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_id stringlengths 1 5	task stringclasses 7 values	src stringlengths 22 884	tgt stringlengths 0 697
81500	clarity	Rewrite the sentence more clearly: We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original KG while significantly deviating from the original semantics and structure.	We show that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original KG while significantly deviating from the original semantics and structure.
81501	clarity	Make the text more understandable: We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original KG while significantly deviating from the original semantics and structure.	We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original KG while significantly deviating from the original KG's semantics and structure.
81502	clarity	Write a better readable version of the sentence: Our findings raise doubts about KG-augmented models' ability to leverage KG information and provide plausible explanations.	Our findings raise doubts about KG-augmented models' ability to reason about KG information and provide plausible explanations.
81503	clarity	Rewrite this sentence for clarity: Our findings raise doubts about KG-augmented models' ability to leverage KG information and provide plausible explanations.	Our findings raise doubts about KG-augmented models' ability to leverage KG information and give sensible explanations.
81504	clarity	Rewrite this sentence for clarity: Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.	Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.
81505	clarity	Write a readable version of the sentence: Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.	Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) for commonsense reasoning tasks, like question answering (QA).
81506	clarity	Write a readable version of the sentence: However, these methods rely on quality and contextualized knowledge structures (i.e., fact triples) that are retrieved at the pre-processing stage but overlook challenges caused by incompleteness of a KG, limited expressiveness of its relations, and retrieved facts irrelevant to the reasoning context. In this paper, we present a novel neural-symbolic model, named Hybrid Graph Network (HGN), which jointly generates feature representations for new triples (as a complement to existing edges in the KG), determines the relevance of the triples to the reasoning context, and learns graph module parameters for encoding the relational information.	However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge. To address these issues, we propose Hybrid Graph Network (HGN), which jointly generates feature representations for new triples (as a complement to existing edges in the KG), determines the relevance of the triples to the reasoning context, and learns graph module parameters for encoding the relational information.
81507	clarity	Clarify: We show marked improvement on three commonsense reasoning benchmarks and demonstrate the superiority of the learned graph structures with user studies.	We show marked improvement on three commonsense reasoning benchmarks and a user study of fact validness and helpfulness.
81508	clarity	Make this easier to read: It has a long history in the field of natural language processing (NLP), but recently it has gained significant attention thanks to the promising performance brought by deep learning models.	It has a long history in the field of natural language processing (NLP), and recently has re-gained significant attention thanks to the promising performance brought by deep learning models.
81509	clarity	Make this sentence better readable: It has a long history in the field of natural language processing (NLP), but recently it has gained significant attention thanks to the promising performance brought by deep learning models.	It has a long history in the field of natural language processing (NLP), but recently it has gained significant attention thanks to the promising performance brought by deep neural models.
81510	clarity	Clarify this text: Overall, we have covered the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data.	We discuss the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data.
81511	clarity	Clarify this paragraph: Overall, we have covered the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data.	Overall, we have covered the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data.
81512	clarity	Change to clearer wording: We also provide discussions a variety of important topics regarding TST, which can shed light on new development in this field.	We also provide discussions a variety of important topics regarding the future development of TST.
81513	clarity	Clarify: Experiments on machine translation and abstract text summarization demonstrate the effectiveness of our proposed approach -- we obtain an average F1 of around 0.6 across all the benchmark datasets and achieve significant improvements in sentence-level hallucination scoring compared to baseline methods.	Experiments on machine translation and abstract text summarization demonstrate the effectiveness of our proposed approach -- we obtain an average F1 of around 0.6 across all the benchmark datasets and achieve significant improvements over strong baseline methods.
81514	clarity	Make this easier to read: We also release our annotated data and code for future researchat URL	We will release our annotated data and code for future researchat URL
81515	clarity	Clarify this sentence: Recently, contextualized word embeddings outperform static word embeddings on many NLP tasks. However, we still do not know much about the mechanism inside these representations. Do they have any common patterns? If so, where do these patterns come from? We find that almost all the contextualized word vectors of BERT and RoBERTa have a commonpattern.	In this work, we demonstrate that the contextualized word vectors of BERT and RoBERTa have a commonpattern.
81516	clarity	Write a better readable version of the sentence: For BERT, the 557^{th neuron-level method to analyze where these "tails" come from. We find that these "tails" are closely related to the positional information.	For BERT, the 557^{th neuron-level analysis method, which reveals that the outliers are closely related to the positional information.
81517	clarity	Clarify this paragraph: We find that these "tails" are closely related to the positional information.	We find that these "tails" are closely related to information captured by positional embeddings.
81518	clarity	Clarify this text: Our theory provides precise reasons answering why or why not a triple is correct.	Our theory provides precise reasons explaining why or why not a triple is correct.
81519	clarity	Write a better readable version of the sentence: Results show that the EARDict model achieves new state-of-the-art performances on benchmark knowledge graph completion tasks, including a Hits@10 score of 80.38 percent on WN18RR.	Results show that our EARDict model significantly outperforms all the benchmark models on two large datasets of knowledge graph completion tasks, including a Hits@10 score of 80.38 percent on WN18RR.
81520	clarity	Write a clarified version of the sentence: Results show that the EARDict model achieves new state-of-the-art performances on benchmark knowledge graph completion tasks, including a Hits@10 score of 80.38 percent on WN18RR.	Results show that the EARDict model achieves new state-of-the-art performances on benchmark knowledge graph completion, including achieving a Hits@10 score of 80.38 percent on WN18RR.
