wanyu/IteraTeR_full_sent huggingface dataset:
Original:  These match corresponding positive results using upper bounds on the threshold or approximate degree in the SQ model for PAC or agnostic learning , and in this sense our results show that the polynomial method is a universal, best-possible approach for distribution-free CSQ learning. Our results imply the first exponential (in the dimension) CSQ lower bounds for PAC learning intersections of two halfspaces and constant depth circuits as well as the first exponential lower bounds for agnostically learning conjunctions and halfspaces .
Revised:  These match corresponding positive results using upper bounds on the threshold or approximate degree in the SQ model for PAC or agnostic learning . Many of these results were implicit in earlier works of Feldman and Sherstov .
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Original:  We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks  .
Revised:  We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks . We also describe a simple technique to prevent "accidental translation" in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language .
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Original: Should sequences be modeled autoregressively---one symbol at a time? How much computation is needed to predict the next symbol? While local normalization is cheap, this also limits its power. We point out that some probability distributions over discrete sequences cannot be well-approximated by any autoregressive model whose runtime and parameter size grow polynomially in the sequence length---even though their unnormalized sequence probabilities are efficient to computeexactly. Intuitively, the probability of the next symbol can be expensive to compute or approximate (even via randomized algorithms) when it marginalizes over exponentially many possible futures, which is in general \mathrm{NP-hard.
Revised: -hard.
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Original:  Our result is conditional on the widely believed hypothesis that NP }%DIFDELCMD < \nsubseteq %%% \mathrm{P/poly energy-based ) models may sometimes outperform locally normalized (autoregressive) models , as we demonstrate experimentally for language modeling .
Revised:  Our result is conditional on the widely believed hypothesis that NP }%DIFDELCMD < \nsubseteq %%% Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol. While this is attractive, it means they cannot model distributions whose next-symbol probability is hard to compute. Indeed, they cannot even model them well enough to solve associated easy decision problems for which an engineer might want to consult a language model. These limitations apply no matter how much computation and data are used to train the model, unless the model is given access to oracle parameters that grow superpolynomially in sequence length. Thus, simply training larger autoregressive language models is not a panacea for NLP. Alternatives include energy-based ) models may sometimes outperform locally normalized (autoregressive) models , as we demonstrate experimentally for language modeling .
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Original:  Our result is conditional on the widely believed hypothesis that NP }%DIFDELCMD < \nsubseteq %%% \mathrm{P/poly energy-based ) models may sometimes outperform locally normalized (autoregressive) models , as we demonstrate experimentally for language modeling .
Revised:  Our result is conditional on the widely believed hypothesis that NP }%DIFDELCMD < \nsubseteq %%% \mathrm{P/poly energy-based models , as we demonstrate experimentally for language modeling .
Edit intent: meaning-changed
Original:  Our result is conditional on the widely believed hypothesis that NP }%DIFDELCMD < \nsubseteq %%% \mathrm{P/poly energy-based ) models may sometimes outperform locally normalized (autoregressive) models , as we demonstrate experimentally for language modeling .
Revised:  Our result is conditional on the widely believed hypothesis that NP }%DIFDELCMD < \nsubseteq %%% \mathrm{P/poly energy-based ) models may sometimes outperform locally normalized (autoregressive) models (which give up efficient sampling) and latent-variable autoregressive models (which give up efficient scoring of a given string). Both are powerful enough to escape the above limitations .
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Original:  In this paper, we propose Multimodal Temporal Graph Attention Networks (MTGAT). MTGAT is an interpretable graph-based neural model that provides a suitable framework for analyzing this type of multimodal sequential data.
Revised:  In this paper, we propose Modal-Temporal Attention Graph (MTAG). MTAG is an interpretable graph-based neural model that provides a suitable framework for analyzing this type of multimodal sequential data.
