wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Specifically, a promising solution of exploiting a long short-term memory algorithm is first introduced in detail for an intElligent Safe-driving assessmenT (EST) scheme. To further facilitate the EST, we demonstrate how a distributed blockchain obtains adequate efficiency, trustworthiness and resilience with an adopted byzantine fault tolerance-based delegated proof-of-stake consensus mechanism.
Revised:  Specifically, a promising solution of exploiting a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then, we investigate how a distributed blockchain obtains adequate efficiency, trustworthiness and resilience with an adopted byzantine fault tolerance-based delegated proof-of-stake consensus mechanism.
Edit intent: clarity
Original:  To further facilitate the EST, we demonstrate how a distributed blockchain obtains adequate efficiency, trustworthiness and resilience with an adopted byzantine fault tolerance-based delegated proof-of-stake consensus mechanism.
Revised:  To further facilitate the EST, we demonstrate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism.
Edit intent: meaning-changed
Original:  First solutions to this problem have been proposed, such as TVM  or ISAMIR, which work on a loop-level representation of operators and rewrite the program before an instruction embedding into the operator is performed .
Revised:  First solutions to this problem have been proposed, such as TVM , UNIT or ISAMIR, which work on a loop-level representation of operators and rewrite the program before an instruction embedding into the operator is performed .
Edit intent: meaning-changed
Original:  First solutions to this problem have been proposed, such as TVM  or ISAMIR, which work on a loop-level representation of operators and rewrite the program before an instruction embedding into the operator is performed .
Revised:  First solutions to this problem have been proposed, such as TVM or ISAMIR, which work on a loop-levelrepresentation of operators and rewrite the program before an instruction embedding into the operator is performed .
Edit intent: fluency
Original:  First solutions to this problem have been proposed, such as TVM  or ISAMIR, which work on a loop-level representation of operators and rewrite the program before an instruction embedding into the operator is performed .
Revised:  First solutions to this problem have been proposed, such as TVM or ISAMIR, which work on a loop-level representation of operators and specify data layout and possible program transformations before the embedding into the operator is performed .
Edit intent: meaning-changed
Original:  First solutions to this problem have been proposed, such as TVM  or ISAMIR, which work on a loop-level representation of operators and rewrite the program before an instruction embedding into the operator is performed .
Revised:  First solutions to this problem have been proposed, such as TVM or ISAMIR, which work on a loop-level representation of operators and rewrite the program before an instruction embedding into the operator isperformed .
Edit intent: fluency
Original:  A detailed evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark suite shows that our approach can automatically generate code competitive to reference implementations , and furthermore that memory layout flexibilty can be beneficial for overall performance.
Revised:  An evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark suite shows that our approach can automatically generate code competitive to reference implementations , and furthermore that memory layout flexibilty can be beneficial for overall performance.
Edit intent: clarity
Original:  A detailed evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark suite shows that our approach can automatically generate code competitive to reference implementations , and furthermore that memory layout flexibilty can be beneficial for overall performance.
Revised:  A detailed evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark shows that our approach can automatically generate code competitive to reference implementations , and furthermore that memory layout flexibilty can be beneficial for overall performance.
Edit intent: clarity
Original:  A detailed evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark suite shows that our approach can automatically generate code competitive to reference implementations , and furthermore that memory layout flexibilty can be beneficial for overall performance.
Revised:  A detailed evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark suite shows that our approach canautomatically generate code competitive to reference implementations , and furthermore that memory layout flexibilty can be beneficial for overall performance.
Edit intent: fluency
Original:  A detailed evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark suite shows that our approach can automatically generate code competitive to reference implementations , and furthermore that memory layout flexibilty can be beneficial for overall performance. While the reference implementation achieves very low hardware utilization due to its fixed embedding strategy, we achieve a geomean speedup of up to x2 .49, while individual operators can improve as much as x238 .
Revised:  A detailed evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark suite shows that our approach can automatically generate code competitive to reference implementations . Further, we show that dynamically determining the data layoutbased on intrinsic and workload is beneficial for hardware utilization and performance. In cases where the reference implementationhas low hardware utilization due to its fixed embedding strategy, we achieve a geomean speedup of up to x2 .49, while individual operators can improve as much as x238 .
Edit intent: clarity
Original:  While the reference implementation achieves very low hardware utilization due to its fixed embedding strategy, we achieve a geomean speedup of up to x2 .49, while individual operators can improve as much as x238 .
Revised:  While the reference implementation achieves very low hardware utilization due to its fixed deployment strategy, we achieve a geomean speedup of up to x2 .49, while individual operators can improve as much as x238 .
Edit intent: clarity
Original:  While the reference implementation achieves very low hardware utilization due to its fixed embedding strategy, we achieve a geomean speedup of up to x2 .49, while individual operators can improve as much as x238 .
