Source: Make this sentence more readable: We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). ExplainIt utilizes a combination of supervised and unsupervised components to mine the opinion phrasesfrom reviews URLanize them in an Opinion Causality Graph (OCG), a novel semi-structured representation which summarizes causal relations. To construct an OCG, we cluster semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation.

Target: We present ExplainIt, a system that extracts URLanizes opinions into an opinion graph, which are useful for downstream applications such as generating explainable review summaries and facilitating search over opinion phrases. In such graphs, a node represents a set of semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation.


Source: Rewrite the sentence more clearly: For each of them, we summarize the prominent methods and models, including approaches to mention encoding based on the self-attention architecture.

Target: For each of them, such as approaches to mention encoding based on the self-attention architecture.


Source: Write a readable version of the sentence: Since many EL models take advantage of entity embeddings to improve their generalization capabilities, we provide an overview of the widely-used entity embedding techniques. We group the variety of EL approaches by several common research directions: joint entity recognition and linking, models for global EL, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.

Target: The vast variety of modifications of this general neural entity linking architecture are grouped by several common research directions: joint entity recognition and linking, models for global EL, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.


Source: Write a clarified version of the sentence: We group the variety of EL approaches by several common research directions: joint entity recognition and linking, models for global EL, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.

Target: We group the variety of EL approaches by several common themes: joint entity recognition and linking, models for global EL, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.


Source: Make the sentence clear: We group the variety of EL approaches by several common research directions: joint entity recognition and linking, models for global EL, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.

Target: We group the variety of EL approaches by several common research directions: joint entity recognition and linking, models for global linking, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches.


Source: Make the sentence clear: Equipped with this new analysis tool, we can now ask questions that were not possible before, e.g. is part-of-speech information important for word prediction?

Target: Equipped with this new analysis tool, we can ask questions that were not possible before, e.g. is part-of-speech information important for word prediction?


Source: Make the sentence clear: In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method which focuses on how the information is being used, rather than on what information is encoded.

Target: In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded.


Source: Write a clarified version of the sentence: In our experiments, the technique boosts the performance for some commonly used UMT methods by 1.5-2.0 BLEU.

Target: In our experiments, it boosts the performance for some commonly used UMT methods by 1.5-2.0 BLEU.


Source: Make this sentence better readable: In our experiments, the technique boosts the performance for some commonly used UMT methods by 1.5-2.0 BLEU.

Target: In our experiments, the technique boosts the performance of the standard UMT methods by 1.5-2.0 BLEU.


Source: Clarification: It also yields 1.5 -3.3 BLEU improvements in IWSLT English-French and English-German translation tasks.

Target: It also yields 1.5 -3.3 BLEU improvements in IWSLT English-French and English-German tasks.


Source: Rewrite this sentence clearly: The quality of the backward system-which is trained on the available parallel data and used for the back-translation-has been shown in many studies to affect the performance of the final NMT model.

Target: The target-side side monolingual data has been used in the back-translation-has been shown in many studies to affect the performance of the final NMT model.


Source: Rewrite this sentence clearly: This work proposes a self-training strategy where the output of the backward model is used to improve the model itself through the forward translation technique. The technique was shown to improve baseline low resource IWSLT'14 English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.

Target: This work proposes a self-training strategy where the output of the backward model is used to improve the backward model through forward translation and the forward model through back-translation. Experimental results on English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.


Source: Make this sentence more readable: The technique was shown to improve baseline low resource IWSLT'14 English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.

Target: The technique was shown to improve baseline low resource IWSLT'14 English-German and English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively.


Source: Rewrite this sentence for readability: The synthetic data generated by the improved English-German backward model was used to train a forward model which out-performed another forward model trained using standard back-translation by 2.7 BLEU.

Target: The synthetic data generated by the improved English-German backward model was used to train a forward model which out-performed another forward model trained using standard back-translation method.


Source: Rewrite the sentence more clearly: This work proposes the use of the target-side data throughout the back-translation approach to improve both the backward and forward models. We explored using only the target-side monolingual data to improve the backward model through forward translation and the forward model through back-translation.

