wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The results show that despite apparently uncoupled joint probabilities, the couplings possess some aspects which diverge from random Gaussian noise. Thereby, with the aid of the network's topological and statistical measurements , we distinguished basic structures of coupling of cross-market networks.
Revised:  The results show that despite apparently uncoupled joint probabilities, the couplings possess some aspects which diverge from the same features of the network mapped from white noise, and from the network mapped from two surrogate time-series. These deviations prove that there exist joint information and cross-correlation therein. By applying the network's topological and statistical measurements , we distinguished basic structures of coupling of cross-market networks.
Edit intent: meaning-changed
Original:  Thereby, with the aid of the network's topological and statistical measurements , we distinguished basic structures of coupling of cross-market networks. Meanwhile, it was discovered that even two possibly known uncoupled markets may possess coupled patterns with each other.
Revised:  Thereby, with the aid of the network's topological and statistical measurements , we distinguished basic structures of cross-correlation and coupling of cross-markets. It was discovered that even two possibly known uncoupled markets may possess coupled patterns with each other.
Edit intent: clarity
Original: Assessing the impacts of COVID-19 are of paramount importance for global sustainability. Using a coordinated set of high-resolution sectoral assessment tools, we report a decrease of 4.2\% in global CO_2 emission in first quarter of 2020.
Revised: Assessing the impacts of COVID-19 pandemic has affected global energy use and CO2 emissions from fossil fuel use and cement production. Here we show that the decrease in global fossil CO2 emissions during the first quarter of 2020.
Edit intent: meaning-changed
Original:  Using a coordinated set of high-resolution sectoral assessment tools, we report a decrease of 4.2\% in global CO_2 emission in first quarter of 2020. Our emission estimatesreflect near real time inventories of emissions }  from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Revised:  Using a coordinated set of high-resolution sectoral assessment tools, we report a decrease of 4.2\% in global CO_2 emission in first quarter of 2020 was of 5.8\% (542 Mt CO2 with a 20\% 1- } from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Edit intent: meaning-changed
Original:  Our emission estimatesreflect near real time inventories of emissions }  from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Revised:  Our emission estimatesreflect near real time inventories of emissions \sigma} from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Edit intent: meaning-changed
Original:  Our emission estimatesreflect near real time inventories of emissions }  from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Revised:  Our emission estimatesreflect near real time inventories of emissions } uncertainty). Unlike other emerging estimates, ours show the temporal dynamics of emissions based on actual emissions data from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Edit intent: meaning-changed
Original:  Our emission estimatesreflect near real time inventories of emissions }  from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Revised:  Our emission estimatesreflect near real time inventories of emissions } from power generation (for 29 countries) and industry (for 73 countries), on near real time activity data for road transportation (for 132 countries), aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Edit intent: meaning-changed
Original:  Our emission estimatesreflect near real time inventories of emissions }  from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years.
Revised:  Our emission estimatesreflect near real time inventories of emissions } from power generation , transportation, industry, international aviation and maritime transportation, and on heating degree days for commercial and residential sectors emissions (for 206 countries). These dynamic estimates cover all of the human induced CO2 emissions in recent years.
Edit intent: meaning-changed
Original:  Our emission estimatesreflect near real time inventories of emissions }  from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions in recent years. Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Revised:  Our emission estimatesreflect near real time inventories of emissions } from power generation , transportation, industry, international aviation and maritime sectors in 34 countries that account for >70\% of world energy-related CO2 emissions from fossil fuel combustion and cement production. The largest share of COVID-related decreases in emissions are due to decreases in industry (157.9 Mt CO2 , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Edit intent: meaning-changed
Original:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Revised:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , -7.1\% compared to 2019), followed by road transportation (145.7 Mt CO2, -8.3 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Edit intent: meaning-changed
Original:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Revised:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), power generation (131.6 Mt CO2, -3.8\%), residential (47.8 Mt CO2, -3.6\%), fishing and maritime transport (35.5Mt CO2, -13.3\%) and aviation (33.4 Mt CO2, -8.0\%). Regionally, decreases in emissions from China were the largest and earliest (-10.3\%), followed by Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Edit intent: meaning-changed
Original:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Revised:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK) (-4.3\%) and India (-2.4\%) , respectively.
Edit intent: meaning-changed
Original:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively.
Revised:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and the U.S. (-4.2\%). Relative decreases of regional CO2 emissions are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks. Despite the unprecedented decreases in CO2 emissions and comparable decreases in economic activities, we monitored decreases in the carbon intensity (Emission per unit of GDP) in China (3.5\%) , respectively.
