphs, i.e. digraphs without cycles of length 2. Given some oriented graph G=(V,E), an oriented r-coloring for G is a partition of the vertex set V into r independent sets, such that all the arcs between two of these sets have the same direction.
Edit intent: meaning-changed
Original:  In order to generalize the results on computing the oriented chromatic number of special graph classes, we consider the parameterized complexity of the Oriented Chromatic Number problem by so-called structural parameters, which are measuring the difficulty of decomposing a graph into a special tree-structure .
Revised:  In order to generalize the results on computing the oriented chromatic number of special graph classes, we consider the parameterized complexity of the Oriented Chromatic Number problem by so-called structural parameters, which are measuring the difficulty of decomposing a graph into a special tree-structure 
Edit intent: fluency
Original:  Unlike many previous studies on discrete trading signals in portfolio management , we make the agent to short in a continuous action space , design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors , in addition, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data.
Revised:  Unlike many previous studies on discrete trading signals , we make the agent to short in a continuous action space , design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors , in addition, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data.
Edit intent: clarity
Original:  Unlike many previous studies on discrete trading signals in portfolio management , we make the agent to short in a continuous action space , design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors , in addition, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data.
Revised:  Unlike many previous studies on discrete trading signals in portfolio management , we make the agent to short in a continuous action space for portfolio optimization. In addition, we design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors , in addition, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data.
Edit intent: meaning-changed
Original:  Unlike many previous studies on discrete trading signals in portfolio management , we make the agent to short in a continuous action space , design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors , in addition, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data.
Revised:  Unlike many previous studies on discrete trading signals in portfolio management , we make the agent to short in a continuous action space , design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors . Furthermore, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data.
Edit intent: fluency
Original:  We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations . Unlike many previous studies on discrete trading signals, we make the agent to short in a continuous action space for portfolio optimization. In addition, we design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors.
Revised:  We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations , such as adding short mechanism, designing an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors.
Edit intent: coherence
Original:  In experiments, we use our model in several randomly selected portfolios which include CSI300 that represents the market's rate of return and the randomly selected constituents of CSI500. The experimental results show that no matter what stocks we select in our portfolios, we can always get a higher return than the market itself, namely we can get artificial intelligence through deep reinforcement learning to defeat market .
Revised:  In experiments, we use our model in several randomly selected portfolios , the results show we can get artificial intelligence and defeat market through deep reinforcement learning to defeat market .
Edit intent: coherence
Original:  After that , a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from  very limited number of training samples generated on the hardware.
Revised:  Thereafter , a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from very limited number of training samples generated on the hardware.
Edit intent: coherence
Original:  After that , a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from  very limited number of training samples generated on the hardware.
Revised:  After that , a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware.
Edit intent: fluency
Original:  Our PPA-hardness result applies even when all mass distributions are unions of non-overlapping squares, and our FIXP-hardness result applies even when all mass distributions are unions of weighted squares and right-angled triangles.
Revised:  Our PPA-hardness results apply even when all mass distributions are unions of non-overlapping squares, and our FIXP-hardness result applies even when all mass distributions are unions of weighted squares and right-angled triangles.
Edit intent: fluency
Original:  It is shown that FPCC-Net outperforms SGPN by about 40\\%DIF < average precision (AP) and can process about 60,000 points in about 0.8[s]abstract %DIF > than SGPN and can process about 60,000 points in about 0.8 [s].
Revised:  It is shown that FPCC-Net improves average precision (AP) by about 40\\%DIF < average precision (AP) and can process about 60,000 points in about 0.8[s]abstract %DIF > than SGPN and can process about 60,000 points in about 0.8 [s].
Edit intent: clarity
Original:  Built upon a "perturbative" partitioning of the matrix into diagonal and off-diagonal parts, IPT has the complexity of one matrix-vector (when one eigenpair is requested) or one matrix-matrix multiplication (when all eigenpairs are requested) per iteration .