Target: This work proposes a self-training strategy where the output of the backward model is used to improve the backward model through forward translation and the forward model through back-translation.


Source: Rewrite the sentence more clearly: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications wheresentiment analysis might be seen sub-optimal.

Target: Stance detection on social media is an emerging opinion mining paradigm for various social and political applications wheresentiment analysis might be sub-optimal.


Source: Rewrite the sentence more clearly: Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining.

Target: Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens in model pre-training.


Source: Write a readable version of the sentence: Visual Question Answering (VQA) models tend to rely on the language bias and thus fail to learn the reasoning from visual knowledge, which is however the original intention of VQA.

Target: Recent VQA models may tend to rely on the language bias and thus fail to learn the reasoning from visual knowledge, which is however the original intention of VQA.


Source: Rewrite this sentence clearly: Visual Question Answering (VQA) models tend to rely on the language bias and thus fail to learn the reasoning from visual knowledge, which is however the original intention of VQA.

Target: Visual Question Answering (VQA) models tend to rely on language bias as a shortcut and thus fail to learn the reasoning from visual knowledge, which is however the original intention of VQA.


Source: Make the text more understandable: In this paper, we propose a novel cause-effect look at the language bias, where the bias is formulated as the direct effect of question on answer from the view of causal inference.

Target: In this paper, we propose a novel cause-effect look at the language bias as the direct effect of question on answer from the view of causal inference.


Source: Clarify this text: Our proposed cause-effect look 1) is general to any baseline VQA architecture, 2) achieves significant improvement on the language-bias sensitive VQA-CP dataset, and 3) fills the theoretical gap in recent language prior based works.

Target: Our proposed cause-effect look 1) is general to any baseline VQA architecture, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset, and 3) fills the theoretical gap in recent language prior based works.


Source: Change to clearer wording: Recent VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language.

Target: VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language.


Source: Clarify this text: (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during HRL inspired by fictitious play, to preserve the comprehensibility of generated system utterances while improving fulfilling user requests ;

Target: (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during training to theoretically guarantee their convergence to a local maximizer ;


Source: Clarify this sentence: Finally, we demonstrate the semantic meanings of latent dialogue acts to show the ability of explanation.

Target: Finally, we demonstrate the semantic meanings of latent dialogue acts to show the explanability for HDNO.


Source: Make this sentence readable: SSNs address this by learning where and how to share parameters between layers in a neural network while avoiding degenerate solutions that result in underfitting. Specifically, we automatically construct parameter groups that identify where parameter sharing is most beneficial. Then, we map each group's weights to construct layerswith learned combinations of candidates from a shared parameter pool. SSNs can share parameters across layers even when they have different sizes, perform different operations, and/or operate on features from different modalities.

Target: SSNs address this by learning where and how to allocate parameters to layers. This can result in sharing parameters across layers even when they have different sizes, perform different operations, and/or operate on features from different modalities.


Source: Clarify this text: In SSNseach layer obtains weights from a parameter store that decides where and how to allocate parameters to layers. This can result in sharing parameters across layers even when they have different sizes or perform different operations.

Target: In SSNseach layer obtains weights from a parameter store that decides where and how to share parameters between all layers in a network, even between layers of varying sizes and operations.


Source: Make the text more understandable: SSNs do not require any modifications to a model's loss function or architecture, making them easy to use.

Target: SSNs do not require any loss function or architecture, making them easy to use.


Source: Clarify this sentence: We evaluate SSNs using seven network architectures across diverse tasks that include image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters.

Target: We evaluate SSNs using seven network architectures across diverse tasks including image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters.


Source: Rewrite this sentence for readability: When lowering the amount of labeled data to one hour, our model outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data.

Target: When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data.


Source: Clarification: This demonstrates the feasibility of speech recognition with limited amounts of labeled data. Fine-tuning on all of Librispeech achieves 1.9/3.5 WER using a simple baseline model architecture. We will release code and models.

Target: This demonstrates the feasibility of speech recognition with limited amounts of labeled data.


Source: Write a clarified version of the sentence: Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/ noisy test sets.

