wanyu/IteraTeR_v2 huggingface dataset:
Original:  Given such a graph, an angle cover maps every vertex to a pair of consecutive edges in the ordering -- an angle -- such that each edge participates in at least one such pair.
Revised:  Given such a graph, an angle cover maps every vertex to a pair of consecutive edges in the ordering -- an angle -- such that each edge participates in at least one such pair.
Edit intent: fluency
Original:  To do so, we developed a novel representation of the internal state of a prover in terms of clauses and inference actions , and a novel neural-based attention mechanism to learn interactions between clauses.
Revised:  To do so, we developed a novel representation of the internal state of a prover in terms of clauses and inference actions . We also propose a novel neural-based attention mechanism to learn interactions between clauses.
Edit intent: coherence
Original:  To do so, we developed anovel representation of the internal state of a prover in terms of clauses and inference actions.
Revised:  To do so, we developed anovel representation of the state of a prover in terms of clauses and inference actions.
Edit intent: clarity
Original: In this paper, we present two approaches for designing geometrically symmetric quadrature rules to address the logarithmic singularities arising in the method of moments from the Green's function in integrals over the test domain  .
Revised: In this paper, we characterize the logarithmic singularities arising in the method of moments from the Green's function in integrals over the test domain .
Edit intent: clarity
Original:  We establish parametrized sample complexity bounds for learning an up-to-\varepsilon optimal mechanism in both models, which scale polynomially in the size of the model, i.e.  the number of items and bidders, and only exponential in the natural complexity measure of the model, namely either the largest in-degree (for Bayesian Networks) or the size of the largest hyper-edge (for Markov Random Fields).
Revised:  We establish parametrized sample complexity bounds for learning an up-to-\varepsilon optimal mechanism in both models, which scale polynomially in the size of the model, i.e. ~ the number of items and bidders, and only exponential in the natural complexity measure of the model, namely either the largest in-degree (for Bayesian Networks) or the size of the largest hyper-edge (for Markov Random Fields).
Edit intent: fluency
Original:  We show that, given only "approximate distributions" for bidder valuations, we can learn a mechanism whose revenue is nearly optimal simultaneously for all "true distributions" that are close to the ones we were given in Prokhorov distance.
Revised:  We show that, given only ``approximate distributions'' for bidder valuations, we can learn a mechanism whose revenue is nearly optimal simultaneously for all "true distributions" that are close to the ones we were given in Prokhorov distance.
Edit intent: fluency
Original:  We show that, given only "approximate distributions" for bidder valuations, we can learn a mechanism whose revenue is nearly optimal simultaneously for all "true distributions" that are close to the ones we were given in Prokhorov distance.
Revised:  We show that, given only "approximate distributions" for bidder valuations, we can learn a mechanism whose revenue is nearly optimal simultaneously for all ``true distributions'' that are close to the ones we were given in Prokhorov distance.
Edit intent: fluency
Original:  When item values are independent, learning in Prokhorov distance is immediate, hence our framework directly implies the main result of Gonczarowski and Weinberg \mbox{ GonczarowskiW18 . When item values are sampled from more general graphical models, we combine our robustness theorem with novel sample complexity results for learning Markov Random Fields or Bayesian Networks in Prokhorov distance, which may be of independent interest.
Revised:  When item values are independent, learning in Prokhorov distance is immediate, hence our framework directly implies the main result of Gonczarowski and Weinberg . When item values are sampled from more general graphical models, we combine our robustness theorem with novel sample complexity results for learning Markov Random Fields or Bayesian Networks in Prokhorov distance, which may be of independent interest.
Edit intent: coherence
Original: Summarization of opinions is the process of automatically creating text summaries that reflect subjective information expressed in input documents, such as product reviews.
Revised: Opinion summarization is the task of automatically creating text summaries that reflect subjective information expressed in input documents, such as product reviews.
Edit intent: clarity
Original: Summarization of opinions is the process of automatically creating text summaries that reflect subjective information expressed in input documents, such as product reviews.
Revised: Summarization of opinions is the process of automatically creating summaries that reflect subjective information expressed in input documents, such as product reviews.
Edit intent: clarity
Original: Summarization of opinions is the process of automatically creating text summaries that reflect subjective information expressed in input documents, such as product reviews.
