wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Revised:  We define the l.u.b. ( least upper bound ) and theg.l.b. and the greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Edit intent: clarity
Original:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Revised:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Edit intent: fluency
Original:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random matchings has two dual lattices.
Revised:  We define the l.u.b. ( least upper bound ) and theg.l.b. ( greatest lower bound ) for both sides of the matching market, and we prove the that with these binary operations the set of random stable matchings forms two dual lattices.
Edit intent: clarity
Original:  We define the least upper bound and the greatest lower bound for both sides of the matching market , and we prove that with these binary operations the set of random stable matchings forms two dual lattices.
Revised:  We define a partial order on the random stable set and present two intuitive binary operations to compute the least upper bound and the greatest lower bound for both sides of the matching market , and we prove that with these binary operations the set of random stable matchings forms two dual lattices.
Edit intent: meaning-changed
Original: Sentiment Analysis and Emotion Detection in conversation is key in a number of real-world applications, with different applications leveraging different kinds of data to be able to achieve reasonably accurate predictions. Multimodal Emotion Detection and Sentiment Analysis can be particularly useful  as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture  the context of the conversation through all modalities, the current speaker and listener(s)
Revised: Sentiment Analysis and Emotion Detection in conversation is key in a number of real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions. Multi-modal Emotion Detection and Sentiment Analysis can be particularly useful as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture the context of the conversation through all modalities, the current speaker and listener(s)
Edit intent: clarity
Original:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful  as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture  the context of the conversation through all modalities, the current speaker and listener(s)
Revised:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful , as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture the context of the conversation through all modalities, the current speaker and listener(s)
Edit intent: fluency
Original:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful  as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture  the context of the conversation through all modalities, the current speaker and listener(s)
Revised:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful as applications will be able to use specific subsets of available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture the context of the conversation through all modalities, the current speaker and listener(s)
Edit intent: fluency
Original:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful  as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture  the context of the conversation through all modalities, the current speaker and listener(s)
Revised:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful as applications will be able to use specific subsets of the available modalities, as per the available data . Current systems dealing with Multimodal functionality fail to leverage and capture the context of the conversation through all modalities, the current speaker and listener(s)
Edit intent: coherence
Original:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful  as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture  the context of the conversation through all modalities, the current speaker and listener(s)
Revised:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multi-modal functionality fail to leverage and capture the context of the conversation through all modalities, the current speaker and listener(s)
Edit intent: fluency
Original:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful  as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture  the context of the conversation through all modalities, the current speaker and listener(s)
Revised:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture - the context of the conversation through all modalities, the current speaker and listener(s)
Edit intent: fluency
Original:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful  as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture  the context of the conversation through all modalities, the current speaker and listener(s)
Revised:  Multimodal Emotion Detection and Sentiment Analysis can be particularly useful as applications will be able to use specific subsets of the available modalities, as per their available data, to be able to produce relevant predictions . Current systems dealing with Multimodal functionality fail to leverage and capture the context of the conversation through all modalities, the dependency between the listener(s)
Edit intent: clarity
Original:  In this paper, we propose a recurrent neural network architecture that attempts to take into account all the mentioned drawbacks , and keeps track of the context of the conversation, interlocutor states, and the emotions conveyed by the speakers in the conversation .
Revised:  In this paper, we propose an end to end RNN architecture that attempts to take into account all the mentioned drawbacks , and keeps track of the context of the conversation, interlocutor states, and the emotions conveyed by the speakers in the conversation .
Edit intent: clarity
Original:  We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.abstract 
Revised:  We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.abstract \%) do not affect pre-training loss or transfer to downstream tasks at all. Medium levels of pruning increase the pre-training loss and prevent useful pre-training information from being transferred to downstream tasks. High levels of pruning additionally prevent models from fitting downstream datasets, leading to further degradation. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.
Edit intent: meaning-changed
Original:  We extend this model to the class of mixed unit interval graphs . The original bubble model was used by Boyaci, Ekim, and Shalom for proving the polynomiality of the MaxCut problem on unit interval graphs. However, we found a significant mistake in the proof which seems to be hardly repairable. Moreover, we demonstrate the advantages of such a model by providing a subexponential-time algorithm  solving the MaxCut problem on mixed unit interval graphs using our extended version of the bubble model .
Revised:  We extend this model to the class of mixed unit interval graphs and demonstrate the advantages of such a model by providing a subexponential-time algorithm solving the MaxCut problem on mixed unit interval graphs using our extended version of the bubble model .
Edit intent: coherence
Original:  A general model is set up where many of the terms affecting parallel processing are listed and the validity of neglecting certain terms in different approximations are scrutinized , with special emphasize on the famous scaling laws of parallel processing.
Revised:  We set up a general model and list many of the terms affecting parallel processing are listed and the validity of neglecting certain terms in different approximations are scrutinized , with special emphasize on the famous scaling laws of parallel processing.