81521	clarity	Clarify the sentence: Results show that our EARDict model significantly outperforms all the benchmark models on two large datasets of knowledge graph completion, including achieving a Hits@10 score of 96.6 percent on WN18RR.	Results show that our EARDict model significantly outperforms all the benchmark models on two large datasets of knowledge graph completion. Especially, our model achieves a Hits@10 score of 96.6 percent on WN18RR.
81522	clarity	Write a clearer version for the sentence: Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows the same hardware-agnostic application codes of the HPC kernels, without any change, to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score.	Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for the host program to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score.
81523	clarity	Clarify this paragraph: Many online comments on social media platforms are hateful, humorous, or sarcastic.	Sentiment analysis of social media comments is very important for review analysis. Many online reviews are sarcastic, humorous, or sarcastic.
81524	clarity	Make the sentence clear: Many online comments on social media platforms are hateful, humorous, or sarcastic. The sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models.	Many online comments on social media platforms are hateful, humorous, or hateful. This sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models.
81525	clarity	Write a clearer version for the sentence: The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words phrases responsible for invoking sarcasm. Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter).	The proposed phrases responsible for invoking sarcasm. Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter).
81526	clarity	Use clearer wording: The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words phrases responsible for invoking sarcasm. Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter). To the best of our knowledge, none of the existing state-of-the-art models use an inter-sentence contextual attention mechanism to detect sarcasm in the user-generated short text using only conversational context.	The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words model solves the problem of polysemy also by using context enriched language modules like ELMO and BERT in its first component. This model comprises a total of three major components which takes into account inter sentence, intra sentence contextual information and at last use a convolutional neural network for capturing global contextual information for sarcasm detection. The proposed model was able to generate decent results and cleared showed potential to perform state of the art if trained on a larger dataset.
81527	clarity	Clarify this text: Sentiment analysis of social media comments is very important for review analysis. Many online reviews are sarcastic, humorous, or hateful.	Many online comments on social media platforms are hateful, humorous, or hateful.
81528	clarity	Make this sentence better readable: This sarcastic nature of these short texts change the actual sentiments of the review as predicted by a machine learning model that attempts to detect sentiment alone. Thus, having a model that is explicitly aware of these features should help it perform better on reviews that are characterized by them. Several research has already been done in this field.	This sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models. A lot of research has already been done in this field.
81529	clarity	Write a better readable version of the sentence: Several research has already been done in this field. This paper deals with sarcasm detection on reddit comments. Several machine learning and deep learning algorithms have been applied for the same but each of these models only take into account the initial text instead of the conversation which serves as a better measure to determine sarcasm. The other shortcoming these papers have is they rely on word embedding for representing comments and thus do not take into account the problem of polysemy(A word can have multiple meanings based on the context in which it appears). These existing modules were able to solve the problem of capturing inter sentence contextual information but not the intra sentence contextual information.	Several research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not the intra sentence contextual information.
81530	clarity	Change to clearer wording: So we propose a novel architecture which solves the problem of sarcasm detection by capturing intra sentence contextual information using a novel contextual attention mechanism. The proposed model solves the problem of polysemy also by using context enriched language modules like ELMO and BERT in its first component. This model comprises a total of three major components which takes into account inter sentence, intra sentence contextual information and at last use a convolutional neural network for capturing global contextual information for sarcasm detection. The proposed model was able to generate decent results and cleared showed potential to perform state of the art if trained on a larger dataset.	The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words.
81531	clarity	Clarification: Moreover, we propose an alignment strategy to tackle the label inconsistency during clustering assignments.	Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments.
81532	clarity	Make this sentence readable: ( Code available at URL	( The code is available at URL
81533	clarity	Make this sentence better readable: Natural Language Processing (NLP) systems learn harmful societal biases that cause them to extend and proliferate inequality widely, as they are deployed in more and more situations.	Natural Language Processing (NLP) systems learn harmful societal biases that cause them to widely proliferate inequality as they are deployed in more and more situations.
81534	clarity	Rewrite this sentence clearly: To address and combat this, the NLP community has come to rely on a variety of metrics to identify and quantify bias in black-box models, which are used to monitor model behaviour and to guide efforts at debiasing.	To address and combat this, the NLP community relies on a variety of metrics to identify and quantify bias in black-box models, which are used to monitor model behaviour and to guide efforts at debiasing.
81535	clarity	Change to clearer wording: This research examines whether intrinsic metrics (which are easy to measure) correlate well to extrinsic metrics (which reflect real world bias).	This research examines whether easy-to-measure intrinsic metrics correlate well to extrinsic metrics (which reflect real world bias).
81536	clarity	Clarification: This research examines whether intrinsic metrics (which are easy to measure) correlate well to extrinsic metrics (which reflect real world bias).	This research examines whether intrinsic metrics (which are easy to measure) correlate well to real world extrinsic metrics.
81537	clarity	Make the text more understandable: We measure both intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in more than extremely specific settings.	We measure both intrinsic and extrinsic bias across hundreds of trained models covering different tasks and experimental conditions and find that there is no reliable correlation between these metrics that holds in all scenarios across tasks and languages.
81538	clarity	Make the sentence clear: We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community direct more effort into making downstream measurement simpler and easier.	We advise that efforts to debias embedding spaces be always also paired with measurement of downstream model bias, and suggest that that community increase effort into making downstream measurement simpler and easier.
81539	clarity	Make the sentence clear: However, their memory footprint, inference latency, and power consumption are prohibitive for efficient inference at the edge, and even at the data center.	However, their memory footprint, inference latency, and power consumption are prohibitive efficient inference at the edge, and even at the data center.