Revised: Summarization of opinions is the process of automatically creating text summaries that reflect subjective information expressed in multiple documents, such as product reviews.
Edit intent: clarity
Original:  While most previous research in opinion summarization has focused on the extractive setting, i.e. selecting fragments of the input documents to produce a summary, we let the model generate novel sentences and hence produce fluent text.
Revised:  While the majority of previous work has focused on the extractive setting, i.e. selecting fragments of the input documents to produce a summary, we let the model generate novel sentences and hence produce fluent text.
Edit intent: clarity
Original:  While most previous research in opinion summarization has focused on the extractive setting, i.e. selecting fragments of the input documents to produce a summary, we let the model generate novel sentences and hence produce fluent text.
Revised:  While most previous research in opinion summarization has focused on the extractive setting, i.e. , selecting fragments from input reviews to produce a summary, we let the model generate novel sentences and hence produce fluent text.
Edit intent: clarity
Original:  Supervised abstractive summarization methods typically rely on large quantities of document-summary pairs which are expensive to acquire .
Revised:  Supervised abstractive summarization methods typically rely on large quantities of document-summary pairs . Since such training data is expensive to acquire .
Edit intent: clarity
Original:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Revised:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of a product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Edit intent: fluency
Original:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Revised:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we should be able to control the " amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Edit intent: clarity
Original:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Revised:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty " going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Edit intent: fluency
Original:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Revised:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the extent to which it deviates from the input reviews .
Edit intent: clarity
Original:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input reviews .
Revised:  Intuitively, we want to design such a model that , when generating a new review given a set of other reviews of the product, we can control the ` amount of novelty ' going into the new review or, equivalently, vary the degree of deviation from the input .
Edit intent: fluency
Original:  Both individual reviews and  products they correspond to are associated with stochastic latent codes, and the review generator ( 'decoder' ) has direct access to the text of input reviews through the pointer-generator mechanism.
Revised:  Both individual reviews and the products they correspond to are associated with stochastic latent codes, and the review generator ( 'decoder' ) has direct access to the text of input reviews through the pointer-generator mechanism.
Edit intent: fluency
Original:  Both individual reviews and  products they correspond to are associated with stochastic latent codes, and the review generator ( 'decoder' ) has direct access to the text of input reviews through the pointer-generator mechanism.
Revised:  Both individual reviews and products they correspond to are associated with stochastic latent codes, and the review generator ( "decoder" ) has direct access to the text of input reviews through the pointer-generator mechanism.
Edit intent: fluency
Original:  In experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, we produce fluent and coherent summaries  .
Revised:  Experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, we produce fluent and coherent summaries .
Edit intent: fluency
Original:  In experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, we produce fluent and coherent summaries  .
Revised:  In experiments on Amazon and Yelp datasets, show that setting at test time the review's latent code to its mean, we produce fluent and coherent summaries .
Edit intent: clarity
Original:  In experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, we produce fluent and coherent summaries  .
Revised:  In experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, allows the model to produce fluent and coherent summaries .
Edit intent: clarity
Original: It has been widely regarded that only considering the immediate user feedback is not sufficient for modern industrial recommendation systems .
Revised: In recommender systems such as news feed stream, it is essential to optimize the long-term utilities in the continuous user-system interaction processes. Previous works have proved the capability of reinforcement learning in this problem .
Edit intent: coherence
Original:  Many previous works attempt to maximize the long term rewards with Reinforcement Learning(RL).
Edit intent: coherence
Original:  To achieve high sample efficiency in practical situations , we propose a novel model-based reinforcement learning method, namely the model-based counterfactual advantage learning (MBCAL).
Revised:  To achieve high sample efficiency in practical situations , we propose a novel reinforcement learning method, namely the model-based counterfactual advantage learning (MBCAL).
Edit intent: fluency
Original:  To achieve high sample efficiency in practical situations , we propose a novel model-based reinforcement learning method, namely the model-based counterfactual advantage learning (MBCAL).
Revised:  To achieve high sample efficiency in practical situations , we propose a novel model-based reinforcement learning method, namely model-based counterfactual advantage learning (MBCAL).
Edit intent: fluency
Original:  In the experiments, we compare our methods with several baselines, including supervised learning, model-free RL, and other model-based RL methods in carefully designed experiments . Results show that our method transcends all the baselines in both sample efficiency and asymptotic performance .