Target: Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/ other test sets.


Source: Make this easier to read: How to explicitly encode positional information into neural networks is an important problem in natural language processing. In the Transformer model, the positional information is simply encoded as embedding vectors, which are used in the input layer, or encoded as a bias term in the self-attention module.

Target: How to explicitly encode positional information into neural networks is important in learning the representation of natural languages, such as BERT. Based on the Transformer architecture, the positional information is simply encoded as embedding vectors, which are used in the input layer, or encoded as a bias term in the self-attention module.


Source: Make the sentence clear: This design removes the noisy word-position correlation and gives more expressiveness to characterize the relationship between words/positions by using different projection matrices.

Target: This design removes the addition over heterogeneous embeddings in the input, which may potentially bring randomness, and gives more expressiveness to characterize the relationship between words/positions by using different projection matrices.


Source: Use clearer wording: To combat COVID-19, clinicians and scientists all need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions.

Target: To combat COVID-19, clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions.


Source: Clarification: We have developed a novel and comprehensive knowledge discovery framework, COVID-KG, which leverages novel semantic representation and external ontologies to represent text and images in the input literature data, and then performs various extraction components to extract fine-grained multimedia knowledge elements (entities, relations and events).

Target: We have developed a novel and comprehensive knowledge discovery framework, COVID-KG, which leverages novel semantic representation and external ontologies to represent text and images in the input literature data, and then performs various extraction components to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature.


Source: Write a readable version of the sentence: We then exploit the constructed multimedia KGs for question answering and report generation, using drug repurposing as a case study.

Target: We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study.


Source: Clarify the sentence: All of the data, KGs, resources, and shared services are publicly available.

Target: All of the data, KGs, reports, resources and shared services are publicly available.


Source: Write a clearer version for the sentence: To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions.

Target: To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in scientific literature to understand the disease mechanism and the related biological functions.


Source: Clarify: Word embedding models overcome this problem by constructing a standardized meaning space where words are assigned a location based on relations of similarity to, and difference from, other words based on how they are used in natural language samples.

Target: Word embedding models overcome this problem by constructing a standardized and continuous "meaning-space" where words are assigned a location based on relations of similarity to, and difference from, other words based on how they are used in natural language samples.


Source: Rewrite the sentence more clearly: Motivation: NLP continues improving substantially through auto-regressive and auto-encoding Language Models. These LMsrequire expensive computing resources for self-supervised or un-supervised learning from huge unlabelled text corpora. The information learned is transferred through so-called embeddings to downstream prediction tasks. Bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost.

Target: Computational biology and bioinformatics provide vast data gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost.


Source: Make this sentence better readable: Bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost.

Target: Bioinformatics provide vast gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference cost.


Source: Make this easier to read: Here, we addressed two questions: (1) To which extent can HPC up-scale protein LMs to larger databases and larger models? (2) To which extent can LMs extract features from single proteins to get closer to the performance of methods using evolutionary information? Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences (BFD ).

Target: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences (BFD ).


Source: Write a clearer version for the sentence: Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences (BFD ).

Target: Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids from 2.1 billion protein sequences (BFD ).


Source: Clarify the sentence: Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids from 2.1 billion protein sequences (BFD ).

Target: Methodology: Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (BERT and Albert) using 80 billion amino acids from 200 million protein sequences (UniRef100) and 393 billion amino acids (words) from 2.1 billion protein sequences (BFD ).


Source: Rewrite the sentence more clearly: The LMs were trained on the Summit supercomputer, using 5616 GPUs and one TPU Pod, using V3-512 cores. Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states (Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).

Target: The LMs were trained on the Summit supercomputer, using 5616 GPUs and one TPU Pod, using V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure in three- and eight-states (Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).


Source: Change to clearer wording: Results: The results of training these LMs on proteins was assessed by predicting secondary structure in three- and eight-states (Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).

Target: Results: The results of training these LMs on proteins was assessed by predicting secondary structure (3-states: Q3= 75-83, Q8= 63-72), localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89).