Revised:  While the reference implementation achieves very low hardware utilization due to its fixed embedding strategy, we achieve a geomean speedup of up to x2 .813, while individualoperators can improve as much as x238 .
Edit intent: meaning-changed
Original:  While the reference implementation achieves very low hardware utilization due to its fixed embedding strategy, we achieve a geomean speedup of up to x2 .49, while individual operators can improve as much as x238 .
Revised:  While the reference implementation achieves very low hardware utilization due to its fixed embedding strategy, we achieve a geomean speedup of up to x2 .49, while individual operators can improve as much as x170 .
Edit intent: meaning-changed
Original: We propose an enhancement technique of the Class Activation Mapping methods like Grad-CAM or Excitation Backpropagation, which presents visual explanations of decisions from CNN-based models. Our idea, called Gradual Extrapolation, can supplement any method that generates a heatmap picture by sharpening the output.
Revised: We propose an enhancement technique of the Class Activation Mapping methods like Grad-CAM or Excitation Backpropagation, which presents visual explanations of decisions from convolutional neural network-based models. The proposed idea, called Gradual Extrapolation, can supplement any method that generates a heatmap picture by sharpening the output.
Edit intent: meaning-changed
Original: Despite the fact that natural language conversations with machines represent one of the central objectives of AI , and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (ToD) -- i.e., conversations with an artificial agent with the aim of completing a concrete task -- is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese).
Revised: In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent to complete a concrete task. Although this technology represents one of the central objectives of AI , and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (ToD) -- i.e., conversations with an artificial agent with the aim of completing a concrete task -- is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese).
Edit intent: clarity
Original: Despite the fact that natural language conversations with machines represent one of the central objectives of AI , and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (ToD) -- i.e., conversations with an artificial agent with the aim of completing a concrete task -- is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese).
Revised: Despite the fact that natural language conversations with machines represent one of the central objectives of AI and has been the focus of ever more intense research and development efforts in conversational AI, task-oriented dialogue (ToD) -- i.e., conversations with an artificial agent with the aim of completing a concrete task -- is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese).
Edit intent: clarity
Original: Despite the fact that natural language conversations with machines represent one of the central objectives of AI , and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (ToD) -- i.e., conversations with an artificial agent with the aim of completing a concrete task -- is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese).
Revised: Despite the fact that natural language conversations with machines represent one of the central objectives of AI , and despite the massive increase of research and development efforts , it is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese).
Edit intent: clarity
Original: Despite the fact that natural language conversations with machines represent one of the central objectives of AI , and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (ToD) -- i.e., conversations with an artificial agent with the aim of completing a concrete task -- is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese).
Revised: Despite the fact that natural language conversations with machines represent one of the central objectives of AI , and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (ToD) -- i.e., conversations with an artificial agent with the aim of completing a concrete task -- is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of languages (e.g., English, Chinese).
Edit intent: clarity
Original:  In this work , we provide an extensive overview of existing efforts in multilingual ToD and analyse the factors preventing the development of truly multilingual ToD systems . We identify two main challenges that combined hinder the faster progress in multilingual ToD: (1) current state-of-the-art ToD models based on large pretrained neural language models are data hungry; at the same time (2) data acquisition for ToD use cases is expensive and tedious.
Revised:  In this work , we provide an extensive overview of existing efforts in multilingual ToD and analyse the factors preventing the development of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation. In fact, acquiring annotations or human feedback for each component of modular systems or for data-hungry end-to-end systems is expensive and tedious.
Edit intent: coherence
Original:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces.
Revised:  Hence, state-of-the-art approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces.
Edit intent: meaning-changed
Original:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces.
Revised:  Most existing approaches to multilingual ToD mostly rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces.
Edit intent: clarity
Original:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces.
Revised:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( almost exclusively English), either by means of (i) machine translation or (ii) multilingual representation spaces.
Edit intent: clarity
Original:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces.
Revised:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of machine translation or (ii) multilingual representation spaces.
Edit intent: fluency
Original:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces. However, such approaches are currently not a viable solution for a large number of low-resource languages without parallel data and /or limited monolingual corpora .
Revised:  Most existing approaches to multilingual ToD thus rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( in ToD, this is basically only English), either by means of (i) machine translation or multilingual representations. These approaches are currently not a viable solution for a large number of low-resource languages without parallel data and /or limited monolingual corpora .
Edit intent: clarity
Original:  However, such approaches are currently not a viable solution for a large number of low-resource languages without parallel data and /or limited monolingual corpora . Finally, we discuss critical challenges and potential solutions by drawing parallels between ToD and other cross-lingual and multilingual NLP research .
Revised:  However, such approaches are currently viable only for typologically similar languages and other cross-lingual and multilingual NLP research .