Edit intent: meaning-changed
Original:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , respectively. The decline of short-lived gaseous pollutants, such as NO_2 concentration observed by Satellites (-25.73\%for China, -4.76\%for US) and ground observations (-23\%for China)is consistent with the estimates based on energy activity (-23.94\% for China, -3.52\% for US), but the decline is not seen in satellite assessments of aerosol optical depth (AOD) or dry column CO_2 (XCO_2). With fast recovery and partial re-opening of national economies, our findings suggest that total annual emissions may drop far less than previously estimated (e.g. , by 25\%for China and more than 5\%for the whole world) . However, the longer-term effects on CO_2 emissions are unknown and should be carefully monitored using multiple measures .
Revised:  Regional variations in CO_2 emissions are significant, with a decrease in China (-9.3\% ) , US (-3.0 \%), Europe ( EU-27 & UK)(-3.3\%) and India (-2.4\%) , the U.S. (4.5\%) and Europe (5.4\%) over the first quarter, suggesting that carbon-intensive activities have been disproportionally impacted .
Edit intent: meaning-changed
Original: Neural machine translation (NMT) models do not work well in domains different from the training data. The standard approach to this problem is to build a small parallel data in the target domain and perform domain adaptation from a source domain where massive parallel data is available. However, domain adaptation  between distant domains (e.g.,  subtitles and research papers) does not perform effectively because of mismatches in vocabulary;
Revised: Neural network methods exhibit strong performance only in a few resource-rich domains. Practitioners, therefore, employ domain adaptation between distant domains (e.g., subtitles and research papers) does not perform effectively because of mismatches in vocabulary;
Edit intent: coherence
Original:  In this study, aiming to solve these vocabulary mismatches in distant domain adaptation , we propose vocabulary adaptation, a simple method for effective fine-tuning that adapts embedding layers in a given pre-trained NMT model to the target domain.
Revised:  In this study, aiming to solve these vocabulary mismatches in domain adaptation for neural machine translation (NMT) , we propose vocabulary adaptation, a simple method for effective fine-tuning that adapts embedding layers in a given pre-trained NMT model to the target domain.
Edit intent: clarity
Original:  Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model , by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space.
Revised:  Prior to fine-tuning, our method replaces the embedding layers of the NMT model , by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space.
Edit intent: clarity
Original:  Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model , by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space.
Revised:  Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space.
Edit intent: fluency
Original:  Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model , by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space.
Revised:  Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model , by projecting general word embeddings induced from monolingual data in a target domain onto the source-domain embedding space.
Edit intent: fluency
Original:  Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model , by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space.
Revised:  Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model , by projecting general word embeddings induced from monolingual data in the target domain onto a source-domain embedding space.
Edit intent: fluency
Original:  Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our vocabulary adaptation improves the performance of  fine-tuning by 3.6 BLEU points .
Revised:  Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our method improves the performance of fine-tuning by 3.6 BLEU points .
Edit intent: clarity
Original:  Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our vocabulary adaptation improves the performance of  fine-tuning by 3.6 BLEU points .
Revised:  Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our vocabulary adaptation improves the performance of conventional fine-tuning by 3.6 BLEU points .
Edit intent: meaning-changed
Original:  Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our vocabulary adaptation improves the performance of  fine-tuning by 3.6 BLEU points .
Revised:  Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our vocabulary adaptation improves the performance of fine-tuning by 3.86 and 3.28 BLEU points in En-Ja and De-En translation, respectively .
Edit intent: meaning-changed
Original: Morphological inflection is a process of word formation where base words are modified to express different grammatical categories such as tense, case, voice, person, or number. World Englishes , such as Colloquial Singapore English (CSE) and African American Vernacular English (AAVE), differ from Standard English dialects in inflection use .
Revised: Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English (CSE) and African American Vernacular English (AAVE), differ from Standard English dialects in inflection use .
Edit intent: clarity
Original:  Models using this encoding also generalize better to non-standard dialects without explicit training . We suggest metrics to evaluate tokenizers and extensive model-independent analyses demonstrate the efficacy of the encoding when used together with data-driven subword tokenizers  .
Revised:  Models using this encoding also generalize better to non-standard dialects without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers .
Edit intent: meaning-changed
Original:  We suggest metrics to evaluate tokenizers and extensive model-independent analyses demonstrate the efficacy of the encoding when used together with data-driven subword tokenizers  .
Revised:  We suggest metrics to evaluate tokenizers and extensive model-independent analyses demonstrate the efficacy of the encoding when used together with data-driven subword tokenizers . Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so .
