wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities  , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the description text .
Revised:  Our dataset involves exploring large knowledge graphs (KG) to retrieve abundant knowledge of various types of main entities , which makes the current graph-to-sequence models severely suffered from the problems of information loss and parameter explosion while generating the descriptions .
Edit intent: clarity
Original:  In this paper, we show that attention weights do capture accurate word alignment, which could only be revealed if we choose the correct decoding step and layer to induce word alignment . We propose to induce alignment with the to-be-aligned target token as the decoder input and present two simple but effective interpretation methods for word alignment induction, either through the attention weights or the leave-one-out measures.
Revised:  In this paper, we show that attention weights do capture accurate word alignments and propose two novel word alignment induction methods Shift-Att and Shift-AET. The main idea is to induce alignments at the step when the to-be-aligned target token as the decoder input and present two simple but effective interpretation methods for word alignment induction, either through the attention weights or the leave-one-out measures.
Edit intent: meaning-changed
Original:  We propose to induce alignment with the to-be-aligned target token as the decoder input and present two simple but effective interpretation methods for word alignment induction, either through the attention weights or the leave-one-out measures.
Revised:  We propose to induce alignment with the to-be-aligned target token is the decoder input and present two simple but effective interpretation methods for word alignment induction, either through the attention weights or the leave-one-out measures.
Edit intent: fluency
Original:  We propose to induce alignment with the to-be-aligned target token as the decoder input and present two simple but effective interpretation methods for word alignment induction, either through the attention weights or the leave-one-out measures. In contrast to previous studies, we find that attention weights capture better word alignment than the leave-one-out measures under our setting. Using the proposed method with attention weights , we greatly improve over fast-align on word alignment induction. Finally, we present a multi-task learning framework to train the Transformer model and show that by incorporating GIZA++ alignments into our multi-task training, we can induce significantly better alignmentsthan GIZA++  .
Revised:  We propose to induce alignment with the to-be-aligned target token as the decoder input rather than the decoder output as in previous work. Shift-Att is an interpretation method that induces alignments from the attention weights of Transformer and does not require parameter update or architecture change. Shift-AET extracts alignments from an additional alignment module which is tightly integrated into Transformer and trained in isolation with supervision from symmetrized Shift-Att alignments. Experiments on three publicly available datasets demonstrate that both methods perform better than their corresponding neural baselines and Shift-AET significantly outperforms GIZA++ .
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Original:  Finally, we present a multi-task learning framework to train the Transformer model and show that by incorporating GIZA++ alignments into our multi-task training, we can induce significantly better alignmentsthan GIZA++  .
Revised:  Finally, we present a multi-task learning framework to train the Transformer model and show that by incorporating GIZA++ alignments into our multi-task training, we can induce significantly better alignmentsthan GIZA++ by 1.4-4.8 AER points .
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Original:  Compared with existing unsupervised graph embedding methods , our method shows superior prediction performance with an area under the curve of +0.85. We also present examples of repositioning opportunities found on recent biomedical literature that were also predicted by our method.
Revised:  Compared with existing unsupervised graph embedding methods , our method shows superior prediction performance with an area under the ROC curve, and we present examples of repositioning opportunities found on recent biomedical literature that were also predicted by our method.
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Original:  As a consequence, we get fully-dynamic \varepsilon-coreset algorithms for k-median and k-means with worst-case update time O(\varepsilon^{-2}k^2\log^5 n \log^3 k) and coreset size O(\varepsilon^{-2}k\log n \log^2 k) ignoring \log \log n and \log(1/\varepsilon) factors and assuming that \varepsilon, \lambda = \Omega(1/poly(n)) (very weak assumptions made to make bounds easy to parse) .
Revised:  As a consequence, we get fully-dynamic \varepsilon-coreset algorithms for k-median and k-means with worst-case update time O(\varepsilon^{-2}k^2\log^5 n \log^3 k) and coreset size O(\varepsilon^{-2}k\log n \log^2 k) ignoring \log \log n and \log(1/\varepsilon) factors and assuming that \varepsilon, \lambda = \Omega(1/poly(n)) .
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Original:  Probabilistic models which assume a generative story have been the dominant approach for topic modeling. We propose an alternative approach based on clustering readily available pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words.