Edit intent: coherence
Original:  A general model is set up where many of the terms affecting parallel processing are listed and the validity of neglecting certain terms in different approximations are scrutinized , with special emphasize on the famous scaling laws of parallel processing.
Revised:  A general model is set up where many of the terms affecting parallel processing . We scrutinize the validity of neglecting certain terms in different approximations are scrutinized , with special emphasize on the famous scaling laws of parallel processing.
Edit intent: coherence
Original:  A general model is set up where many of the terms affecting parallel processing are listed and the validity of neglecting certain terms in different approximations are scrutinized , with special emphasize on the famous scaling laws of parallel processing.
Revised:  A general model is set up where many of the terms affecting parallel processing are listed and the validity of neglecting certain terms in different approximations , with special emphasize on the famous scaling laws of parallel processing.
Edit intent: clarity
Original:  A general model is set up where many of the terms affecting parallel processing are listed and the validity of neglecting certain terms in different approximations are scrutinized , with special emphasize on the famous scaling laws of parallel processing.
Revised:  A general model is set up where many of the terms affecting parallel processing are listed and the validity of neglecting certain terms in different approximations are scrutinized , with special emphasis on the famous scaling laws of parallel processing.
Edit intent: fluency
Original:  Amdahl's Law describes among others the history of supercomputing, the inherent performance limitation of the different kinds of parallel processing and it is the basic Law of the 'modern computing' paradigm, that is desperately needed for the computing systems working under extreme computing conditions  .
Revised:  Amdahl's Law describes among others the history of supercomputing, the inherent performance limitation of the different kinds of parallel processing and it is the basic Law of the 'modern computing' paradigm, that the computing systems working under extreme computing conditions .
Edit intent: clarity
Original:  Amdahl's Law describes among others the history of supercomputing, the inherent performance limitation of the different kinds of parallel processing and it is the basic Law of the 'modern computing' paradigm, that is desperately needed for the computing systems working under extreme computing conditions  .
Revised:  Amdahl's Law describes among others the history of supercomputing, the inherent performance limitation of the different kinds of parallel processing and it is the basic Law of the 'modern computing' paradigm, that is desperately needed for the computing systems working under extreme computing conditions are desperately needed .
Edit intent: clarity
Original:  In this paper, we investigate  visual question answering (VQA) through the lens of logical transformation and posit that systems that seek to answer questions about images must be robust to these transformations of the question.
Revised:  In this paper, we investigate whether visual question answering (VQA) through the lens of logical transformation and posit that systems that seek to answer questions about images must be robust to these transformations of the question.
Edit intent: meaning-changed
Original:  In this paper, we investigate  visual question answering (VQA) through the lens of logical transformation and posit that systems that seek to answer questions about images must be robust to these transformations of the question. If a VQA system is able to answer a question , it should also be able to answer the logical composition of questions.
Revised:  In this paper, we investigate visual question answering (VQA) systems trained to answer a question about an image, are able to answer the logical composition of questions.
Edit intent: clarity
Original:  If a VQA system is able to answer a question , it should also be able to answer the logical composition of questions. We analyze the performance of state-of-the-art models on the VQA task under these logical operations and show that they have difficulty in correctly answering such questions.
Revised:  If a VQA system is able to answer a question , it should also be able to answer the logical composition of multiple such questions. When put under this state-of-the-art models on the VQA task under these logical operations and show that they have difficulty in correctly answering such questions.
Edit intent: meaning-changed
Original:  We then construct an augmentation of the VQA dataset  with questions containing logical operations and retrain the same models to establish a baseline. We further propose a novel methodology to train models to learn negation, conjunction, and disjunction and show }  improvement in learning logical composition and retaining performance on VQA.
Revised:  We then construct an augmentation of the VQA dataset with questions containing logical compositions and linguistic transformations (negation, disjunction, conjunction, and antonyms). We propose our } improvement in learning logical composition and retaining performance on VQA.
Edit intent: meaning-changed
Original:  We further propose a novel methodology to train models to learn negation, conjunction, and disjunction and show }  improvement in learning logical composition and retaining performance on VQA.
Revised:  We further propose a novel methodology to train models to learn negation, conjunction, and disjunction and show } model which uses question-attention and logic-attention to understand logical connectives in the question, and a novel Fr\'echet-Compatibility Loss, which ensures that the answers of the component questions and the composed question are consistent with the inferred logical operation. Our model shows substantial improvement in learning logical composition and retaining performance on VQA.
Edit intent: meaning-changed
Original: Fontenele and collaborators \mbox{%DIFAUXCMD fontenele reported extensive analyses of power-law scalings of neuronal avalanches from experimental recordings and from numerical simulations of a model.
Revised: Fontenele and collaborators \mbox{%DIFAUXCMD fontenele reported extensive analyses of power-law scalings of neuronal avalanches in experimental recordings and from numerical simulations of a model.
Edit intent: clarity
Original: Fontenele and collaborators \mbox{%DIFAUXCMD fontenele reported extensive analyses of power-law scalings of neuronal avalanches from experimental recordings and from numerical simulations of a model.
