wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Using simulated experiments, we demonstrate that MT model stealing is possible even when imitation models have different input data or architectures than their victims . Applying these ideas, we train imitation models that reach within 0.6 BLEU of three production MT systems on both high-resource and low-resource language pairs.
Revised:  Using simulated experiments, we demonstrate that MT model stealing is possible even when imitation models have different input data or architectures than their target models . Applying these ideas, we train imitation models that reach within 0.6 BLEU of three production MT systems on both high-resource and low-resource language pairs.
Edit intent: clarity
Original: We present WiC-TSV, a new multi-domain evaluation benchmark for  Target Sense Verification of Words in Context . The benchmark is a new framework for evaluating Word Sense Disambiguation and Entity Linking, which is different from conventional benchmarks for it covering various domains, while being independent of external sense inventories  .
Revised: We present WiC-TSV, a new multi-domain evaluation benchmark for Target Sense Verification of Words in Context which grounds its uniqueness in the formulation as a binary classification task thus being independent of external sense inventories .
Edit intent: meaning-changed
Original:  The benchmark is a new framework for evaluating Word Sense Disambiguation and Entity Linking, which is different from conventional benchmarks for it covering various domains, while being independent of external sense inventories  .
Revised:  The benchmark is a new framework for evaluating Word Sense Disambiguation and Entity Linking, which is different from conventional benchmarks for it covering various domains, while being independent of external sense inventories , and the coverage of various domains .
Edit intent: coherence
Original:  Fighting this infodemic is now ranked second on the list of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic.
Revised:  Fighting this infodemic is ranked second in the list of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic.
Edit intent: clarity
Original:  The fight requires solving a number of  problems such as identifying tweets containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few.
Revised:  Addressing the issue requires solving a number of problems such as identifying tweets containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few.
Edit intent: clarity
Original:  The fight requires solving a number of  problems such as identifying tweets containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few.
Revised:  The fight requires solving a number of challenging problems such as identifying tweets containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few.
Edit intent: clarity
Original:  The fight requires solving a number of  problems such as identifying tweets containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few.
Revised:  The fight requires solving a number of problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few.
Edit intent: clarity
Original:  These are challenging problems, and some of them have been studied previously, but typically in isolation. Here, we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i)focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic  .
Revised:  Thus, here we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i)focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic .
Edit intent: coherence
Original:  Here, we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i)focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic  .
Revised:  Here, we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i)focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic . Finally, we show strong evaluation results using state-of-the-art Transformers, thus confirming the practical utility of the annotation schema and of the dataset .
Edit intent: meaning-changed
Original: Recent transformer-based open-domain dialogue agents are trained by reference responses in a fully supervised scenario. Such agents often display inconsistent personalities as training data potentially contain contradictory responses to identical input utterances and no persona-relevant criteria are used in their training losses. We propose a novel approach to train transformer-based dialogue agents using actor-critic reinforcement learning . We define a new reward function to assess generated responses in terms of persona consistency , topic consistency, and fluency. Our reference-agnostic reward relies only on a dialogue history and a persona defined by a list of facts . Automatic and human evaluations on the PERSONACHAT dataset show that our proposed approach increases the rate of persona-consistent responses compared with its peers that are trained in a fully supervised scenario using reference responses.
Revised: Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona. These models are trained with fully supervised learning where the objective function barely captures factual consistency. We propose to fine-tune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility. Our automatic and human evaluations on the PERSONACHAT dataset show that our proposed approach increases the rate of persona-consistent responses compared with its peers that are trained in a fully supervised scenario using reference responses.
Edit intent: coherence
Original:  Automatic and human evaluations on the PERSONACHAT dataset show that our proposed approach increases the rate of persona-consistent responses compared with its peers that are trained in a fully supervised scenario using reference responses.
Revised:  Automatic and human evaluations on the PERSONACHAT dataset show that our proposed approach increases the rate of responses that are factually consistent with persona facts over its supervised counterpart while retaining the language quality of responses.
Edit intent: meaning-changed
Original: The main goal behind state-of-the-art pretrained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer.
Revised: The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer.
Edit intent: fluency
Original:  MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition  and achieves competitive results on question answering.
Revised:  MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering.
Edit intent: meaning-changed
Original: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals . In this study, we revised 55 off-the-shelf devices in recognition and analysis of emotion, stress, meditation, sleep, and physical activity , especially in field studies .
Revised: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease (CKD) suffering from bipolar disorders. For the need of a large-scale field study, we revised 55 off-the-shelf devices in recognition and analysis of emotion, stress, meditation, sleep, and physical activity , especially in field studies .
Edit intent: meaning-changed
Original:   Empatica E4 and Microsoft Band 2 are good at emotion, stress, and together with Oura Ring at sleep research. Apple, Samsung , Garmin, and Fossil smart watches are proper in activity examination, while Muse and DREEM EEG headbands are suitable for meditation .
Revised:  Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states .
Edit intent: clarity
Original: Current social distancing measures to impede COVID-19 (such as shelter-in-place) are economically unsustainable in the long term. Models are needed to understand the implications of possible relaxation options for these measures.
Revised: Current social distancing measures to impede COVID-19 infections, calling for a roll-back or substantial revisiting of these policies in many states. The situation is suggestive of difficulties modeling the impact of partial distancing/re-opening policies on future epidemic spread for purposes of choosing safe alternatives. More specifically, one needs to understand the implications of possible relaxation options for these measures.
