wanyu/IteraTeR_full_sent huggingface dataset:
Original:  MaxQP admits an \Omega(1/\lg a_{\max})-approximation in O(n^{1.5} \lg a_{\max}) time, where a_{\max} is the maximum absolute value in A, when the corresponding graph is d-degenerate. - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph has bounded local treewidth . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor-free .
Revised:  MaxQP admits an \Omega(1/\lg a_{\max})-approximation in O(n^{1.5} \lg a_{\max}) time, where a_{\max} is the maximum absolute value in A, when the corresponding graph is d-degenerate. - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph has bounded local treewidth . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph is H-minor-free .
Edit intent: meaning-changed
Original:  MaxQP admits an \Omega(1/\lg a_{\max})-approximation in O(n^{1.5} \lg a_{\max}) time, where a_{\max} is the maximum absolute value in A, when the corresponding graph is d-degenerate. - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph has bounded local treewidth . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor-free .
Revised:  MaxQP admits an \Omega(1/\lg a_{\max})-approximation in O(n^{1.5} \lg a_{\max}) time, where a_{\max} is the maximum absolute value in A, when the corresponding graph is d-degenerate. - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph has bounded local treewidth . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: clarity
Original:   UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. -
Revised:  MaxQP admits a (1/2\Delta)-approximation in O(n \lg n) time, where \Delta is the maximum degree of the corresponding graph. - UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. -
Edit intent: meaning-changed
Original:   UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. -
Revised:  UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits a (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. -
Edit intent: fluency
Original:   UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. -
Revised:  UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 2d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. -
Edit intent: meaning-changed
Original:   UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. -
Revised:  UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ) time when the corresponding graph has no isolated vertices and at most dn edges. -
Edit intent: clarity
Original:   UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph has no isolated vertices and at most dn edges. - MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  UnitMaxQP, where A \in \{-1,0,1\}^{n\times n}, admits an (1/ 3d )-approximation in O(n ^{1.5 when the corresponding graph is d-degenerate, and a (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: coherence
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ 3\delta )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: meaning-changed
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} ) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: fluency
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } ) time when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: fluency
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph has \delta n edges . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: meaning-changed
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - MaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: meaning-changed
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: clarity
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph and each of its minors have bounded local treewidth . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: clarity
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: fluency
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph and each of its minors have bounded local treewidth .
Edit intent: fluency
Original:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, }  when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n  ) time when the corresponding graph and each of its minors have bounded local treewidth .
Revised:  MaxQP admits an \Omega (1/ \lg a_{\max )-approximation in O(n^{1.5} \lg a_{\max) time , where a_{\max} is the maximum absolute value in A, } when the corresponding graph is d-degenerate . - UnitMaxQP admits a (1-\varepsilon)-approximation in O(n ^2 ) time when the corresponding graph is H-minor free . - MaxQP admits a (1-\varepsilon)-approximation in O(n ) time when the corresponding graph is H-minor free .
Edit intent: meaning-changed
Original: The task of Event Detection ( ED) is a subfield of Information Extraction (IE) that consists in recognizing event mentions in natural language texts. Several applications can take advantage of an ED system, including alert systems, text summarization, question-answering systems, and any system that needs to extract structured information about  events from unstructured texts . ED is a complex task, which is hampered by two main challenges: the lack of a dataset large enough to train and test the developed models and the variety of event type definitions that exist in the literature.
Revised: The task of Event Detection ( OED) task, which is a specific Event Detection task where the goal is to detect ongoing event mentions only, as opposed to historical, future, hypothetical, or other forms or events that are neither fresh nor current. Any application that needs to extract structured information about events from unstructured texts . ED is a complex task, which is hampered by two main challenges: the lack of a dataset large enough to train and test the developed models and the variety of event type definitions that exist in the literature.
Edit intent: meaning-changed
Original:  Several applications can take advantage of an ED system, including alert systems, text summarization, question-answering systems, and any system that needs to extract structured information about  events from unstructured texts . ED is a complex task, which is hampered by two main challenges: the lack of a dataset large enough to train and test the developed models and the variety of event type definitions that exist in the literature.
Revised:  Several applications can take advantage of an ED system, including alert systems, text summarization, question-answering systems, and any system that needs to extract structured information about ongoing events from unstructured texts . ED is a complex task, which is hampered by two main challenges: the lack of a dataset large enough to train and test the developed models and the variety of event type definitions that exist in the literature.
Edit intent: clarity
Original:  Several applications can take advantage of an ED system, including alert systems, text summarization, question-answering systems, and any system that needs to extract structured information about  events from unstructured texts . ED is a complex task, which is hampered by two main challenges: the lack of a dataset large enough to train and test the developed models and the variety of event type definitions that exist in the literature. These problems make generalization hard to achieve, resulting in poor adaptation to different domains and targets.
Revised:  Several applications can take advantage of an ED system, including alert systems, text summarization, question-answering systems, and any system that needs to extract structured information about events from unstructured texts can take advantage of an OED system.
