wanyu/IteraTeR_full_sent huggingface dataset:
Original: We present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations , a self-supervised method for learning universal sentence embeddings that transfer to a wide variety of natural language processing (NLP) tasks. Our objective leverages recent advances in deep metric learning (DML) and has the advantage of being conceptually simple and easy to implement, requiring no specialized architectures or labelled training data.
Revised: We present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations . Inspired by recent advances in deep metric learning (DML) and has the advantage of being conceptually simple and easy to implement, requiring no specialized architectures or labelled training data.
Edit intent: coherence
Original:  We demonstrate that our objective can be used to pretrain transformers to state-of-the-art performance on SentEval, a popular benchmark for evaluating universal sentence embeddings, outperforming existing supervised, semi-supervised and unsupervised methods. We perform extensive ablations to determine which factors contribute to the quality of the learned embeddings  .
Revised:  When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings .
Edit intent: clarity
Original:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity  O\big( s \log^3 d \cdot \log ^4\frac{1{\epsilon}} {\epsilon}}} \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate.
Revised:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity of O\big( s \log^3 d \cdot \log ^4\frac{1{\epsilon}} {\epsilon}}} \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate.
Edit intent: clarity
Original:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity  O\big( s \log^3 d \cdot \log ^4\frac{1{\epsilon}} {\epsilon}}} \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate.
Revised:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity O\big( s \log^3 d \cdot \log {\epsilon}} {\epsilon}}} \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate.
Edit intent: clarity
Original:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity  O\big( s \log^3 d \cdot \log ^4\frac{1{\epsilon}} {\epsilon}}} \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate.
Revised:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity O\big( s \log^3 d \cdot \log ^4\frac{1{\epsilon}} ^3\frac{1{\epsilon}}} \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate.
Edit intent: fluency
Original:  As a byproduct of our analysis, we resolve a long-standing problem in statistics and machine learning: we show that a global optimum of  sparse principal component analysis can be found in polynomial time without any statistical assumption on the data .
Revised:  As a byproduct of our analysis, we show that by applying a result of Bienstock and Michalka (2014), a global optimum of sparse principal component analysis can be found in polynomial time without any statistical assumption on the data .
Edit intent: meaning-changed
Original:  As a byproduct of our analysis, we resolve a long-standing problem in statistics and machine learning: we show that a global optimum of  sparse principal component analysis can be found in polynomial time without any statistical assumption on the data .
Revised:  As a byproduct of our analysis, we resolve a long-standing problem in statistics and machine learning: we show that a global optimum of a form of sparse principal component analysis can be found in polynomial time without any statistical assumption on the data .
Edit intent: clarity
Original:  As a byproduct of our analysis, we resolve a long-standing problem in statistics and machine learning: we show that a global optimum of  sparse principal component analysis can be found in polynomial time without any statistical assumption on the data . This result might be of independent interest to both communities .
Revised:  As a byproduct of our analysis, we resolve a long-standing problem in statistics and machine learning: we show that a global optimum of sparse principal component analysis can be found in polynomial time without any statistical assumption on the data , which has not been recognized before and might be of independent interest to both communities .
Edit intent: meaning-changed
Original:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question of when and how s-sparse halfspaces can be efficiently learned under the challenging %DIFDELCMD < {\em %%% malicious noise model, where an adversary may corrupt both the unlabeled data distribution and the labels.
Revised:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question when and how s-sparse halfspaces can be efficiently learned under the challenging %DIFDELCMD < {\em %%% malicious noise model, where an adversary may corrupt both the unlabeled data distribution and the labels.
Edit intent: clarity
Original:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question of when and how s-sparse halfspaces can be efficiently learned under the challenging %DIFDELCMD < {\em %%% malicious noise model, where an adversary may corrupt both the unlabeled data distribution and the labels.
Revised:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question of when and how s-sparse halfspaces can be efficiently learned under the challenging %DIFDELCMD < {\em %%% malicious noise model, where an adversary may corrupt both the unlabeled data distribution and the labels.
Edit intent: fluency
Original:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question of when and how s-sparse halfspaces can be efficiently learned under the challenging %DIFDELCMD < {\em %%% malicious noise model, where an adversary may corrupt both the unlabeled data distribution and the labels.