Revised:  Probabilistic models which assume a generative story , but in this paper we propose an alternative approach based on clustering readily available pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words.
Edit intent: coherence
Original:  However, regular testing of every individual is expensive. We present an approach to pooled testing, based on the geometry of a hypercube , which enables fast, cost-effective identification of infected people when the prevalence of the virus is low .
Revised:  However, regular testing of every individual expensive. The costs are a challenge for all countries and particularly for developing countries. Cost reductions can be achieved by combining samples and testing them in groups. We propose an algorithm for grouping subsamples, prior to testing, based on the geometry of a hypercube , which enables fast, cost-effective identification of infected people when the prevalence of the virus is low .
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Original:  We present an approach to pooled testing, based on the geometry of a hypercube , which enables fast, cost-effective identification of infected people when the prevalence of the virus is low .
Revised:  We present an approach to pooled testing, based on the geometry of a hypercube . At low prevalence, this testing procedure uniquely identifies infected individuals in a small number of tests. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, parallel searches are preferred. We report proof of concept experiments in which a positive sample was detected even when diluted a hundred-fold with negative samples. Using these methods, the costs of mass testing could be reduced by a factor of ten to a hundred or more. If infected individuals are quickly and effectively quarantined, the prevalence will fall and so will the costs of regularly testing everyone. Such a strategy provides a possible pathway to the longterm elimination of SARS-CoV-2 .
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Original: In this work we made use of a stochastic SEIR%DIFDELCMD < {\em %%%  Susceptible-Exposed-Infectious-Recovered model and adapted it conveniently to describe the propagation of COVID-19 during a football tournament.
Revised: In this work %DIFDELCMD < {\em %%% Susceptible-Exposed-Infectious-Recovered model and adapted it conveniently to describe the propagation of COVID-19 during a football tournament.
Edit intent: coherence
Original: In this work we made use of a stochastic SEIR%DIFDELCMD < {\em %%%  Susceptible-Exposed-Infectious-Recovered model and adapted it conveniently to describe the propagation of COVID-19 during a football tournament.
Revised: In this work we made use of a stochastic SEIR%DIFDELCMD < {\em %%% , we present a stochastic discrete-time SEIR ( Susceptible-Exposed-Infectious-Recovered model and adapted it conveniently to describe the propagation of COVID-19 during a football tournament.
Edit intent: meaning-changed
Original: In this work we made use of a stochastic SEIR%DIFDELCMD < {\em %%%  Susceptible-Exposed-Infectious-Recovered model and adapted it conveniently to describe the propagation of COVID-19 during a football tournament.
Revised: In this work we made use of a stochastic SEIR%DIFDELCMD < {\em %%% Susceptible-Exposed-Infectious-Recovered ) model adapted to describe the propagation of COVID-19 during a football tournament.
Edit intent: coherence
Original:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" ) , which is currently  stopped with 11 fixtures remaining.
Revised:  Specifically, we are concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" ) , which is currently stopped with 11 fixtures remaining.
Edit intent: fluency
Original:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" ) , which is currently  stopped with 11 fixtures remaining.
Revised:  Specifically, we were concerned about the re-start of the Spanish national football league %DIFDELCMD < {\it %%% "La Liga" ) , which is currently stopped with 11 fixtures remaining.
Edit intent: fluency
Original:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" ) , which is currently  stopped with 11 fixtures remaining.
Revised:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% ) , which is currently stopped with 11 fixtures remaining.
Edit intent: clarity
Original:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" ) , which is currently  stopped with 11 fixtures remaining.
Revised:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" , La Liga , which is currently stopped with 11 fixtures remaining.
Edit intent: meaning-changed
Original:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" ) , which is currently  stopped with 11 fixtures remaining.
Revised:  Specifically, we were concerned about the re-start of the Spanish national football league (%DIFDELCMD < {\it %%% "La Liga" ) , which is currently -May 2020- stopped with 11 fixtures remaining.
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Original:  Our model included two additional states of an individual, namely, confined and quarantined, which can be reached when an individual  has undergone a COVID-19 test with a positive result.