Edit intent: clarity
Original:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Revised:  Then, a novel graph fusion operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Edit intent: meaning-changed
Original:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Revised:  Then, a novel graph operation, called MTAG fusion , along with a dynamic pruning and read-out technique is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Edit intent: meaning-changed
Original:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Revised:  Then, a novel graph operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique , is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Edit intent: fluency
Original:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Revised:  Then, a novel graph operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique is designed to efficiently process this modal-temporal graph and capture various interactions . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Edit intent: clarity
Original:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Revised:  Then, a novel graph operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, MTAG achieves state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Edit intent: clarity
Original:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Revised:  Then, a novel graph operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks , while utilizing significantly fewer computations .
Edit intent: clarity
Original:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
Revised:  Then, a novel graph operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer model parameters .
Edit intent: clarity
Original:  Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for human annotations in the target languages  . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
Revised:  Our proposed multi-task training of the generative model only requires the training samples in English , thus removing the need for human annotations in the target languages . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
Edit intent: clarity
Original:  Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for human annotations in the target languages  . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
Revised:  Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for labeled samples in the target languages . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
Edit intent: clarity
Original:  Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for human annotations in the target languages  . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
Revised:  Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for human annotations in the target languages , making it applicable to far more languages than those with labeled data . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
Edit intent: meaning-changed
Original:  Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for human annotations in the target languages  . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
Revised:  Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for human annotations in the target languages . Experimental results show our proposed approach achieves significant gains on several multilingual QA benchmarks, reducing the gap between zero-shot and supervised performance of QA models on various languages .
Edit intent: meaning-changed
Original:  Our proposed multi-task training of the generative model only requires the  training samples in English, thus removing the need for labeled samples in the target languages, making it applicable to far more languages than those with labeled data.
Revised:  Our proposed multi-task training of the generative model only requires the labeled training samples in English, thus removing the need for labeled samples in the target languages, making it applicable to far more languages than those with labeled data.
Edit intent: meaning-changed
Original:  Our proposed multi-task training of the generative model only requires the  training samples in English, thus removing the need for labeled samples in the target languages, making it applicable to far more languages than those with labeled data.
Revised:  Our proposed multi-task training of the generative model only requires the training samples in English, thus removing the need for such samples in the target languages, making it applicable to far more languages than those with labeled data.
Edit intent: clarity
Original:   Experimental results show our proposed approach achieves significant gains on several multilingual QA benchmarks , reducing the gap between zero-shot and supervised performance of  QA models on various languages.
Revised:  Human evaluations indicate the majority of such samples are grammatically correct and sensible. Experimental results show our proposed approach achieves significant gains on several multilingual QA benchmarks , reducing the gap between zero-shot and supervised performance of QA models on various languages.
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Original:  A diverse array of numerical illustrations are presented, along with the MATLAB code for reproducing the results in a publicly available at repository URL A number of open problems are discussed, and an appendix containing all algorithms type-set in a uniform fashion is provided.
Revised:  Additionally, new block versions of low-synchronization variants are derived, and their efficacy and stability are demonstrated for a wide range of challenging examples. Low-synchronization variants appear remarkably stable for s-step-like matrices built with Newton polynomials, pointing towards a new stable and efficient backbone for Krylov subspace methods. Numerical examples are computed with a versatile MATLAB package hosted at URL and scripts for reproducing all results in the paper are provided. Block Gram-Schmidt implementations in popular software packages are discussed, along with a number of open problems are discussed, and an appendix containing all algorithms type-set in a uniform fashion is provided.
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Original:  A diverse array of numerical illustrations are presented, along with the MATLAB code for reproducing the results in a publicly available at repository URL A number of open problems are discussed, and an appendix containing all algorithms type-set in a uniform fashion is provided.
Revised:  A diverse array of numerical illustrations are presented, along with the MATLAB code for reproducing the results in a publicly available at repository URL A number of open problems . An appendix containing all algorithms type-set in a uniform fashion is provided.