Revised:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question of when and how s-sparse halfspaces can be efficiently learned under the challenging %DIFDELCMD < {\em %%% malicious noise model, where an adversary may corrupt both the unlabeled examples and the labels.
Edit intent: clarity
Original:  We answer this question in the affirmative by designing a computationally efficient  algorithm with near-optimal label complexity of O\big({s \log ^3 d \cdot \log^3\frac{1{\epsilon}}  } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate .
Revised:  We answer this question in the affirmative by designing a computationally efficient active learning algorithm with near-optimal label complexity of O\big({s \log ^3 d \cdot \log^3\frac{1{\epsilon}} } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate .
Edit intent: meaning-changed
Original:  We answer this question in the affirmative by designing a computationally efficient  algorithm with near-optimal label complexity of O\big({s \log ^3 d \cdot \log^3\frac{1{\epsilon}}  } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate .
Revised:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity of O\big({s \log {\epsilon}} } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate .
Edit intent: meaning-changed
Original:  We answer this question in the affirmative by designing a computationally efficient  algorithm with near-optimal label complexity of O\big({s \log ^3 d \cdot \log^3\frac{1{\epsilon}}  } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate .
Revised:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity of O\big({s \log ^3 d \cdot \log^3\frac{1{\epsilon}} ^4 \frac d \epsilon } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate .
Edit intent: meaning-changed
Original:  We answer this question in the affirmative by designing a computationally efficient  algorithm with near-optimal label complexity of O\big({s \log ^3 d \cdot \log^3\frac{1{\epsilon}}  } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate . OurO\big({} \big)  main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Revised:  We answer this question in the affirmative by designing a computationally efficient algorithm with near-optimal label complexity of O\big({s \log ^3 d \cdot \log^3\frac{1{\epsilon}} } \big) and noise tolerance \eta = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate , under the assumption that the distribution over (uncorrupted) unlabeled examples is isotropic log-concave. Our algorithm can be straightforwardly tailored to the passive learning setting, and we show that the sample complexity isO\big({} \big) main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Edit intent: meaning-changed
Original:  OurO\big({} \big)  main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Revised:  OurO\big({\frac 1 \epsilon s^2 \log^5 d} \big) main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Edit intent: meaning-changed
Original:  OurO\big({} \big)  main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Revised:  OurO\big({} \big) which also enjoys the attribute efficiency. Our main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Edit intent: meaning-changed
Original:  OurO\big({} \big)  main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Revised:  OurO\big({} \big) main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data -- ~ all of them crucially take the structure of the underlying halfspace into account .
Edit intent: fluency
Original:  OurO\big({} \big)  main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Revised:  OurO\big({} \big) main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- all of them crucially take the structure of the underlying halfspace into account .
Edit intent: fluency
Original:  OurO\big({} \big)  main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account . To the best of our knowledge, this is the first near-optimal result in the setting. As a byproduct of our analysis, we show that by applying a result of Bienstock and Michalka (2014), a global optimum of a form of sparse principal component analysis can be found in polynomial time without any statistical assumption on the data, which has not been recognized before and might be of independent interest .
Revised:  OurO\big({} \big) main techniques include attribute-efficient paradigms for instance reweighting and for empirical risk minimization, and a new analysis of uniform concentration for unbounded data ~ -- ~ all of them crucially take the structure of the underlying halfspace into account .
Edit intent: coherence
Original:  We propose a novel formulation to learn a set of sparse deformation components with local support on the face that, together with an original non-rigid deformation algorithm, allow the 3DMM to precisely fit unseen faces and transfer its semantic annotation to arbitrary 3D faces. We experimented our approach on three large and diverse datasets , showing it can effectively generalize to very different samples and accurately establish a dense correspondence even in presence of complex facial expressions.
Revised:  We propose a novel formulation to learn a set of sparse deformation components with local support on the face that, together with an original non-rigid deformation algorithm, allow the 3DMM to precisely fit unseen faces and transfer its semantic annotation . We extensively experimented our approach on three large and diverse datasets , showing it can effectively generalize to very different samples and accurately establish a dense correspondence even in presence of complex facial expressions.