Revised:  In the experiments, we compare our methods with several baselines, including supervised learning, model-free RL, and other model-based RL methods in carefully designed experiments . Results show that our method transcends all the baselines in both sample efficiency and asymptotic performances .
Edit intent: fluency
Original:  The best known approximation factor for this NP-hard problem is 2, and this can be achieved with multiple approaches (the first such result is in [Frederickson and Jaja '81]).
Revised:  The best known approximation factor for this NP-hard problem is 2, and this can be achieved with multiple approaches (the first such result is in [Frederickson and J\'aj\'a '81]).
Edit intent: fluency
Original:  In this paper we breach the 2 approximation barrier for CacAP, hence for CAP, by presenting a polynomial-time 2 ln(4) - 967 +\epsilon<1.91 approximation.
Revised:  In this paper we breach the 2 approximation barrier for CacAP, hence for CAP, by presenting a polynomial-time 2 \ln(4) - 967 +\epsilon<1.91 approximation.
Edit intent: fluency
Original:  In this paper we breach the 2 approximation barrier for CacAP, hence for CAP, by presenting a polynomial-time 2 ln(4) - 967 +\epsilon<1.91 approximation.
Revised:  In this paper we breach the 2 approximation barrier for CacAP, hence for CAP, by presenting a polynomial-time 2 ln(4) - \frac{967 +\epsilon<1.91 approximation.
Edit intent: fluency
Original:  To achieve the latter goal, we " open the box " and exploit the specific properties of the instances of Steiner tree arising from CacAP.
Revised:  To achieve the latter goal, we `` open the box " and exploit the specific properties of the instances of Steiner tree arising from CacAP.
Edit intent: fluency
Original:  To achieve the latter goal, we " open the box " and exploit the specific properties of the instances of Steiner tree arising from CacAP.
Revised:  To achieve the latter goal, we " open the box '' and exploit the specific properties of the instances of Steiner tree arising from CacAP.
Edit intent: fluency
Original: We introduce translations between display calculus proofs and labelled calculus proofs in the context of tense logics.
Revised: We introduce translations between display calculus proofs and labeled calculus proofs in the context of tense logics.
Edit intent: fluency
Original:  First, we show that every derivation in the display calculus for the minimal tense logic Kt extended with general path axioms can be effectively transformed into a derivation in the corresponding labelled calculus.
Revised:  First, we show that every derivation in the display calculus for the minimal tense logic Kt extended with general path axioms can be effectively transformed into a derivation in the corresponding labeled calculus.
Edit intent: fluency
Original:  Concerning the converse translation, we show that for Kt extended with path axioms, every derivation in the corresponding labelled calculus can be put into a special form that is easily translatable to a derivation in the display calculus .
Revised:  Concerning the converse translation, we show that for Kt extended with path axioms, every derivation in the corresponding labeled calculus can be put into a special form that is easily translatable to a derivation in the display calculus .
Edit intent: fluency
Original:  Concerning the converse translation, we show that for Kt extended with path axioms, every derivation in the corresponding labelled calculus can be put into a special form that is easily translatable to a derivation in the display calculus .
Revised:  Concerning the converse translation, we show that for Kt extended with path axioms, every derivation in the corresponding labelled calculus can be put into a special form that is translatable to a derivation in the display calculus .
Edit intent: clarity
Original:  I propose a potentially novel theory of humor as the feeling of Rapid Anxiety Reduction (RAR).
Revised: Here I propose a potentially novel theory of humor as the feeling of Rapid Anxiety Reduction (RAR).
Edit intent: coherence
Original:  I propose a potentially novel theory of humor as the feeling of Rapid Anxiety Reduction (RAR).
Revised:  I propose a novel theory in which humor is the feeling of Rapid Anxiety Reduction (RAR).
Edit intent: clarity
Original:  By providing this cypher, RAR makes humor into a potentially invaluable window for understanding persons in both individual and social contexts, with far-reaching implications.
Revised:  In this way, humor is a powerful cypher for understanding persons in both individual and social contexts, with far-reaching implications.
Edit intent: clarity
Original:  Finally, if humor is such an essential aspect of the human experience, and if humor can be expressed in such a simple formula which has remained undiscovered for this long, then this is an extremely surprising state of affairs worthy of further investigation.