Revised:  Our model included two additional states of an individual, namely, confined and quarantined, which can be reached when an individual presents COVID-19 symptoms or has undergone a COVID-19 test with a positive result.
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Original:  The model also accounted for the interaction dynamics of players, considering three different sources of infection: (i) the player social circle, (ii) the interaction with his/her team colleagues during training sessions and (iii) the interaction with rivals during a match.
Revised:  The model also accounts for the interaction dynamics of players, considering three different sources of infection: (i) the player social circle, (ii) the interaction with his/her team colleagues during training sessions and (iii) the interaction with rivals during a match.
Edit intent: fluency
Original:  The model also accounted for the interaction dynamics of players, considering three different sources of infection: (i) the player social circle, (ii) the interaction with his/her team colleagues during training sessions and (iii) the interaction with rivals during a match.
Revised:  The model also accounted for the interaction dynamics of players, considering three different sources of infection: the player social circle, (ii) the interaction with his/her team colleagues during training sessions and (iii) the interaction with rivals during a match.
Edit intent: fluency
Original:  The model also accounted for the interaction dynamics of players, considering three different sources of infection: (i) the player social circle, (ii) the interaction with his/her team colleagues during training sessions and (iii) the interaction with rivals during a match.
Revised:  The model also accounted for the interaction dynamics of players, considering three different sources of infection: (i) the player social circle, the contact with his/her team colleagues during training sessions and (iii) the interaction with rivals during a match.
Edit intent: clarity
Original:  The model also accounted for the interaction dynamics of players, considering three different sources of infection: (i) the player social circle, (ii) the interaction with his/her team colleagues during training sessions and (iii) the interaction with rivals during a match.
Revised:  The model also accounted for the interaction dynamics of players, considering three different sources of infection: (i) the player social circle, (ii) the interaction with his/her team colleagues during training sessions , and the interaction with rivals during a match.
Edit intent: fluency
Original:  Second, we simulated the effects of increasing the frequency of tests, whose sensitivity was also analyzed. Finally, we discussed different strategies to minimize the probability that COVID-19 propagates in case the season of "La Liga " were re-started after the current lockdown.
Revised:  Following our findings, we finally present a variety of strategies to minimize the probability that COVID-19 propagates in case the season of "La Liga " were re-started after the current lockdown.
Edit intent: clarity
Original:  We translated two large English NLI datasets into Turkish and had a team of experts validate their quality. As examples of the new issues that these datasetshelp us address, we assess the value of Turkish-specific embeddings and the importance of morphological parsing for developing robust Turkish NLI models .
Revised:  We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly .
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Original:  Comprehensive experiments over three fiction and non-fiction datasets demonstrate that recently introduced large-scale language models, such as GPT-2 and Grover, despite their impressive generation performance, are not sufficient in generating coherent narratives for the given outline, and dynamic plot state tracking is important for composing narratives with tighter, more consistent plots.
Revised:  Comprehensive experiments over three fiction and non-fiction datasets demonstrate that large-scale language models, such as GPT-2 and Grover, despite their impressive generation performance, are not sufficient in generating coherent narratives for the given outline, and dynamic plot state tracking is important for composing narratives with tighter, more consistent plots.
Edit intent: clarity
Original: We introduce the task of scientific fact-checking. Given a corpus of scientific articles and a claim about a scientific finding, a fact-checking model must identify abstracts that support or refute the claim. In addition, it must provide rationales for its predictions in the form of evidentiary sentences from the retrieved abstracts. For this task, we introduce SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts , and annotated with labels and rationales.
Revised: We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that supports or refutes a given scientific claim, and to identify rationales justifying each decision. To study this task, we introduce SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts , and annotated with labels and rationales.
Edit intent: clarity
Original:  We present a baseline model and assess its performance on SciFact. We observe that, while fact-checking models trained on Wikipedia articles or political news have difficulty generalizing to our task, simple domain adaptation techniques represent a promising avenue for improvement. Finally, we provide initial results showing how our model can be used to verify claims relevant to COVID-19 on the CORD-19 corpus.
