wanyu/IteraTeR_full_sent huggingface dataset:
Original: We theoretically and experimentally compare estimators for off-policy evaluation (OPE) using dependent samples obtained via multi-armed bandit (MAB) algorithms. The goal of OPE is to evaluate a new policy using historical data. Because the MAB algorithms sequentially update the policy based on past observations, the generated samples are not independent and identically distributed. To conduct OPE from dependent samples , we need to use some techniques for constructing the estimator with asymptotic normality. In particular, we focus on a doubly robust (DR ) estimator , which consists of an inverse probability weighting (IPW) component and an estimatorof the conditionally expected outcome. We first summarize existing and new theoretical results for such OPE estimators .
Revised: A doubly robust (DR) estimator is crucial in causal inference, which consists of two nuisance parameters: the conditional mean outcome and logging policy (probability of choosing an action). This paper provides a DR estimator for dependent samples obtained in adaptive experiments and introduces two related topics. First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators. Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
Edit intent: meaning-changed
Original: Contact tracing has been a widely-discussed technique for controlling COVID-19. The traditional test-trace-isolate-support paradigm focuses on identifying people after they have been exposed to positive individuals, and isolating them to protect others. This article introduces an alternative and complementary approach, which appears to be the first to notify people before exposure happens, in the context of their interaction network, so that they can directly take actions to avoid exposure themselves, without using personally identifiable information. Our system has just become achievable with present technology : for each positive case, do not only notify their direct contacts , but inform thousands of people of how far away they are from the positive case, as measured in network-theoretic distance in their physical relationship network.
Revised: We introduce a fundamentally different paradigm for contact tracing : for each positive case, do not only notify their direct contacts , but inform thousands of people of how far away they are from the positive case, as measured in network-theoretic distance in their physical relationship network.
Edit intent: coherence
Original:  Our system has just become achievable with present technology : for each positive case, do not only notify their direct contacts , but inform thousands of people of how far away they are from the positive case, as measured in network-theoretic distance in their physical relationship network. This fundamentally different approach has already been deployed in a publicly downloadable app . It brings a new tool to bear on the pandemic , powered by network theory.
Revised:  Our system has just become achievable with present technology : for each positive case, do not only ask direct contacts to quarantine; instead, tell everyone how many relationships away the disease just struck (so, "2" is a close physical contact of a close physical contact). This new approach, which has already been deployed in a publicly downloadable app . It brings a new tool to bear on the pandemic , powered by network theory.
Edit intent: meaning-changed
Original:  We have pursued the use of methods for extracting diverse forms of mechanism relations from the natural language of scientific papers. We seek to identify concepts in COVID-19 and related literature which represent activities, functions , associations and causal relations, ranging from cellular processes to economic impacts.
Revised:  We pursue the construction of a knowledge base (KB) of mechanisms -- a fundamental concept across the sciences encompassing activities, functions , associations and causal relations, ranging from cellular processes to economic impacts.
Edit intent: clarity
Original:  We curate a dataset of scientific papers annotated according to our novel schema . Using an information extraction model trained on this new corpus, we construct a knowledge base (KB) of 2M mechanism relations, which we make publicly available. Our model is able to extract relations at an F1 at least twice that of baselines such as open IE or related scientific IE systems. We conduct experiments examining the ability of our system to retrieve relevant information on viral mechanisms of action, and on applications of AI to COVID-19 research.
Revised:  We curate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 research.
Edit intent: clarity
Original:  We conduct experiments examining the ability of our system to retrieve relevant information on viral mechanisms of action, and on applications of AI to COVID-19 research. In both cases, our system identifies relevant information from our automatically-constructed knowledge base with high precision .
Revised:  We conduct experiments examining the ability of our system to retrieve relevant information on viral mechanisms of action, and on applications of AI to COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts .
Edit intent: meaning-changed
Original:  In this paper, we focus on improving Chinese SLU by flexibly injecting word information , which has shown effectiveness for various Chinese NLP tasks . Previous studies on Chinese SLU do not consider the word information, failing to detect the word boundaries that are beneficial for intent detection and slot filling.