Revised:  Finally, if humor can be expressed in such a simple formula which has remained undiscovered for this long, then this is an extremely surprising state of affairs worthy of further investigation.
Edit intent: coherence
Original:  In this model  we prove that the expected complexity of the k-level is of order \Theta((k+1)^{d-1}).
Revised:  In this model , we prove that the expected complexity of the k-level is of order \Theta((k+1)^{d-1}).
Edit intent: fluency
Original: Research on content and style representations has been widely studied in the field of style transfer.
Revised: Content and style representations has been widely studied in the field of style transfer.
Edit intent: clarity
Original: Research on content and style representations has been widely studied in the field of style transfer.
Revised: Research on content and style representations have been widely studied in the field of style transfer.
Edit intent: fluency
Original:  In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it a speaker representation loss (SRL) . The objective is to extract the 'sound of my voice ' from the noisy input and also remove it from the residual components.
Revised:  In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it speaker representation loss (SRL) . The objective is to extract the 'sound of my voice ' from the noisy input and also remove it from the residual components.
Edit intent: fluency
Original:  In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it a speaker representation loss (SRL) . The objective is to extract the 'sound of my voice ' from the noisy input and also remove it from the residual components.
Revised:  In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it a speaker representation loss . The objective is to extract the 'sound of my voice ' from the noisy input and also remove it from the residual components.
Edit intent: clarity
Original:  In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it a speaker representation loss (SRL) . The objective is to extract the 'sound of my voice ' from the noisy input and also remove it from the residual components.
Revised:  In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it a speaker representation loss (SRL) . The objective is to extract the target speaker voice from the noisy input and also remove it from the residual components.
Edit intent: clarity
Original:  Compared to the conventional spectral reconstruction, our proposed framework maximizes the use of target speaker information by minimizing the distance between the content of target speaker and source separation output.
Revised:  Compared to the conventional spectral reconstruction, our proposed framework maximizes the use of target speaker information by minimizing the distance between the speaker representations of reference and source separation output.
Edit intent: clarity
Original:  We also propose triplet SRL as an additional criterion to remove the target speaker information from residual spectrogram output.
Revised:  We also propose triplet speaker representation loss as an additional criterion to remove the target speaker information from residual spectrogram output.
Edit intent: clarity
Original:  and (2) CVP_p with preprocessing , in which we are allowed arbitrary advice about the lattice (assuming a non-uniform version of SETH).
Revised:  and (2) CVP_p with preprocessing (assuming non-uniform version of SETH).
Edit intent: clarity
Original:  and (2) CVP_p with preprocessing , in which we are allowed arbitrary advice about the lattice (assuming a non-uniform version of SETH).
Revised:  and (2) CVP_p with preprocessing , in which we are allowed arbitrary advice about the lattice (assuming a non-uniform SETH).
Edit intent: clarity
Original:  The collapse on the master-curve is not perfect however, suggesting that for a more realistic modelling specific socio-economic characteristics have to be taken also into account .
Revised:  The collapse on the master-curve is not perfect however, suggesting that for a more realistic modelling the model parameters have to be taken also into account .
Edit intent: clarity
Original:  The collapse on the master-curve is not perfect however, suggesting that for a more realistic modelling specific socio-economic characteristics have to be taken also into account .
Revised:  The collapse on the master-curve is not perfect however, suggesting that for a more realistic modelling specific socio-economic characteristics have to be linked with specific socio-economic regulations .
Edit intent: clarity
Original: We propose a framework which makes a model predict fine-grained dimensional emotions (valence-arousal-dominance, VAD) trained on corpus annotated with coarse-grained categorical emotions.
Revised: We present a model to predict fine-grained dimensional emotions (valence-arousal-dominance, VAD) trained on corpus annotated with coarse-grained categorical emotions.
Edit intent: clarity
Original:  We train a model by minimizing EMD distances between predicted VAD score distribution and sorted categorical emotion distributions in terms of VAD, as a proxy of target VAD score distributions.
Revised:  We train a model by minimizing EMD distances between predicted VAD score distribution and the categorical emotion distributions in terms of VAD, as a proxy of target VAD score distributions.
Edit intent: clarity