wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We find that we can train a competent multi-hop QA model with only generated data. The F1 gap between the unsupervised and fully-supervised models is less than 20 in both the HotpotQA and the HybridQA dataset. Further experiments reveal that an unsupervised pretraining with the QA data generated by our model would greatly reduce the demand for human-annotated training data for multi-hop QA.
Revised:  We find that we can train a competent multi-hop QA which achieves 61\% and 83\% of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data for multi-hop QA.
Edit intent: meaning-changed
Original:  We propose measurements of label-rationale association , a necessary property of faithful rationales, for these models. Using our measurements , we show that a state-of-the-art joint model based on T5 has strengths and weaknesses for producing faithful  rationales.
Revised:  We propose measurements of label-rationale association , a necessary property for faithfulness: the internal mechanisms of the model producing the label and the rationale must be meaningfully correlated. We propose two measurements to test this property: robustness equivalence and feature importance agreement. We find that state-of-the-art joint model based on T5 has strengths and weaknesses for producing faithful rationales.
Edit intent: meaning-changed
Original:  In this work, we introduce a general method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner which makes use of characteristics of the target dataset  such as the length and abstractiveness of the desired summaries.
Revised:  In this work, we introduce a novel and generalizable method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner which makes use of characteristics of the target dataset such as the length and abstractiveness of the desired summaries.
Edit intent: clarity
Original:  In this work, we introduce a general method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner which makes use of characteristics of the target dataset  such as the length and abstractiveness of the desired summaries.
Revised:  In this work, we introduce a general method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner . WikiTransfer fine-tunes pretrained models on pseudo-summaries, produced from generic Wikipedia data, which contain characteristics of the target dataset such as the length and abstractiveness of the desired summaries.
Edit intent: meaning-changed
Original:  In this work, we introduce a general method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner which makes use of characteristics of the target dataset  such as the length and abstractiveness of the desired summaries.
Revised:  In this work, we introduce a general method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner which makes use of characteristics of the target dataset , such as the length and abstractiveness of the desired summaries.
Edit intent: fluency
Original:  In this work, we introduce a general method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner which makes use of characteristics of the target dataset  such as the length and abstractiveness of the desired summaries.
Revised:  In this work, we introduce a general method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner which makes use of characteristics of the target dataset such as the length and level of abstraction of the desired summaries.
Edit intent: clarity
Original:  We achieve state-of-the-art, zero-shot abstractive summarization performance on the CNN-DailyMail dataset and demonstrate the effectiveness of our approach on three additional , diverse datasets.
Revised:  WikiTransfer models achieve state-of-the-art, zero-shot abstractive summarization performance on the CNN-DailyMail dataset and demonstrate the effectiveness of our approach on three additional , diverse datasets.
Edit intent: meaning-changed
Original:  The models fine-tuned in this unsupervised manner are more robust to noisy data and also achieve better  few-shot performance using 10 and 100 training examples . We perform ablation studies on the effect of the components of our unsupervised fine-tuning data and analyze the performance of these models in few-shot scenarios along with data augmentation techniques using both automatic and human evaluation.
Revised:  The models fine-tuned in this unsupervised manner are more robust to noisy data and also achieve better few-shot performance using 10 and 100 training examples when compared to few-shot transfer from other summarization datasets. To further boost performance, we employ data augmentation via round-trip translation as well as introduce a regularization term for improved few-shot transfer. To understand the role of dataset aspects in transfer performance and the quality of the resulting output summaries, we further study the effect of the components of our unsupervised fine-tuning data and analyze the performance of these models in few-shot scenarios along with data augmentation techniques using both automatic and human evaluation.
Edit intent: meaning-changed
Original:  We perform ablation studies on the effect of the components of our unsupervised fine-tuning data and analyze the performance of these models in few-shot scenarios along with data augmentation techniques using both automatic and human evaluation.