Revised:  In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information , which has shown effectiveness for various Chinese NLP tasks . Previous studies on Chinese SLU do not consider the word information, failing to detect the word boundaries that are beneficial for intent detection and slot filling.
Edit intent: clarity
Original:  In this paper, we focus on improving Chinese SLU by flexibly injecting word information , which has shown effectiveness for various Chinese NLP tasks . Previous studies on Chinese SLU do not consider the word information, failing to detect the word boundaries that are beneficial for intent detection and slot filling.
Revised:  In this paper, we focus on improving Chinese SLU by flexibly injecting word information . Previous studies on Chinese SLU do not consider the word information, failing to detect the word boundaries that are beneficial for intent detection and slot filling.
Edit intent: coherence
Original:  In this paper, we focus on improving Chinese SLU by flexibly injecting word information , which has shown effectiveness for various Chinese NLP tasks . Previous studies on Chinese SLU do not consider the word information, failing to detect the word boundaries that are beneficial for intent detection and slot filling.
Revised:  In this paper, we focus on improving Chinese SLU by flexibly injecting word information , which has shown effectiveness for various Chinese NLP tasks . Previous studies on Chinese SLU do not consider the word information, failing to detect word boundaries that are beneficial for intent detection and slot filling.
Edit intent: fluency
Original:  To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU : (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection ;
Revised:  To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU , which consists of (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection ;
Edit intent: coherence
Original:  To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU : (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection ;
Revised:  To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU : (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection and
Edit intent: fluency
Original:  Experimental results on two Chinese SLU datasets show that our model can capture useful word information and achieve state-of-the-art performance . More importantly, our framework substantially gains further improvements when we plug the word adapters into a BERT, which again demonstrates the effectiveness and flexibility of our word adapter .
Revised:  Experimental results on two Chinese SLU datasets show that our model can capture useful word information and achieve state-of-the-art performance .
Edit intent: clarity
Original: Using the attention map based probing framework from (Clark et al., 2019), we observe that  BERT's attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, varying from a low of 17.77\% for Place to a high of 51.61\% for Artifact.
Revised: Using the attention map based probing frame-work from (Clark et al., 2019), we observe that BERT's attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, varying from a low of 17.77\% for Place to a high of 51.61\% for Artifact.
Edit intent: clarity
Original: Using the attention map based probing framework from (Clark et al., 2019), we observe that  BERT's attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, varying from a low of 17.77\% for Place to a high of 51.61\% for Artifact.
Revised: Using the attention map based probing framework from (Clark et al., 2019), we observe that , on the RAMS dataset (Ebner et al., 2020), BERT's attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, varying from a low of 17.77\% for Place to a high of 51.61\% for Artifact.
Edit intent: meaning-changed
Original: Using the attention map based probing framework from (Clark et al., 2019), we observe that  BERT's attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, varying from a low of 17.77\% for Place to a high of 51.61\% for Artifact.
Revised: Using the attention map based probing framework from (Clark et al., 2019), we observe that BERT's attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, vary-ing from a low of 17.77\% for Place to a high of 51.61\% for Artifact.
Edit intent: fluency
Original:  Next, we find that linear combinations of these heads, estimated with approx 11\% of available total  supervision, can push performance well-higher for some roles - highest two being Victim (68.29\% Accuracy) and Artifact(58.82\% Accuracy).
Revised:  Next, we find that linear combinations of these heads, estimated with approx 11\% of available total event argument detection supervision, can push performance well-higher for some roles - highest two being Victim (68.29\% Accuracy) and Artifact(58.82\% Accuracy).
Edit intent: meaning-changed
Original:  The heads thus estimated have superior cross-sentence performance compared to their jointly estimated equivalents  . Lastly, we seek to isolate to what extent our numbers stem from lexical frequency based associations between gold arguments and roles.
Revised:  The heads thus estimated have superior cross-sentence performance compared to their jointly estimated equivalents , albeit only under the unrealistic assumption that we already know the argument is present in an-other sentence . Lastly, we seek to isolate to what extent our numbers stem from lexical frequency based associations between gold arguments and roles.
Edit intent: meaning-changed
Original: We find UK 'local lockdowns' of cities and sub-regions , focused on limiting contact between households in homes, turn the tide on rising positive COVID-19 cases without the large declines in consumption accompanying the March 2020 national lockdown, which limited all social contact.