Edit intent: clarity
Original:  Experimental results show that ERNIE-Gram outperforms previous pre-training models like XLNet and RoBERTa by a large margin, and achieves comparable results with state-of-the-art methods. 
Revised:  Experimental results show that ERNIE-Gram outperforms previous pre-training models like XLNet and RoBERTa by a large margin, and achieves comparable results with state-of-the-art methods. The source codes and pre-trained models have been released at URL
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Original: Identifying similar movie characters is a captivating task that can be our first step to understand the commonalities between human characteristics and experiences . Here, we seek to identify similar movie character descriptions and evaluate our findings based on whether they belong to a common fan-curated trope (theme). Rather than simply comparing the embedding representation of character description, we use a pair-wise attention model to make use of complex word/span-level relationships across the two character descriptions to predict the similarity of the two characters. Naively, such a model would require the exhaustive comparison of each character to all other characters, which is an O(n^2) operation with respect to the number of characters, making it unfeasible to be used in practice. We reduced this into an O(n) operation using a two-step approach that involves choosing only a tiny fraction of character-pairs to perform pairwise attention on while still being effective in this task.
Revised: While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness. We propose a new task to capture this richness based on an unlikely setting: movie characters. We sought to capture theme-level similarities between movie characters that were community-curated into 20,000 themes. By introducing a two-step approach that involves choosing only a tiny fraction of character-pairs to perform pairwise attention on while still being effective in this task.
Edit intent: coherence
Original:  We reduced this into an O(n) operation using a two-step approach that involves choosing only a tiny fraction of character-pairs to perform pairwise attention on while still being effective in this task. Our approach performs at least 9-27 \% better than methods based on state-of-the-art paragraph embedding representations.
Revised:  We reduced this into an O(n) operation using a two-step approach that balances performance and efficiency, we managed to achieve 9-27 \% better than methods based on state-of-the-art paragraph embedding representations.
Edit intent: clarity
Original:  We then ground the use of divergence measuresin three different application groups -- 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild  . From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and higher-order measures are common for 2).
Revised:  Further, to understand the common use-cases of these measures, we recognise three novel applications -- 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild . From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and higher-order measures are common for 2).
Edit intent: clarity
Original:  We then ground the use of divergence measuresin three different application groups -- 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild  . From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and higher-order measures are common for 2).
Revised:  We then ground the use of divergence measuresin three different application groups -- 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild -- and use it URLanise our literature . From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and higher-order measures are common for 2).
Edit intent: meaning-changed
Original:  We then ground the use of divergence measuresin three different application groups -- 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild  . From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and higher-order measures are common for 2).
Revised:  We then ground the use of divergence measuresin three different application groups -- 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild . From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and Higher-order measures are more common for 2).
Edit intent: clarity
Original:  To further help researchers , we validate these uses empirically through a correlation analysis of performance drops. We consider the current contextual word representations (CWR) to contrast with the older word distribution based representationsfor this analysis .
Revised:  To further help researchers choose appropriate measures to predict drop in performance -- an important aspect of Decisions in the Wild, we perform correlation analysis spanning 130 domain adaptation scenarios, 3 varied NLP tasks and 12 divergence measures identified from our survey. To calculate these divergences, we consider the current contextual word representations (CWR) to contrast with the older word distribution based representationsfor this analysis .
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Original: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through three aspects: 1) fast model predictive foothold planning,  2) LQR control for robust motion tracking and 3) adaptive feet for terrain adaptation .
Revised: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through two aspects: 1) fast model predictive foothold planning, 2) LQR control for robust motion tracking and 3) adaptive feet for terrain adaptation .
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Original: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through three aspects: 1) fast model predictive foothold planning,  2) LQR control for robust motion tracking and 3) adaptive feet for terrain adaptation .
Revised: In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through three aspects: 1) fast model predictive foothold planning, and 2) LQR control for robust motion tracking and 3) adaptive feet for terrain adaptation .
Edit intent: coherence