Edit intent: meaning-changed
Original:  Accordingly, we propose to decouple word vectors into their norm and direction then computing the alignment-based similarity using earth mover's distance (  optimal transport cost), which we refer to as word rotator's distance.
Revised:  Accordingly, we propose a method that first decouples word vectors into their norm and direction then computing the alignment-based similarity using earth mover's distance ( optimal transport cost), which we refer to as word rotator's distance.
Edit intent: clarity
Original:  Accordingly, we propose to decouple word vectors into their norm and direction then computing the alignment-based similarity using earth mover's distance (  optimal transport cost), which we refer to as word rotator's distance.
Revised:  Accordingly, we propose to decouple word vectors into their norm and direction , and then computes alignment-based similarity using earth mover's distance ( optimal transport cost), which we refer to as word rotator's distance.
Edit intent: clarity
Original:  Accordingly, we propose to decouple word vectors into their norm and direction then computing the alignment-based similarity using earth mover's distance (  optimal transport cost), which we refer to as word rotator's distance.
Revised:  Accordingly, we propose to decouple word vectors into their norm and direction then computing the alignment-based similarity using earth mover's distance ( i.e., optimal transport cost), which we refer to as word rotator's distance.
Edit intent: coherence
Original:  Furthermore, we demonstrate how to grow the norm and direction of word vectors (vector converter) ; this is a new systematic approach derived from the sentence-vector estimation methods , which can significantly improve the performance of the proposed method .
Revised:  Furthermore, we demonstrate how to grow the norm and direction of word vectors (vector converter) , which is a new systematic approach derived from the sentence-vector estimation methods , which can significantly improve the performance of the proposed method .
Edit intent: coherence
Original:  Unfortunately, datasets have only limited cross-modal associations: images are not paired with others , captions are only paired with others that describe the same image, there are no negative associations and there are missing positive cross-modal associations.
Revised:  Unfortunately, datasets have limited cross-modal associations: images are not paired with others , captions are only paired with others that describe the same image, there are no negative associations and there are missing positive cross-modal associations.
Edit intent: clarity
Original:  Unfortunately, datasets have only limited cross-modal associations: images are not paired with others , captions are only paired with others that describe the same image, there are no negative associations and there are missing positive cross-modal associations.
Revised:  Unfortunately, datasets have only limited cross-modal associations: images are not paired with other images , captions are only paired with others that describe the same image, there are no negative associations and there are missing positive cross-modal associations.
Edit intent: fluency
Original:  Unfortunately, datasets have only limited cross-modal associations: images are not paired with others , captions are only paired with others that describe the same image, there are no negative associations and there are missing positive cross-modal associations.
Revised:  Unfortunately, datasets have only limited cross-modal associations: images are not paired with others , captions are only paired with other captions of the same image, there are no negative associations and there are missing positive cross-modal associations.
Edit intent: clarity
Original:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Revised:  We address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Edit intent: fluency
Original:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Revised:  To address this gap Crisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Edit intent: coherence
Original:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Revised:  To address this gap , we create thewith Crisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Edit intent: meaning-changed
Original:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Revised:  To address this gap , we create theCrisscrossed Captions (CxC) , an extension of the MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Edit intent: clarity
Original:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Revised:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO dataset with human semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Edit intent: clarity
Original:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{247,315 intra- and inter-modality pairs.
Revised:  To address this gap , we create theCrisscrossed Captions (CxC) dataset, extending MS-COCO with new semantic similarity judgments for \textbf{267,095 intra- and inter-modality pairs.
Edit intent: meaning-changed
Original:  We provide baseline model performance results for both retrieval and correlations with human rankings, emphasizing both intra- and inter-modality learning.
Revised:  We report baseline results on CxC for strong existing unimodal and multimodal models. We also evaluate a multitask dual encoder trained on both image-caption and caption-caption pairs that crucially demonstrates CxC's value for measuring the influence of intra- and inter-modality learning.
Edit intent: meaning-changed
Original: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph  where each vertex  connects to at least \gamma fraction of the other vertices .
Revised: Given a user-specified minimum degree threshold , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph where each vertex connects to at least \gamma fraction of the other vertices .
Edit intent: fluency
Original: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph  where each vertex  connects to at least \gamma fraction of the other vertices .
Revised: Given a user-specified minimum degree threshold \gamma %DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph where each vertex connects to at least \gamma fraction of the other vertices .
Edit intent: fluency
Original: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph  where each vertex  connects to at least \gamma fraction of the other vertices .
Revised: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma, a \gamma -quasi-clique is a subgraph where each vertex connects to at least \gamma fraction of the other vertices .
Edit intent: fluency