Revised: We find UK 'local lockdowns' of cities and small regions , focused on limiting contact between households in homes, turn the tide on rising positive COVID-19 cases without the large declines in consumption accompanying the March 2020 national lockdown, which limited all social contact.
Edit intent: meaning-changed
Original: We find UK 'local lockdowns' of cities and sub-regions , focused on limiting contact between households in homes, turn the tide on rising positive COVID-19 cases without the large declines in consumption accompanying the March 2020 national lockdown, which limited all social contact.
Revised: We find UK 'local lockdowns' of cities and sub-regions , focused on limiting how many people a household can interact with and in what settings, are effective in turning the tide on rising positive COVID-19 cases without the large declines in consumption accompanying the March 2020 national lockdown, which limited all social contact.
Edit intent: clarity
Original: We find UK 'local lockdowns' of cities and sub-regions , focused on limiting contact between households in homes, turn the tide on rising positive COVID-19 cases without the large declines in consumption accompanying the March 2020 national lockdown, which limited all social contact.
Revised: We find UK 'local lockdowns' of cities and sub-regions , focused on limiting contact between households in homes, turn the tide on rising positive COVID-19 cases . Yet, by focusing on household mixing within the home, these local lockdowns have not inflicted the large declines in consumption accompanying the March 2020 national lockdown, which limited all social contact.
Edit intent: meaning-changed
Original:  Our approach is based on a novel alternating minimization scheme that interleaves ordinary least-squares with a semidefinite program for finding appropriate reweightings of the distribution . Moreover we give extensions of our main results to infinite dimensional settings where the feature vectors are represented implicitly via a kernel map.
Revised:  Our approach is based on a novel alternating minimization scheme that interleaves ordinary least-squares with a semidefinite program for finding appropriate reweightings of the distribution under a spectral constraint. Our results obtain essentially optimal dependence on the contamination level \eta, reach the optimal breakdown point, and naturally apply to infinite dimensional settings where the feature vectors are represented implicitly via a kernel map.
Edit intent: meaning-changed
Original:  We extend the metric beyond English and evaluate it on 12 languages for which training examples are available, as well as four "zero-shot" languages , for which we have no fine-tuning data .
Revised:  We extend the metric beyond English and evaluate it on 14 language pairs for which fine-tuning data is available, as well as four "zero-shot" languages , for which we have no fine-tuning data .
Edit intent: meaning-changed
Original:  We extend the metric beyond English and evaluate it on 12 languages for which training examples are available, as well as four "zero-shot" languages , for which we have no fine-tuning data .
Revised:  We extend the metric beyond English and evaluate it on 12 languages for which training examples are available, as well as 4 "zero-shot" languages , for which we have no fine-tuning data .
Edit intent: fluency
Original:  We construct a sequential , neural variational autoencoder that uses a carefully defined encoder, and Gumbel-Softmax reparametrization, to allow for successful backpropagation during training.
Revised:  We construct a sequential neural variational autoencoder that uses a carefully defined encoder, and Gumbel-Softmax reparametrization, to allow for successful backpropagation during training.
Edit intent: fluency
Original:  We construct a sequential , neural variational autoencoder that uses a carefully defined encoder, and Gumbel-Softmax reparametrization, to allow for successful backpropagation during training.
Revised:  We construct a sequential , neural variational autoencoder , which uses Gumbel-Softmax reparametrization within a carefully defined encoder, and Gumbel-Softmax reparametrization, to allow for successful backpropagation during training.
Edit intent: clarity
Original:  We show that our approach  outperforms multiple baselines and the state-of-the-art in narrative script induction on multiple event modeling tasks.
Revised:  The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach outperforms multiple baselines and the state-of-the-art in narrative script induction on multiple event modeling tasks.
Edit intent: meaning-changed
Original:  Our result contrasts with related work by Chernozhukov and Hansen (2005) for thispoint. We also establish identification of the local treatment effects in multi-valued treatment settings and derive the closed-form expressions of the identified treatment effects.