Revised: Fontenele and collaborators \mbox{%DIFAUXCMD fontenele reported extensive analyses of power-law scalings of neuronal avalanches from experimental recordings and in a critical theoretical model.
Edit intent: clarity
Original:  We show here that two non-critical systems exhibit similar behaviors as those used to conclude on criticality, and thus, the experimental observations provided in \mbox{%DIFAUXCMD fontenele .
Revised:  To prove this point, they developed a test (hereafter, "Fontenele test") for criticality, which they indicate to rely on our own theoretical result. However, the results we established do not provide necessary and sufficient conditions for criticality, and thus, the experimental observations provided in \mbox{%DIFAUXCMD fontenele .
Edit intent: meaning-changed
Original:  We show here that two non-critical systems exhibit similar behaviors as those used to conclude on criticality, and thus, the experimental observations provided in \mbox{%DIFAUXCMD fontenele .
Revised:  We show here that two non-critical systems exhibit similar behaviors as those used to conclude on criticality, and cannot be extrapolated to derive a test for criticality. Moreover, the authors did not include any control applying the Fontenele test to non-critical systems, to assess possible differences with the experimental data. We propose here two such controls, and show that the Fontenele test can misclassify as critical these non-critical systems. This implies that the analyses of Fontenele et al. do not support their conclusions, and the question of whether the brain operates at criticality remains open .
Edit intent: meaning-changed
Original:  One of their main conclusions is that the brain operates at criticality in specific regimes. To prove this point, they developed a test (hereafter, "Fontenele test") for criticality, which they indicate to rely on our own theoretical result. However, the results we established do not provide necessary and sufficient conditions for criticality , and cannot be extrapolated to derive a test for criticality. Moreover, the authors did not include any control applying the Fontenele test to non-critical systems , to assess possible differences with the experimental data. We propose here two such controls, and show that the Fontenele test can misclassify as critical these non-critical systems .
Revised:  One of their main conclusions is that the brain operates at a critical point, a processus known as URLanized criticality. A recent paper found remarkable scalings suggestive of criticality in systems as different as neural cultures, anesthetized or awake brains. We point out here that the diversity of these states would question any claimed role of criticality in information processing. Furthermore, we show that two non-critical systems .
Edit intent: meaning-changed
Original:  We propose here two such controls, and show that the Fontenele test can misclassify as critical these non-critical systems . This implies that the analyses of Fontenele et al. do not support their conclusions, and the question of whether the brain operates at criticality remains open .
Revised:  We propose here two such controls, and show that the Fontenele test can misclassify as critical these non-critical systems pass all the tests for criticality, a control that was not provided in the original article. We conclude that such false positives demonstrate that the presence of criticality in the brain is still not proven and that we need better methods that scaling analyses .
Edit intent: meaning-changed
Original: We show the existence of an exact mimicking network of k^{O(\log k)} edges for minimum multicuts over a set of terminals in an undirected graph, where k is the total capacity of the terminals . Furthermore, if Small Set Expansion has an approximation algorithm with a ratio slightly better than \Theta(\log n), then a mimicking network of quasipolynomial size can be computed in polynomial time.
Revised: We show the existence of an exact mimicking network of k^{O(\log k)} edges for minimum multicuts over a set of terminals in an undirected graph, where k is the total capacity of the terminals , as well as a method for computing a mimicking network of quasipolynomial size can be computed in polynomial time.
Edit intent: coherence
Original:  As a consequence of the latter, several problems would have quasipolynomial kernels, including Edge Multiway Cut, Group Feedback Edge Set for an arbitrary group, 0-Extension for integer-weighted metrics, and Edge Multicut parameterized by the solution and the number of cut requests.
Revised:  As a consequence of the latter, several problems are shown to have quasipolynomial kernels, including Edge Multiway Cut, Group Feedback Edge Set for an arbitrary group, 0-Extension for integer-weighted metrics, and Edge Multicut parameterized by the solution and the number of cut requests.
Edit intent: clarity
Original:  As a consequence of the latter, several problems would have quasipolynomial kernels, including Edge Multiway Cut, Group Feedback Edge Set for an arbitrary group, 0-Extension for integer-weighted metrics, and Edge Multicut parameterized by the solution and the number of cut requests.
Revised:  As a consequence of the latter, several problems would have quasipolynomial kernels, including Edge Multiway Cut, Group Feedback Edge Set for an arbitrary group, and Edge Multicut parameterized by the solution and the number of cut requests.
Edit intent: clarity
Original:  The result works via a combination of the matroid-based irrelevant edge approach used in the kernel for s-Multiway Cut with a recursive decomposition and sparsification of the graph along sparse cuts. The main technical contribution is a matroid-based marking procedure that we can show will mark all non-irrelevant edges, assuming that the graph is sufficiently densely connected. The only part of the result that is not currently constructive and polynomial-time computable is the detection of such sparse cuts.
Revised:  The result works via a combination of the matroid-based irrelevant edge approach used in the kernel for s-Multiway Cut with a recursive decomposition and sparsification of the graph along sparse cuts.
Edit intent: coherence