Revised:  Built upon a "perturbative" partitioning of the matrix into diagonal and off-diagonal parts, IPT computes one or all eigenpairs with a complexity per iteration of one matrix-vector (when one eigenpair is requested) or one matrix-matrix multiplication (when all eigenpairs are requested) per iteration .
Edit intent: clarity
Original:  Built upon a "perturbative" partitioning of the matrix into diagonal and off-diagonal parts, IPT has the complexity of one matrix-vector (when one eigenpair is requested) or one matrix-matrix multiplication (when all eigenpairs are requested) per iteration .
Revised:  Built upon a "perturbative" partitioning of the matrix into diagonal and off-diagonal parts, IPT has the complexity of one matrix-vector or one matrix-matrix multiplication (when all eigenpairs are requested) per iteration .
Edit intent: fluency
Original:  Built upon a "perturbative" partitioning of the matrix into diagonal and off-diagonal parts, IPT has the complexity of one matrix-vector (when one eigenpair is requested) or one matrix-matrix multiplication (when all eigenpairs are requested) per iteration .
Revised:  Built upon a "perturbative" partitioning of the matrix into diagonal and off-diagonal parts, IPT has the complexity of one matrix-vector (when one eigenpair is requested) or one matrix-matrix multiplication respectively .
Edit intent: clarity
Original:  Thanks to the high parallelism of these basic linear algebra operations, we obtain excellent performance on multi-core processors and GPUs, with large speed-ups over standard methods (up to \sim50x with respect to LAPACK and ARPACK ,%DIFDELCMD < \sim5x %%% with respect to Davidson).
Revised:  Thanks to the high parallelism of these basic linear algebra operations, we obtain excellent performance on multi-core processors and GPUs, with large speed-ups over standard methods (up to \sim50x with respect to LAPACK and ARPACK %DIFDELCMD < \sim5x %%% with respect to Davidson).
Edit intent: fluency
Original:  Thanks to the high parallelism of these basic linear algebra operations, we obtain excellent performance on multi-core processors and GPUs, with large speed-ups over standard methods (up to \sim50x with respect to LAPACK and ARPACK ,%DIFDELCMD < \sim5x %%% with respect to Davidson). When off-diagonal elements are comparable to eigengaps, IPT diverges like the quadratic map outside the Mandelbrot set; for such general matrices IPT can nevertheless be used to refine low-precision eigenvectors obtained by other methods.
Revised:  Thanks to the high parallelism of these basic linear algebra operations, we obtain excellent performance on multi-core processors and GPUs, with large speed-ups over standard methods (up to \sim50x with respect to LAPACK and ARPACK ,%DIFDELCMD < \sim5x %%% ). For matrices which are not close to being diagonal but have well-separated eigenvalues, IPT can be be used to refine low-precision eigenvectors obtained by other methods.
Edit intent: clarity
Original: Code summarization (CS) is becoming a promising area in recent natural language understanding, which aims to generate sensible annotations automatically for source code and is known as programmer oriented. Previous works attempt to apply structure-based traversal (SBT) or non-sequential models like Tree-LSTM and GNN to learn structural program semantics.
Revised: Code summarization (CS) is becoming a promising area in recent natural language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing. It is well known that programming languages are highly structured. Thus previous works attempt to apply structure-based traversal (SBT) or non-sequential models like Tree-LSTM and GNN to learn structural program semantics.
Edit intent: coherence
Original:  They both meet the following drawbacks: 1) it is shown ineffective to incorporate SBT into Transformer; 2) it is limited to capture global information through GNN ; 3) it is underestimated to capture structural semantics only using Transformer. In this paper , we propose a novel model based on structure-induced self-attention , which encodes sequential inputs with highly-effective structure modeling .
Revised:  However, it is surprising that incorporating SBT into advanced encoder like Transformer instead of LSTM has been shown no performance gain, which lets GNN become the only rest means modeling such necessary structural clue in source code. To release such inconvenience , we propose a novel model based on structure-induced self-attention , which encodes sequential inputs with highly-effective structure modeling .