Source: Clarify this paragraph: Dimensionality reduction revealed that the LM-embeddings from unlabelled data (only protein sequences) captured important biophysical properties of the protein alphabet, namely the amino acids, and their well orchestrated interplay in governing the shapeof proteins. In the analogy of NLP, this implied having learned some of the grammar of the language of life realized in protein sequences.

Target: Dimensionality reduction revealed that the LM-embeddings from unlabelled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences.


Source: Make this sentence readable: Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people use words in order to express.

Target: Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people express through words.


Source: Make the sentence clear: Machines show an increasingly broad set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP).

Target: Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP).


Source: Make the sentence clearer: We argue that contemporary NLP systems are promising models of human word similarity, but they fall short in many other respects.

Target: We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects.


Source: Clarify: Word meanings must also be grounded in vision and action, and capable of flexible combinations in ways that current systems are not.

Target: Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not.


Source: Rewrite this sentence clearly: In this paper, we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time. Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency.

Target: In this paper, we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time. Experiments on three benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency.


Source: Make this sentence readable: In this paper, we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time. Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency.

Target: In this paper, we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time. Experiments on several benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without sacrificing any inference efficiency.


Source: Make the sentence clearer: In this paper, we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time. Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without sacrificing any inference efficiency.

Target: In this paper, we propose Glancing Transformer (GLAT) to address the above disadvantages, which reduces the difficulty of learning simultaneous generation and introduces explicit target language modeling in the non-autoregressive setting at the same time. Experiments on several benchmarks demonstrate that our approach significantly improves the accuracy of non-autoregressive models without multiple decoding iterations.


Source: Change to clearer wording: In particular, GLAT achieves 30.91 BLEU on WMT 2014 German-English, which narrows the gap between autoregressive models and non-autoregressive models to less than 0.5 BLEU score.

Target: In particular, GLAT achieves state-of-the-art results among non-iterative models and even outperforms top iterative counterparts in some specific benchmarks.


Source: Clarification: Although non-autoregressive models with one-iteration generation achieve remarkable inference speed-up, they still fall behind their autoregressive counterparts in prediction accuracy.

Target: Recent work on non-autoregressive models with one-iteration generation achieve remarkable inference speed-up, they still fall behind their autoregressive counterparts in prediction accuracy.


Source: Make this easier to read: Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually. Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations.

Target: Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually. Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive models without multiple decoding iterations.


Source: Make the text more understandable: However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification, imbalanced data and open vocabulary exist.

Target: However, to build a real-world intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification, imbalanced data and open vocabulary exist.


Source: Rewrite this sentence for readability: However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification, imbalanced data and open vocabulary exist.

Target: However, to build an intelligent assistant that recommends commonly composed charts, it should take the challenges of efficiency, imbalanced data and open vocabulary exist.


Source: Clarify the sentence: However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification, imbalanced data and open vocabulary exist.

Target: However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification, imbalanced data hungry and table context into consideration.


Source: Improve this sentence for readability: However, to build a real-world intelligent assistant that recommends commonly composed charts, it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.

Target: However, to recommend commonly composed charts, it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.


Source: Rewrite this sentence for clarity: However, to build a real-world intelligent assistant that recommends commonly composed charts, it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.

Target: However, to build a real-world intelligent assistant that recommends commonly composed charts in real world, one should take the challenges of efficiency, imbalanced data hungry and table context into consideration.


Source: Clarify this paragraph: However, to build a real-world intelligent assistant that recommends commonly composed charts, it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.

Target: However, to build a real-world intelligent assistant that recommends commonly composed charts, it should take the challenges of efficiency, imbalanced data and table context into consideration.


Source: Clarify this text: Table2Charts outperforms other chart recommendation systems in both multi-type task (with almost doubled recall numbers R@3= 0.62 and R@1= 0.44) and human evaluations.

Target: Table2Charts outperforms other chart recommendation systems in both multi-type task (with doubled recall numbers R@3= 0.62 and R@1= 0.44) and human evaluations.


Source: Clarify this text: One of the main conclusions of our analysis is that BERT performs a decent job in capturing high-level sense distinctions, even when a limited number of examples is available for each word sense.

