wanyu/IteraTeR_full_sent huggingface dataset:
Original: Few resources represent implicit roles for natural language understanding , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form.
Revised: Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for natural language understanding , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form.
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Original:  In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013).
Revised:  This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013).
Edit intent: clarity
Original:  In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013). Our design aligns with O'Gorman (2019)'s implicit role interpretation in a linguistic and computational model.
Revised:  In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer .
Edit intent: clarity
Original:  The proposed implicit argument categorisation set consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set.
Revised:  The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set.
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Original:  We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . It is anticipated that our study will inspire tailored design of implicit role annotation in other meaning representation frameworks, and stimulate research in relevant fields, such as coreference resolution and question answering .
Revised:  We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes .
Edit intent: clarity
Original:  This structure, coined as the GT-model, allows us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas , and more importantly, how it may co-evolve with city growth and mobility pattern shifts. We observe the emergence of several congestion regimes, with abrupt transitions betwenn them, related to the entanglement of arterial and urban local roads.
Revised:  This structure, coined as the GT-model, allows us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas of monocentric cities. The model predicts the emergence of several congestion regimes, with abrupt transitions betwenn them, related to the entanglement of arterial and urban local roads.
Edit intent: clarity
Original:  To help unveiling this spatial dependence of the betweenness , we introduce a simple model composed of a grid connected to a set of tree graphs. This structure , coined as the GT-model, allows us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas of monocentric cities .
Revised:  To help unveiling this spatial dependence of the betweenness , we introduce a simple model composed of a dense urban center connected to an arboreal periphery. These models , coined as the GT-model, allows us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas of monocentric cities .
Edit intent: meaning-changed
Original:  This structure , coined as the GT-model, allows us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas of monocentric cities .
Revised:  This structure , coined as GT and DT-MST models, allow us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas of monocentric cities .
Edit intent: meaning-changed
Original:  This structure , coined as the GT-model, allows us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas of monocentric cities . The model predicts the emergence of several congestion regimes, with abrupt transitions between them, related to the entanglement of arterial and urban local roads. The existence of these regimes and transitions is in agreement with the multiple abrupt phase transitions found for real cities, and represents an important step towards the understanding and optimization of traffic dynamics over coupled road networks .
Revised:  This structure , coined as the GT-model, allows us to analytically describe in terms of betweenness, how and why congestion emerges in particular geographical areas of monocentric cities and, subsequently, to describe the congestion regimes and the factors that promote the appearance of their abrupt transitions. We show that the fundamental ingredient behind the observed abrupt transitions is in agreement with the multiple abrupt phase transitions found for real cities, and represents an important step towards the understanding and optimization of traffic dynamics over coupled road networks .
Edit intent: coherence
Original:  The existence of these regimes and transitions is in agreement with the multiple abrupt phase transitions found for real cities, and represents an important step towards the understanding and optimization of traffic dynamics over coupled road networks .
Revised:  The existence of these regimes and transitions is the spatial separation between the urban center and the periphery, and the number of separate areas that form the periphery. Elaborating on the implications of our results, we show that they may have an influence on the design and optimization of traffic dynamics over coupled road networks .
Edit intent: meaning-changed
Original:  The existence of these regimes and transitions is in agreement with the multiple abrupt phase transitions found for real cities, and represents an important step towards the understanding and optimization of traffic dynamics over coupled road networks .
Revised:  The existence of these regimes and transitions is in agreement with the multiple abrupt phase transitions found for real cities, and represents an important step towards the understanding and optimization of road networks regarding urban growth and the management of daily traffic dynamics .
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Original:  However, it is unclear whether these sampling strategies are the best choice for candidate generation in recommender system, where we face severe selection bias in the training data with an extremely large candidate set and rich features of various types .
Revised:  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the training data with an extremely large candidate set and rich features of various types .
Edit intent: meaning-changed
Original:  However, it is unclear whether these sampling strategies are the best choice for candidate generation in recommender system, where we face severe selection bias in the training data with an extremely large candidate set and rich features of various types .