Edit intent: meaning-changed
Original:  Models are needed to understand the implications of possible relaxation options for these measures.We report such models, together with corresponding parameter estimation techniques and prediction outcomes, borrowing insights from another domain; namely, information cascades. Our models assume that the containment of the virus into isolated locales is no longer possible. Instead, we explore options that reduce the rate of spread. We predict COVID-19 contagion trends in different geographic regions under a "what if " scenario to understand the effects of potential policy decisions on regional trends.
Revised:  Models are needed to understand the impact of manipulating the availability of social interaction venues (e.g., schools, workplaces, and retail establishments) on virus spread. We introduce a model, inspired by social networks research, that answers the above question. Our model compartmentalizes interaction venues into categories we call mixing domains, enabling one to predict COVID-19 contagion trends in different geographic regions under a "what if " scenario to understand the effects of potential policy decisions on regional trends.
Edit intent: clarity
Original:  We predict COVID-19 contagion trends in different geographic regions under a "what if " scenario to understand the effects of potential policy decisions on regional trends. Our model allows experimentation with other policies that, we hope, can contribute to socially viable outcomes both in terms of health system capacity and economic impact .
Revised:  We predict COVID-19 contagion trends in different geographic regions under different what if assumptions on partial re-opening of individual domains .
Edit intent: meaning-changed
Original:  We apply our model to over 30 highly impacted states in the US and publish the results at URL
Revised:  We apply our model to over 30 highly impacted states showing (i) how accurately it predicts the extent of current resurgence (from available policy descriptions), and (ii) what alternatives might be more effective at mitigating the second wave. We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i.e., in lieu of social distancing). Our models predict that the benefits of (mandatory) testing out-shadow the benefits of partial venue closure, suggesting that perhaps more efforts should be directed to such a mitigation strategy.
Edit intent: meaning-changed
Original:  The complete historical bookkeeping and its wide availability opens up an unprecedented possibility, i.e. , that of understanding the evolution of their network structure while gaining useful insight on the relationships between user  behaviour and cryptocurrency pricing in exchange markets.
Revised:  The complete historical bookkeeping and its wide availability opens up an unprecedented possibility, i.e. that of understanding the evolution of their network structure while gaining useful insight on the relationships between user behaviour and cryptocurrency pricing in exchange markets.
Edit intent: fluency
Original:  The complete historical bookkeeping and its wide availability opens up an unprecedented possibility, i.e. , that of understanding the evolution of their network structure while gaining useful insight on the relationships between user  behaviour and cryptocurrency pricing in exchange markets.
Revised:  The complete historical bookkeeping and its wide availability opens up an unprecedented possibility, i.e. , that of understanding the evolution of their network structure while gaining useful insight on the relationships between user ' behaviour and cryptocurrency pricing in exchange markets.
Edit intent: fluency
Original:  A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Revised:  The picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Edit intent: clarity
Original:  A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Revised:  A common picture that emerges is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Edit intent: clarity
Original:  A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Revised:  A common picture that emerges out of analysing them all is that of system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Edit intent: fluency
Original:  A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Revised:  A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse and whose mesoscopic URLanization is characterised by the presence of a statistically-significant core-periphery structure.
Edit intent: clarity
Original:  A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure.
Revised:  A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse , and whose URLanization at the mesoscopic level is characterised by the presence of an increasingly significant core-periphery structure.
Edit intent: clarity
Original:  This task requires the system to query a knowledge base (KB)  and use the retrieved results to fulfil user needs. Predicting the KB queries is crucial and can lead to severe under-performance if made incorrectly. KB queries are usually annotated in real-world datasets and are learnt using supervised approaches to achieve acceptable task completion. This need for query annotations prevents TOD systems from easily adapting to new domains. In this paper, we propose a novel problem of learning end-to-end TOD systems using dialogs that do not contain KB query annotations. Our approach first learns to predict the KB queries using reinforcement learning (RL) and then learns the end-to-end system using the predicted queries .
Revised:  This task requires the system to query a knowledge base (KB) and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries -- these annotations can be time consuming, and expensive. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) and then learns the end-to-end system using the predicted queries .
Edit intent: coherence
Original:  Our approach first learns to predict the KB queries using reinforcement learning (RL) and then learns the end-to-end system using the predicted queries . However, predicting the correct query in TOD systems is uniquely plagued by correlated attributes , in which, due to data bias, certain attributes always occur together in the KB. This prevents the RL system to generalise and accuracy suffers as a result. We propose Correlated Attributes Resilient RL (CARRL), a modification to the RL gradient estimation, which mitigates the problem of correlated attributes and predicts KB queries better than existing weakly supervised approaches. Finally, we compare the performance of our end-to-end system trained using predicted queries to a system trained using annotated gold queries .
Revised:  Our approach first learns to predict the KB queries using reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in subsequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but important modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation .
Edit intent: meaning-changed
Original:  Our algorithm creates explicit representations for each entity, where given a new sentence, spans are proposed and subsequently scored against each entity representation, leading to emergent clusters. Our approach is end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts .
Revised:  Our algorithm creates explicit representations for each entity, where given a new sentence, our end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts .
Edit intent: clarity
Original:  Our approach is end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts .
Revised:  Our approach is end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity's representations before being URLotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3\% relative loss in F1 on OntoNotes 5.0 .
Edit intent: meaning-changed
Original:  Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction : substantial gains on average against other models and consistent improvements over HANs without structure-tags .
Revised:  Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction .
Edit intent: clarity
Original:  The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model . It gains 4.7\% accuracy over the best of both models on the computation and language domain and loses 2.4\% against the best of both on the machine learning domain . Compared to plain HANs, accuracy increases on both domains, with 1.5\% and 2\% respectively .
Revised:  The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs . Compared to plain HANs, accuracy increases on both domains, with 1.5\% and 2\% respectively .
Edit intent: clarity