Revised:  We perform ablation studies on the effect of the components of our unsupervised fine-tuning data and analyze few-shot scenarios along with data augmentation techniques using both automatic and human evaluation.
Edit intent: clarity
Original:  We perform ablation studies on the effect of the components of our unsupervised fine-tuning data and analyze the performance of these models in few-shot scenarios along with data augmentation techniques using both automatic and human evaluation.
Revised:  We perform ablation studies on the effect of the components of our unsupervised fine-tuning data and analyze the performance of these models in few-shot performance using both automatic and human evaluation.
Edit intent: clarity
Original: As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is of critical importance. Based on observations , AVs need to  predict the future behaviors of other traffic participants, and be aware of the uncertainties associated with such prediction so that safe, efficient, and human-like motions can be generated .
Revised: As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions , AVs need to predict the future behaviors of other traffic participants, and be aware of the uncertainties associated with such prediction so that safe, efficient, and human-like motions can be generated .
Edit intent: clarity
Original:  Based on observations , AVs need to  predict the future behaviors of other traffic participants, and be aware of the uncertainties associated with such prediction so that safe, efficient, and human-like motions can be generated .
Revised:  Based on observations , AVs need to not only predict the future behaviors of other traffic participants, and be aware of the uncertainties associated with such prediction so that safe, efficient, and human-like motions can be generated .
Edit intent: clarity
Original:  Based on observations , AVs need to  predict the future behaviors of other traffic participants, and be aware of the uncertainties associated with such prediction so that safe, efficient, and human-like motions can be generated .
Revised:  Based on observations , AVs need to predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such prediction so that safe, efficient, and human-like motions can be generated .
Edit intent: coherence
Original:  In this paper, we propose an  integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users  and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Revised:  In this paper, we propose an uncertain-aware integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Edit intent: meaning-changed
Original:  In this paper, we propose an  integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users  and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Revised:  In this paper, we propose an integrated prediction and planning (UAPP) framework. It allows the AVs to online infer the characteristics of other road users and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Edit intent: meaning-changed
Original:  In this paper, we propose an  integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users  and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Revised:  In this paper, we propose an integrated prediction and planning framework that allows the AVs to infer the characteristics of other road users and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Edit intent: clarity
Original:  In this paper, we propose an  integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users  and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Revised:  In this paper, we propose an integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties.
Edit intent: meaning-changed
Original:  In this paper, we propose an  integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users  and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties. Based on the definitions of courtesy and confidence , we explore the influences of such factors on the behaviors of AVs in interactive driving scenarios  .
Revised:  In this paper, we propose an integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence , we explore the influences of such factors on the behaviors of AVs in interactive driving scenarios .
Edit intent: clarity
Original: We provide the first polynomial-sample and polynomial-time algorithm for learning tree-structured Ising models. In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which  does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Revised: We show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Edit intent: coherence
Original:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which  does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Revised:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Edit intent: clarity
Original:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which  does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Revised:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \ln n/\epsilon^2) samples, where O( . ) hides an absolute constant which does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Edit intent: clarity
Original:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which  does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Revised:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( \cdot ) hides an absolute constant which does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Edit intent: meaning-changed
Original:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which  does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Revised:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which , importantly, does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Edit intent: clarity
Original:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which  does not depend on the model being learned -- neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Revised:  In particular, we show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance ~ \epsilon from an optimal O(n \log n/\epsilon^2) samples, where O( . ) hides an absolute constant which does not depend on the model being learned - neither its tree nor the magnitude of its edge strengths, on which we place no assumptions.
Edit intent: fluency
Original:  While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is close to the true one in TV distance, a guarantee called "proper learning."
Revised:  While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is \epsilon-close to the true one in TV distance, a guarantee called "proper learning."
Edit intent: meaning-changed
Original:  While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is close to the true one in TV distance, a guarantee called "proper learning."
Revised:  While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is close to the true one in total variation distance, a guarantee called "proper learning."