Revised:  Our result contrasts with related work by Chernozhukov and Hansen (2005) with regard to this. In addition, we identify the local treatment effects in multi-valued treatment settings and derive the closed-form expressions of the identified treatment effects.
Edit intent: coherence
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images  as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle.
Revised:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle.
Edit intent: meaning-changed
Original:  We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation . Even a relatively shallow convolutional neural network model was able to learn key driving behaviors from sparsely labelled datasets and was tolerant to environmental variations during deployment of the said driving behaviors .
Revised:  We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity .
Edit intent: clarity
Original:  We provide a novel characterization: the parallel trends assumption holds under all  monotonic transformations of the outcome if and only if a stronger "parallel trends"-type assumption holds on the entire distribution of  potential outcomes.
Revised:  We provide a novel characterization: the parallel trends assumption holds under all strictly monotonic transformations of the outcome if and only if a stronger "parallel trends"-type assumption holds on the entire distribution of potential outcomes.
Edit intent: meaning-changed
Original:  We provide a novel characterization: the parallel trends assumption holds under all  monotonic transformations of the outcome if and only if a stronger "parallel trends"-type assumption holds on the entire distribution of  potential outcomes.
Revised:  We provide a novel characterization: the parallel trends assumption holds under all monotonic transformations of the outcome if and only if a stronger "parallel trends"-type assumption holds on the entire distribution of untreated potential outcomes.
Edit intent: meaning-changed
Original:  This assumption necessarily holds when treatment is (as if ) randomly assigned, but will often be implausible in settings where randomization fails. We show further that the average treatment effect on the treated (ATT) is identified regardless of functional form if and only if the entire distribution of untreated outcomes is identified for the treated group. It is thus impossible to construct an estimator that is consistent (or unbiased) for the ATT regardless of functional form unless one imposes assumptions that identify the entire counterfactual distribution of untreated potential outcomes.
Revised:  This assumption necessarily holds when treatment is (as if ) randomly assigned, (ii) the distribution of potential outcomes is stationary, or (iii) treatment is as-if randomly assigned among a subset of the population and the remainder of the population has stationary potential outcomes. We show further that it is impossible to construct an estimator that is consistent (or unbiased) for the ATT regardless of functional form unless one imposes assumptions that identify the entire counterfactual distribution of untreated potential outcomes.
Edit intent: meaning-changed
Original:  It is thus impossible to construct an estimator that is consistent (or unbiased) for the ATT regardless of functional form unless one imposes assumptions that identify the entire counterfactual distribution of untreated potential outcomes.
Revised:  It is thus impossible to construct any estimator that is consistent (or unbiased) for the ATT regardless of functional form unless one imposes assumptions that identify the entire counterfactual distribution of untreated potential outcomes.
Edit intent: style
Original:  It is thus impossible to construct an estimator that is consistent (or unbiased) for the ATT regardless of functional form unless one imposes assumptions that identify the entire counterfactual distribution of untreated potential outcomes.
Revised:  It is thus impossible to construct an estimator that is consistent (or unbiased) for the average treatment effect on the treated (ATT) without either imposing functional form restrictions or imposing assumptions that identify the entire counterfactual distribution of untreated potential outcomes.
Edit intent: meaning-changed
Original:  It is thus impossible to construct an estimator that is consistent (or unbiased) for the ATT regardless of functional form unless one imposes assumptions that identify the entire counterfactual distribution of untreated potential outcomes.
Revised:  It is thus impossible to construct an estimator that is consistent (or unbiased) for the ATT regardless of functional form unless one imposes assumptions that identify the full distribution of untreated potential outcomes.
Edit intent: clarity
Original:  Our results suggest that researchers who wish to point-identify the ATT should justify one of the following: (i) why treatment is  randomly assigned, (ii) why the chosen functional form is correct at the exclusion of others, or (iii) a method for inferring the entire counterfactual distribution of untreated potential outcomes.
Revised:  Our results suggest that researchers who wish to point-identify the ATT should justify one of the following: (i) why treatment is as-if randomly assigned, (ii) why the chosen functional form is correct at the exclusion of others, or (iii) a method for inferring the entire counterfactual distribution of untreated potential outcomes.
Edit intent: meaning-changed