Edit intent: meaning-changed
Original:  Finally, we discuss critical challenges and potential solutions by drawing parallels between ToD and other cross-lingual and multilingual NLP research .
Revised:  Finally, we discuss critical challenges and potential solutions by drawing parallels between ToD and languages with parallel / monolingual corpora available. On the other hand, their effectiveness beyond these boundaries is doubtful or hard to assess due to the lack of linguistically diverse benchmarks (especially for natural language generation and end-to-end evaluation). To overcome this limitation, we draw parallels between components of the ToD pipeline and other NLP tasks, which can inspire solutions for learning in low-resource scenarios. Finally, we list additional challenges that multilinguality poses for related areas (such as speech and human-centred evaluation), and indicate future directions that hold promise to further expand language coverage and dialogue capabilities of current ToD systems .
Edit intent: meaning-changed
Original:  Due to the severity of the hate speech and offensive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate our method. However, the proposal may be applied to any other language or domain.
Revised:  Due to the severity of the hate speech and offensive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the proposed method. Nevertheless, our proposal may be applied to any other language or domain.
Edit intent: coherence
Original:  Hence, people are normally not only pay attention to  what their interlocutors are saying but also try to inspect their visual appearances, including faces, to  find any signs that indicate whether the person is telling the truth or not.
Revised:  Thus, people normally pay attention to what their interlocutors are saying but also try to inspect their visual appearances, including faces, to find any signs that indicate whether the person is telling the truth or not.
Edit intent: clarity
Original:  Hence, people are normally not only pay attention to  what their interlocutors are saying but also try to inspect their visual appearances, including faces, to  find any signs that indicate whether the person is telling the truth or not.
Revised:  Hence, people are normally not only pay attention to both what their interlocutors are saying but also try to inspect their visual appearances, including faces, to find any signs that indicate whether the person is telling the truth or not.
Edit intent: clarity
Original:  Hence, people are normally not only pay attention to  what their interlocutors are saying but also try to inspect their visual appearances, including faces, to  find any signs that indicate whether the person is telling the truth or not.
Revised:  Hence, people are normally not only pay attention to what their interlocutors are saying and also to their visual appearances, including faces, to find any signs that indicate whether the person is telling the truth or not.
Edit intent: clarity
Original:  Hence, people are normally not only pay attention to  what their interlocutors are saying but also try to inspect their visual appearances, including faces, to  find any signs that indicate whether the person is telling the truth or not.
Revised:  Hence, people are normally not only pay attention to what their interlocutors are saying but also try to inspect their visual appearances, including faces, to try to find any signs that indicate whether the person is telling the truth or not.
Edit intent: fluency
Original:  Using our collected dataset, we evaluated several types of machine learning based lie detector through generalize, personal and cross lie lie experiments. In these experiments, we showed the superiority of deep learning based model in recognizing the lie with best accuracyof 57\\%DIF < for generalized task and 63\\% when dealing with a single participant.
Revised:  Using our collected dataset, we evaluated several types of machine learning-based lie detectors in terms of their generalization, person-specific and cross-domain experiments. Our results show that models based on deep learning achieve the best accuracy, reaching up to 57\\%DIF < for generalized task and 63\\% when dealing with a single participant.
Edit intent: clarity
Original:  Approaching this issue requires established measures of fairness regarding the representation of various social groups in retrieved contents , as well as methods to mitigate such biases, particularly in the light of the advances in deep ranking models.
Revised:  Approaching this issue requires established measures of fairness in respect to the representation of various social groups in retrieved contents , as well as methods to mitigate such biases, particularly in the light of the advances in deep ranking models.
Edit intent: clarity
Original:  Approaching this issue requires established measures of fairness regarding the representation of various social groups in retrieved contents , as well as methods to mitigate such biases, particularly in the light of the advances in deep ranking models.
Revised:  Approaching this issue requires established measures of fairness regarding the representation of various social groups in retrieval results , as well as methods to mitigate such biases, particularly in the light of the advances in deep ranking models.
Edit intent: clarity
Original:  Second , we propose an adversarial bias mitigation approach applied to the state-of-the-art Bert rankers , which jointly learns to predict relevance and remove protected attributes.
Revised:  Second , we propose AdvBert, a ranking model achieved by adapting adversarial bias mitigation approach applied to the state-of-the-art Bert rankers , which jointly learns to predict relevance and remove protected attributes.
Edit intent: meaning-changed
Original:  We conduct experiments on two passage retrieval collections ( MS MARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a selected subset of queries regarding gender attributes.
Revised:  We conduct experiments on two passage retrieval collections ( MSMARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a selected subset of queries regarding gender attributes.
Edit intent: fluency
Original:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Revised:  Our results on the MSMARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Edit intent: fluency