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_id stringlengths 1 5	task stringclasses 6 values	src stringlengths 22 884	tgt stringlengths 1 697
68101	clarity	Clarify this paragraph: constructs the storage model of information, and simulate the attribute information precessing process in one of the attribute spaces, interprets how the relations which represented by "Be", "Of", "Have", and so on are embodied in the information storage data structures and the corresponding data reading modes, reclassifies the sentences types from the perspective of task types and data reading modes. Then, simulated the understanding process (the information processing process) on a dialogue example. Finally, the author summarizes the basic conditions of understanding and gives out the definition of understanding from a personal point of view. The study in this paper provides a practical, theoretical basis and research methods for NLU.It also can be applied in large-scale, multi-type information processing in the artificial intelligence (AI) area.	constructs the storage model of information, and simulate the attribute information precessing process in one of the attribute spaces, interprets how the relations which represented by "Be", "Of", "Have", and so on are embodied in the information storage data structures and the corresponding data reading modes can be further divided into the data description task, the data verification task, and the data search task, according to task types represented by these sentences...
68102	clarity	Write a readable version of the sentence: Symbolic knowledge (e.g., entities, relations, and facts in a knowledge graph) has become an increasingly popular component of neural-symbolic models applied to machine learning tasks, such as question answering and recommender systems.	Knowledge graphs (KGs) have helped neural-symbolic models applied to machine learning tasks, such as question answering and recommender systems.
68103	clarity	Rewrite this sentence clearly: Symbolic knowledge (e.g., entities, relations, and facts in a knowledge graph) has become an increasingly popular component of neural-symbolic models applied to machine learning tasks, such as question answering and recommender systems. Besides improving downstream performance, these symbolic structures (and their associated attention weights) are often used to help explain the model's predictions and provide " insights " to practitioners.	Symbolic knowledge (e.g., entities, relations, and facts in a knowledge graph) has become an increasingly popular component of neural-symbolic models applied to machine learning tasks, such as question answering and item recommendation. By using attention over the KG, such models can also " insights " to practitioners.
68104	clarity	Clarify: Besides improving downstream performance, these symbolic structures (and their associated attention weights) are often used to help explain the model's predictions and provide " insights " to practitioners.	Besides improving downstream performance, these symbolic structures (and their associated attention weights) are often used to help explain the model's predictions and provide " explain " to practitioners.
68105	clarity	Rewrite this sentence clearly: In this paper, we question the faithfulness of such symbolic explanations.	In this paper, we question whether these models are really behaving as we expect.
68106	clarity	Clarify this text: We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics.	We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics.
68107	clarity	Clarify this sentence: We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics.	We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original structure while significantly deviating from the original semantics.
68108	clarity	Make the sentence clear: We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics. In particular, we train a reinforcement learning policy to manipulate relation types or edge connections in a knowledge graph, such that the resulting downstream performance is maximally preserved. Across multiple models and tasks, our approach drastically alters knowledge graphs with little to no drop in performance. These results raise doubts about the faithfulness of explanations provided by learned symbolic structures and the reliability of current neural-symbolic modelsin leveraging symbolic knowledge.	We demonstrate that, through a learned strategy (or even simple heuristics), one can produce deceptively perturbed symbolic structures which maintain the downstream performance of the original structure while significantly deviating from the original semantics and structure. Our findings raise doubts about the faithfulness of explanations provided by learned symbolic structures and the reliability of current neural-symbolic modelsin leveraging symbolic knowledge.
68109	clarity	Rewrite the sentence more clearly: These results raise doubts about the faithfulness of explanations provided by learned symbolic structures and the reliability of current neural-symbolic modelsin leveraging symbolic knowledge.	These results raise doubts about KG-augmented models' ability to leverage KG information and provide plausible explanations.
68110	clarity	Make the text more understandable: Knowledge graphs (KGs) have helped neural-symbolic models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.	Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
68111	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.
68112	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.
68113	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.
68114	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.
68115	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.
68116	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).
68117	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.
68118	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.
68119	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.
68120	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.
68121	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.
68122	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.
68123	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.
68124	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.
68125	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
68126	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.
68127	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.
68128	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.
68129	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.
68130	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.
68131	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.
68132	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.
68133	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.
68134	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.
68135	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.
68136	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).
68137	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.
68138	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.
68139	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.
68140	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.