Revised:  We develop baseline models for SciFact, and demonstrate that these models benefit from combined training on a large dataset of claims about Wikipedia articles, together with the new SciFact data. We show that our claim verification system is able to identify plausible evidence for 23 / 36 claims relevant to COVID-19 on the CORD-19 corpus.
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Original: While probing is a common technique for identifying knowledge in the representations of pretrained models, it is unclear whether this technique can explain the downstream success of models like BERT which are trained end-to-end during finetuning. To address this question, we compare probing with a different measure of transferability: the decrease in finetuning performanceof a partially-reinitialized model .
Revised: How does language model pretraining help transfer learning? We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance. This method, partial reinitialization, involves replacing different layers of a pretrained model with random weights, then finetuning the entire model on the transfer task and observing the change in performance .
Edit intent: coherence
Original:  In addition, dataset size impacts layer transferability: the less finetuning data one has, the more important the middle and later layers of BERT become. Furthermore, BERT does not simply find a better initializer for individual layers; instead, interactions between layers matter and reordering BERT's layers prior to finetuning significantly harms evaluation metrics. These results provide a way of understanding the transferability of parameters in pretrained language models, revealing the fluidity and complexity of transfer learning in these models .
Revised:  Furthermore, the benefit of using pretrained parameters for a layer varies dramatically with finetuning dataset size: parameters that provide tremendous performance improvement when data is plentiful may provide negligible benefits in data-scarce settings. These results reveal the complexity of the transfer learning process, highlighting the limitations of methods that operate on frozen models or single data samples .
Edit intent: clarity
Original:  In a downstream NLP task, the sentences generated by our CGA model not only show significant improvements over a strong baseline, but also a classification performance very similar to real data. Furthermore, we are able to show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments .
Revised:  In a downstream NLP task, the sentences generated by our CGA model not only show significant improvements over a strong baseline, but also a classification performance often comparable to adding same amount of additional real data .
Edit intent: clarity
Original: Following previous work on automatic paraphrasing, we assess the feasibility of improving BLEU(Papineni et al. , 2002) using state-of-the-art neural paraphrasing techniques to generate additional references.
Revised: We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques to generate additional references.
Edit intent: clarity
Original: Following previous work on automatic paraphrasing, we assess the feasibility of improving BLEU(Papineni et al. , 2002) using state-of-the-art neural paraphrasing techniques to generate additional references. We explore the extent to which diverse paraphrases can adequately cover the space of valid translations and compare to an alternative approach of generating paraphrases constrained by MT outputs.
Revised: Following previous work on automatic paraphrasing, we assess the feasibility of improving BLEU(Papineni et al. , 2002) using state-of-the-art neural paraphrasing techniques , we study whether automatically generating additional diverse references can provide better coverage of the space of valid translations and compare to an alternative approach of generating paraphrases constrained by MT outputs.
Edit intent: clarity
Original:  We explore the extent to which diverse paraphrases can adequately cover the space of valid translations and compare to an alternative approach of generating paraphrases constrained by MT outputs. We compare both approaches to human-produced references in terms of diversity and the improvement in BLEU's correlation with human judgements of MT quality .
Revised:  We explore the extent to which diverse paraphrases can adequately cover the space of valid translations and thereby improve its correlation with human judgements of MT quality .
Edit intent: clarity
Original:  Our experiments on the WMT19 metrics tasks for all into-English language directions show that somewhat surprisingly, the addition of diverse paraphrases, even those produced by humans, leads to only small, inconsistent changes in BLEU's correlation with human judgments, suggesting that BLEU's ability to correctly exploit multiple references is limited
Revised:  Our experiments on the into-English language directions show that somewhat surprisingly, the addition of diverse paraphrases, even those produced by humans, leads to only small, inconsistent changes in BLEU's correlation with human judgments, suggesting that BLEU's ability to correctly exploit multiple references is limited
Edit intent: clarity
Original:  Our experiments on the WMT19 metrics tasks for all into-English language directions show that somewhat surprisingly, the addition of diverse paraphrases, even those produced by humans, leads to only small, inconsistent changes in BLEU's correlation with human judgments, suggesting that BLEU's ability to correctly exploit multiple references is limited
Revised:  Our experiments on the WMT19 metrics tasks for all into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU's ability to correctly exploit multiple references is limited
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