Edit intent: clarity
Original:  By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a new word embedding intrinsic evaluation task , and the first unsupervised method that estimates the affective meaning of valence in words with high accuracy.
Revised:  By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a novel word embedding intrinsic evaluation task , and the first unsupervised method that estimates the affective meaning of valence in words with high accuracy.
Edit intent: clarity
Original:  By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a new word embedding intrinsic evaluation task , and the first unsupervised method that estimates the affective meaning of valence in words with high accuracy.
Revised:  By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a new word embedding intrinsic evaluation task and a method to measure the affective meaning of valence in words with high accuracy.
Edit intent: clarity
Original:  By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a new word embedding intrinsic evaluation task , and the first unsupervised method that estimates the affective meaning of valence in words with high accuracy.
Revised:  By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a new word embedding intrinsic evaluation task , and the first unsupervised method that estimates the affective meaning of valence (pleasantness/unpleasantness) in words, with high accuracy.
Edit intent: meaning-changed
Original:  We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model , dispensing with templates . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts.
Revised:  Dispensing with templates, we introduce the Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model , dispensing with templates . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts.
Edit intent: clarity
Original:  We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model , dispensing with templates . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts.
Revised:  We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), that can summarize the magnitude of overall bias in neural language models by incorporating a random-effects model , dispensing with templates . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts.
Edit intent: meaning-changed
Original:  We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model , dispensing with templates . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts.
Revised:  We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts.
Edit intent: clarity
Original:  We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model , dispensing with templates . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts.
Revised:  We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model , dispensing with templates . Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variance of effect magnitudes of the same bias in different contexts.
Edit intent: fluency
Original:   Furthermore, we develop two methods, Intersectional Bias Detection (IBD) and Emergent Intersectional Bias Detection (EIBD), to automatically identify the intersectional biases and emergent intersectional biases from static word embeddings in addition to measuring them in contextualized word embeddings.
Revised:  All the models trained on English corpora that we study contain biased representations. Furthermore, we develop two methods, Intersectional Bias Detection (IBD) and Emergent Intersectional Bias Detection (EIBD), to automatically identify the intersectional biases and emergent intersectional biases from static word embeddings in addition to measuring them in contextualized word embeddings.
Edit intent: meaning-changed
Original:  EIBD reaches an accuracy of 84.7\% and 65.3\%, respectively, when detecting the emergent intersectional biases unique to African American females and Mexican American females (random correct identification probability ranges from 1.0\% to 25.5\%) .
Revised:  Our results indicate that biases at the intersection of race and gender associated with members of multiple minority groups, such as African American females and Mexican American females (random correct identification probability ranges from 1.0\% to 25.5\%) .
Edit intent: clarity
Original:  EIBD reaches an accuracy of 84.7\% and 65.3\%, respectively, when detecting the emergent intersectional biases unique to African American females and Mexican American females (random correct identification probability ranges from 1.0\% to 25.5\%) .
Revised:  EIBD reaches an accuracy of 84.7\% and 65.3\%, respectively, when detecting the emergent intersectional biases unique to African American females and Mexican American females , have the highest magnitude across all neural language models .
Edit intent: meaning-changed
Original:   We propose an input model called the RAndom Circuit Block-Encoded Matrix (RACBEM), which is a proper generalization of a dense random matrix in the quantum setting.
Revised:  The success of the quantum LINPACK benchmark should be viewed as the minimal requirement for a quantum computer to perform a useful task of solving linear algebra problems, such as linear systems of equations. We propose an input model called the RAndom Circuit Block-Encoded Matrix (RACBEM), which is a proper generalization of a dense random matrix in the quantum setting.
Edit intent: meaning-changed
Original:  Besides solving linear systems, the RACBEM model can be used to perform a variety of linear algebra tasks  , such as computing spectral measures, time series generated by a Hamiltonian simulation, and thermal averages of the energy.
Revised:  Besides solving linear systems, the RACBEM model can be used to perform a variety of linear algebra tasks relevant to many physical applications , such as computing spectral measures, time series generated by a Hamiltonian simulation, and thermal averages of the energy.
Edit intent: meaning-changed