Edit intent: coherence
Original:  Extensive experiments show that our newly-proposed model achieves new state-of-the-art results on popular benchmarks. To our best knowledge, it is the first work on code summarization that uses Transformer to model structural information with high efficiency and no extra parameters. We also provide a tutorial on how we pre-process .
Revised:  Extensive experiments show that our newly-proposed model achieves new state-of-the-art results on benchmarks .
Edit intent: clarity
Original:  Motivated by the recent finding that random negatives are often too trivial to train a reliable model , we propose a hierarchical curriculum learning (HCL) framework that consists of two complementary curricula: (1) corpus-level curriculum (CC);
Revised:  Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios , we propose a hierarchical curriculum learning (HCL) framework that consists of two complementary curricula: (1) corpus-level curriculum (CC);
Edit intent: clarity
Original:  Motivated by the recent finding that random negatives are often too trivial to train a reliable model , we propose a hierarchical curriculum learning (HCL) framework that consists of two complementary curricula: (1) corpus-level curriculum (CC);
Revised:  Motivated by the recent finding that random negatives are often too trivial to train a reliable model , we propose a hierarchical curriculum learning framework that trains the matching model in an "easy-to-difficult" scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC);
Edit intent: meaning-changed
Original:  In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and response. On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Revised:  In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Edit intent: clarity
Original:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Revised:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Edit intent: fluency
Original:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Revised:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and a response candidate . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Edit intent: clarity
Original:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Revised:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Edit intent: style
Original:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.
Revised:  On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response . Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.
Edit intent: clarity
Original: The availability of large-scale datasets has driven the development of neural sequence-to-sequence models to generate generic summaries , i. e., summaries which do not correspond to any pre-specified queries.
Revised: The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i. e., summaries which do not correspond to any pre-specified queries.
Edit intent: meaning-changed
Original: The availability of large-scale datasets has driven the development of neural sequence-to-sequence models to generate generic summaries , i. e., summaries which do not correspond to any pre-specified queries.However, due to the lack of training data, queryfocused summarization (QFS) has been studied mainly with extractive methods.In this work, we consider the problem of leveraging only generic summarization resources to build an abstractive QFS system. We propose Marge , a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries, and a distantly supervised training task for answer evidence estimation.
Revised: The availability of large-scale datasets has driven the development of neural sequence-to-sequence models to generate generic summaries , i. e., finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation). We introduce MaRGE , a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries, and a distantly supervised training task for answer evidence estimation.
Edit intent: coherence
Original:  We propose Marge , a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries, and a distantly supervised training task for answer evidence estimation. To further utilize generic data for generation, three attributes are incorporated during training and inference to control the shape of the final summary: evidence rank, query guidance, and summary length. Despite learning from minimal supervision, our system achieves state-of-the-art results in the distantly supervised setting across domains and query types  .
Revised:  We propose Marge , a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model. Experiments across QFS benchmarks and query types .
Edit intent: clarity
Original:  According our numerical experiments, the new method is more robust and faster than the traditional optimization method such as the trust-region method and the line search method  .
Revised:  According our numerical experiments, the new method is more robust and faster than the traditional optimization method such as the trust-region method and the line search method . The computational time of the new method is about one percent of that of the trust-region method (the subroutine fminunc.m of the MATLAB2019a environment, it is set by the trust-region method) or one fifth of that the line search method (fminunc.m is set by the quasi-Newton method) for the large-scale problem. Finally, the global convergence analysis of the new method is also given .
Edit intent: meaning-changed
Original:  Neural Information Retrieval (Neu-IR) models have shown their effectiveness and thrive from end-to-end training with massive high-quality relevance labels. Nevertheless, relevance labels at such quantity are luxury and unavailable in many ranking scenarios, for example, in biomedical search. This paper improves Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision.
Revised:  Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision.
Edit intent: clarity
Original:  This paper improves Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision.
Revised:  This paper improves Neu-IR , this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision.
Edit intent: meaning-changed