Revised:  However, it is unclear whether these sampling strategies are the best choice for candidate generation in recommender system, where we face severe selection bias in the long run, as it aims to faithfully fit the history records, and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
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Original:  In this paper, we propose CLRec, a Contrastive Learning paradigm for large scale candidate generation in Recommender systems. CLRec employs a queue based buffer to hold previous examples or representations as negative labels, within which a contrastive loss is optimized. This framework achieves better performance while requiring no explicit sampling , providing great efficiency in encoding rich types of features on the label side. We analyze both theoretically and empirically that CLRec can in fact alleviate the selection bias, leading to a more diversified and fairer recommendation. We deploy CLRec in Taobao and conduct online A/B test on a traffic-intensive scenario, showing a large margin improvementon both performance and efficiency, as well as a dramatic reduction on the rich-get-richer phenomenon .
Revised:  In this paper, we propose CLRec, a Contrastive Learning paradigm successfully deployed in a real-world massive recommender system, for alleviating exposure unfairness in DCG. We theoretically prove that a popular choice of contrastive loss is equivalent to reducing the exposure bias via inverse propensity scoring, which complements previous understanding of contrastive learning. We further implement a good sampling distribution and reuse most of the computation when encoding rich features for both positive and negative items, by employing a fix-sized queue to store items (and reuse the computed representations) from previous batches, where the queue serves as the negative sampler. In contrast, previous state-of-art DCG approaches do not use rich features for the negative items due to high computational costs, since the number of negative samples needs to be large. Extensive offline analyses and online A/B test on a traffic-intensive scenario, showing a large margin improvementon both performance and efficiency, as well as a dramatic reduction on the rich-get-richer phenomenon .
Edit intent: meaning-changed
Original:  We deploy CLRec in Taobao and conduct online A/B test on a traffic-intensive scenario, showing a large margin improvementon both performance and efficiency, as well as a dramatic reduction on the rich-get-richer phenomenon .
Revised:  We deploy CLRec in Taobao and conduct online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction on the rich-get-richer phenomenon .
Edit intent: meaning-changed
Original:  We deploy CLRec in Taobao and conduct online A/B test on a traffic-intensive scenario, showing a large margin improvementon both performance and efficiency, as well as a dramatic reduction on the rich-get-richer phenomenon .
Revised:  We deploy CLRec in Taobao and conduct online A/B test on a traffic-intensive scenario, showing a large margin improvementon both performance and efficiency, as well as a dramatic reduction in the Matthew effect .
Edit intent: meaning-changed
Original:  However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items .
Revised:  However, live recommender systems face severe exposure bias and have a vocabulary several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items .
Edit intent: clarity
Original:  However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items .
Revised:  However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, implying that MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items .
Edit intent: fluency
Original:  However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items .
Revised:  However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples .
Edit intent: coherence
Original:  In this paper, we introduce CLRec, a Contrastive Learning paradigm that has been successfully deployed in a real-world massive recommender system, to alleviate exposure bias in DCG. We theoretically prove that a popular choice of contrastive loss is equivalently reducing the exposure bias via inverse propensity scoring , which provides a new perspective on the effectiveness of contrastive learning.
Revised:  In this paper, we theoretically prove that a popular choice of contrastive loss is equivalently reducing the exposure bias via inverse propensity scoring , which provides a new perspective on the effectiveness of contrastive learning.
Edit intent: coherence
Original:  We further employ a fixed-size queue to store the items' representations computed in previously processed batches, and use the queue to serve as an effective sampler of negative examples. This queue-based design provides great efficiency in incorporating rich features of the thousand negative items per batch thanks to computation reuse. Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement , including a dramatic reduction in the Matthew effect.
Revised:  Based on the theoretical discovery, we design CLRec, a contrastive learning method to improve DCG in terms of fairness, effectiveness and efficiency in recommender systems with extremely large candidate size. We further improve upon CLRec and propose Multi-CLRec, for accurate multi-intention aware bias reduction. Our methods have been successfully deployed in Taobao, where at least four-month online A/B tests in Mobile Taobao demonstrate substantial improvement , including a dramatic reduction in the Matthew effect.