Edit intent: meaning-changed
Original:  Prior to our work there were no known sample- and time-efficient algorithms for learning (properly or non-properly) arbitrary tree-structured graphical models. In particular, our guarantees cannot be derived from known results for the Chow-Liu algorithm and the ensuing literature on learning graphical models, including a recent renaissance of algorithms on this learning challenge, which only yield asymptotic consistency results, or sample-inefficient and/or time-inefficient algorithms, unless further assumptions are placed on the graphical model, such as bounds on the "strengths" of the model's edges  .
Revised:  Our guarantees do not follow from known results for the Chow-Liu algorithm and the ensuing literature on learning graphical models, including a recent renaissance of algorithms on this learning challenge, which only yield asymptotic consistency results, or sample-inefficient and/or time-inefficient algorithms, unless further assumptions are placed on the graphical model, such as bounds on the "strengths" of the model's edges .
Edit intent: clarity
Original:  In particular, our guarantees cannot be derived from known results for the Chow-Liu algorithm and the ensuing literature on learning graphical models, including a recent renaissance of algorithms on this learning challenge, which only yield asymptotic consistency results, or sample-inefficient and/or time-inefficient algorithms, unless further assumptions are placed on the graphical model, such as bounds on the "strengths" of the model's edges  .
Revised:  In particular, our guarantees cannot be derived from known results for the Chow-Liu algorithm and the ensuing literature on learning graphical models, including a recent renaissance of algorithms on this learning challenge, which only yield asymptotic consistency results, or sample-inefficient and/or time-inefficient algorithms, unless further assumptions are placed on the graphical model, such as bounds on the "strengths" of the model's edges /hyperedges .
Edit intent: clarity
Original:  While we establish guarantees for a widely known and simple algorithm, the analysis that this algorithm succeeds  is quite complex, requiring a hierarchical classification of the edges into layers with different reconstruction guarantees, depending on their strength, combined with delicate uses of the subadditivity of the squared Hellinger distance over graphical models to control the error accumulation.
Revised:  While we establish guarantees for a widely known and simple algorithm, the analysis that this algorithm succeeds and is sample-optimal is quite complex, requiring a hierarchical classification of the edges into layers with different reconstruction guarantees, depending on their strength, combined with delicate uses of the subadditivity of the squared Hellinger distance over graphical models to control the error accumulation.
Edit intent: meaning-changed
Original:  In this work, we propose a notion of explanation for plans , based on visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint  .
Revised:  In this work, we propose a notion of explanation for a plan of MMP , based on visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint .
Edit intent: meaning-changed
Original:  In this work, we propose a notion of explanation for plans , based on visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint  .
Revised:  In this work, we propose a notion of explanation for plans , based on visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint , clearly illustrating the safety of the plan .
Edit intent: meaning-changed
Original:  Thus, we propose a meta-algorithm, namely MAPS-X  , that can be plugged on existing  sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Revised:  Thus, we propose meta-algorithms, namely MAPS-X , that can be plugged on existing sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Edit intent: fluency
Original:  Thus, we propose a meta-algorithm, namely MAPS-X  , that can be plugged on existing  sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Revised:  Thus, we propose a meta-algorithm, namelymulti-agent plan segmenting MAPS-X , that can be plugged on existing sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Edit intent: meaning-changed
Original:  Thus, we propose a meta-algorithm, namely MAPS-X  , that can be plugged on existing  sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Revised:  Thus, we propose a meta-algorithm, namely-X ( MAPS-X , that can be plugged on existing sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Edit intent: meaning-changed
Original:  Thus, we propose a meta-algorithm, namely MAPS-X  , that can be plugged on existing  sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Revised:  Thus, we propose a meta-algorithm, namely MAPS-X ) and its lazy variant , that can be plugged on existing sampling-based motion planners (e.g., RRT), to produce plans with good explanations .
Edit intent: meaning-changed