Edit intent: clarity
Original:  The main contribution of this paper is the design, implementation and evaluation of a recurrent neural network model for ED that combines several features. In particular, the paper makes the followingcontributions : (1) it  uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for the ED task;
Revised:  The main contribution of this paper are the following : (1) it uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for the ED task;
Edit intent: coherence
Original:  In particular, the paper makes the followingcontributions : (1) it  uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for the ED task;
Revised:  In particular, the paper makes the followingcontributions : (1) it introduces the OED task along with a dataset manually labeled for the task; (2) it presents the design and implementation of an RNN model for the task that uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for the ED task;
Edit intent: meaning-changed
Original:  In particular, the paper makes the followingcontributions : (1) it  uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for the ED task; (2) the proposed model has the ability to use its first layer to learn good feature representations; ( 3) a new public dataset with a general definition of event;
Revised:  In particular, the paper makes the followingcontributions : (1) it uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for detecting ongoing events in news; ( 3) a new public dataset with a general definition of event;
Edit intent: meaning-changed
Original:  ( 3) a new public dataset with a general definition of event; (4) an extensive empirical evaluation that includes (i) the exploration of different architectures and hyperparameters, (ii) an ablation test to study the impact of each attribute, and (iii) a comparison with a replication of a state-of-the-art model.
Revised:  ( 3) it presents an extensive empirical evaluation that includes (i) the exploration of different architectures and hyperparameters, (ii) an ablation test to study the impact of each attribute, and (iii) a comparison with a replication of a state-of-the-art model.
Edit intent: clarity
Original:  Furthermore, we explore the relations existing between mathematical and financial properties attributable to an acceptance set on the one hand , and the corresponding properties of the induced measure on the other . Dual characterizations in terms of polar sets and support functionals are provided.
Revised:  Furthermore, we explore the relations existing between mathematical and financial properties attributable to an acceptance set , and the corresponding properties of the induced measure on the other . Dual characterizations in terms of polar sets and support functionals are provided.
Edit intent: coherence
Original:  Furthermore, we explore the relations existing between mathematical and financial properties attributable to an acceptance set on the one hand , and the corresponding properties of the induced measure on the other . Dual characterizations in terms of polar sets and support functionals are provided.
Revised:  Furthermore, we explore the relations existing between mathematical and financial properties attributable to an acceptance set on the one hand , and the corresponding properties of the induced measure . Hence, we fill the gap that is the lack of an acceptance set for deviation measures . Dual characterizations in terms of polar sets and support functionals are provided.
Edit intent: meaning-changed
Original:  Through a combination of collaborative best subspace identification, and per-agent learning of an unknown vector in the corresponding low-dimensional subspace, we show that the per-agent  regret is much smaller than the case when agents do not communicate .
Revised:  By distributing the search for the optimal subspace across users and learning of the unknown vector by each agent in the corresponding low-dimensional subspace, we show that the per-agent regret is much smaller than the case when agents do not communicate .
Edit intent: clarity
Original:  Through a combination of collaborative best subspace identification, and per-agent learning of an unknown vector in the corresponding low-dimensional subspace, we show that the per-agent  regret is much smaller than the case when agents do not communicate .
Revised:  Through a combination of collaborative best subspace identification, and per-agent learning of an unknown vector in the corresponding low-dimensional subspace, we show that the per-agent finite-time regret is much smaller than the case when agents do not communicate .
Edit intent: meaning-changed
Original:  Through a combination of collaborative best subspace identification, and per-agent learning of an unknown vector in the corresponding low-dimensional subspace, we show that the per-agent  regret is much smaller than the case when agents do not communicate . By collaborating to identify the subspace containing \theta^*, we show that each agent effectively solves an easier instance of the linear bandit (compared to the case of no collaboration), thus leading to the reduced per-agent regret .
Revised:  Through a combination of collaborative best subspace identification, and per-agent learning of an unknown vector in the corresponding low-dimensional subspace, we show that the per-agent regret is much smaller than the case when agents do not communicate .
Edit intent: coherence
Original:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) obtained by an elegant application of  [ Frank and Tardos, Combinatorica 1987 ] due to Dadush .
Revised:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) obtained by an elegant application of simultaneous diophantine approximation due to [ Frank and Tardos, Combinatorica 1987 ] due to Dadush .
Edit intent: meaning-changed
Original:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) obtained by an elegant application of  [ Frank and Tardos, Combinatorica 1987 ] due to Dadush .
Revised:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) obtained by an elegant application of [ Gr\"otschel, Lov\'asz and Schrijver, Prog. Comb. Opt. 1984, Springer 1988 ] due to Dadush .
Edit intent: meaning-changed
Original:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) obtained by an elegant application of  [ Frank and Tardos, Combinatorica 1987 ] due to Dadush .
Revised:  This improves upon the previously best oracle complexity of O(n^2 (n + \log(R))) obtained by an elegant application of [ Frank and Tardos, Combinatorica 1987 ] over thirty years ago .
Edit intent: meaning-changed