Edit intent: meaning-changed
Original: 54\% as of Apr 20, 2020. The infection ratio out of the US population is estimated to be 0.53\%, implying a case mortality rate at 2.85\% which is close to the 3.4\% suggested by the WHO  .
Revised: 54\% as of Apr 20, 2020, while the infection ratio out of the US population is estimated to be 0.53\%, implying a case mortality rate at 2.85\% which is close to the 3.4\% suggested by the WHO .
Edit intent: coherence
Original:  The infection ratio out of the US population is estimated to be 0.53\%, implying a case mortality rate at 2.85\% which is close to the 3.4\% suggested by the WHO  .
Revised:  The infection ratio out of the US population is estimated to be 0.53\%, implying a case mortality rate at 2.85\% which is close to the 3.4\% suggested by the WHO in Mar 2020. Towards the end of the summer of 2020, the overall infection ratio of the US rises to 2.49\% while the case mortality decreases to 2.09\%, and the ratio of asymptomatic cases out of all infected cases reduces from the pre-summer 35-40\% to around 20-25\% .
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Original:  Here, we investigate whether English intermediate-task training is still helpful on non-English target tasks in a zero-shot cross-lingual setting. Using a set of 7 intermediate language understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on 9 target tasks from the XTREME benchmark.
Revised:  Here, we investigate whether English intermediate-task training is still helpful on non-English target tasks . Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on 9 target tasks from the XTREME benchmark.
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Original:  Using SQuAD for intermediate training achieves the best results across target tasks, with an average improvement of 8.4 points on development sets. Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Revised:  MNLI, SQuAD and HellaSwag achieve the best overall results as intermediate tasks, while multi-task intermediate offers small additional improvements. Using our best intermediate-task models for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Edit intent: meaning-changed
Original:  Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Revised:  Selecting the best intermediate task model for each target task, we obtain a 5.4 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Edit intent: meaning-changed
Original:  Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Revised:  Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting the state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Edit intent: clarity
Original:  Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Revised:  Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art as of June 2020. We also investigate continuing multilingual MLM during intermediate-task training offer significant improvements .
Edit intent: meaning-changed
Original:  Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training offer significant improvements .
Revised:  Selecting the best intermediate task model for each target task, we obtain a 6.1 point improvement over XLM-R Large on the XTREME benchmark, setting a new state of the art . Finally, we show that neither multi-task intermediate-task training nor continuing multilingual MLM during intermediate-task training and using machine-translated intermediate-task data, but neither consistently outperforms simply performing English intermediate-task training .
Edit intent: meaning-changed
Original:  The contribution here is a classification and interpretation carried out with a set of numerical simulations corresponding to networks trained for AND, OR, XOR tasks, and a Flip Flop , by doing the description of the dynamics.
Revised:  The contribution here is the description of the dynamics through the classification and interpretation carried out with a set of numerical simulations corresponding to networks trained for AND, OR, XOR tasks, and a Flip Flop , by doing the description of the dynamics.
Edit intent: clarity
Original:  The contribution here is a classification and interpretation carried out with a set of numerical simulations corresponding to networks trained for AND, OR, XOR tasks, and a Flip Flop , by doing the description of the dynamics.
Revised:  The contribution here is a classification and interpretation carried out with a set of numerical simulations corresponding to networks trained to process time stimuli with AND, OR, XOR tasks, and a Flip Flop , by doing the description of the dynamics.
Edit intent: meaning-changed
Original:  The contribution here is a classification and interpretation carried out with a set of numerical simulations corresponding to networks trained for AND, OR, XOR tasks, and a Flip Flop , by doing the description of the dynamics.
Revised:  The contribution here is a classification and interpretation carried out with a set of numerical simulations corresponding to networks trained for AND, OR, XOR , and Flip Flop tasks. The patterns in the distribution of the eigenvalues of the recurrent weight matrix ware studied and properly related to the dynamics.
Edit intent: meaning-changed