Target: One of the main conclusions of our analysis is that BERT captures high-level sense distinctions, even when a limited number of examples is available for each word sense.


Source: Clarify this paragraph: One of the main conclusions of our analysis is that BERT performs a decent job in capturing high-level sense distinctions, even when a limited number of examples is available for each word sense.

Target: One of the main conclusions of our analysis is that BERT performs a decent job in capturing high-level sense distinctions accurately, even when a limited number of examples is available for each word sense.


Source: Make this sentence readable: One of the main conclusions of our analysis is that BERT captures high-level sense distinctions accurately, even when a limited number of examples is available for each word sense.

Target: One of the main conclusions of our analysis is that BERT can accurately capture high-level sense distinctions accurately, even when a limited number of examples is available for each word sense.


Source: Write a readable version of the sentence: One of the main conclusions of our analysis is that BERT captures high-level sense distinctions accurately, even when a limited number of examples is available for each word sense.

Target: One of the main conclusions of our analysis is that BERT captures high-level sense distinctions, even when a limited number of examples is available for each word sense.


Source: Make the sentence clear: In fact, a simple feature extraction strategy based on the averaging of contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements beyond this small number of examples.

Target: In fact, the simple feature extraction strategy based on the averaging of contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements beyond this small number of examples.


Source: Clarification: In fact, a simple feature extraction strategy based on the averaging of contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements beyond this small number of examples.

Target: In fact, a simple feature extraction strategy of averaging contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements beyond this small number of examples.


Source: Write a clearer version for the sentence: In fact, a simple feature extraction strategy based on the averaging of contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements beyond this small number of examples.

Target: In fact, a simple feature extraction strategy based on the averaging of contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements obtained by increasing the size of this training data.


Source: Make the text more understandable: Formally, some results about operations applied to computably enumerable (c.e.) and bi-immune sets are proven here, where the objective is for the operations to preserve bi-immunity.

Target: Formally, some results about operations applied to computably enumerable (c.e.) and bi-immune sets are proven here, where the operations preserve bi-immunity.


Source: Clarification: Formally, some results about operations applied to computably enumerable (c.e.) and bi-immune sets are proven here, where the operations preserve bi-immunity.

Target: Formally, some results about operations applied to computably enumerable (c.e.) and bi-immune sets are proven here, where the objective is for the operations to preserve bi-immunity.


Source: Clarify this sentence: } The complete structure of this new subgroup and its subgroups generated by one or more bi-immune rearrangements is unknown.

Target: } The complete structure of the bi-immune symmetric group and its subgroups generated by one or more bi-immune rearrangements is unknown.


Source: Make the sentence clear: However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that is essential for multilingual tasks.

Target: However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that proves essential for multilingual tasks.


Source: Clarify this paragraph: We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations, thus leveraging the model's information bottleneck with twofold strength. A careful analysis shows that the contextualization of encoded representations in our model is significantly more effective than in the original Transformer. We achieve a notable reduction in memory usage due to an improved differentiable top-k operator, making the model suitable to process long documents, as shown on an example of a summarization task.

Target: We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on task-specific parts of the input. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust differentiable top-k operator, making the model suitable to process long documents, as shown on an example of a summarization task.


Source: Rewrite this sentence for clarity: Conventional sparse retrieval methods such as TF-IDF and BM25 are simple and efficient, but solely rely on lexical overlap and fail to conduct semantic matching.

Target: Conventional sparse retrieval methods such as TF-IDF and BM25 are simple and efficient, but solely rely on lexical overlap without semantic matching.


Source: Change to clearer wording: Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and sometimes insufficient for exact matching as they embed the entire text sequence into a single vector with limited capacity.

Target: Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and sometimes insufficient for exact matching as they embed the text sequence into a single vector with limited capacity.


Source: Make the sentence clearer: We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR cite{karpukhin2020dense}.

Target: We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR cite{karpukhin2020dense}.


Source: Make the sentence clear: We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR cite{karpukhin2020dense}.

Target: We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense method DPR cite{karpukhin2020dense}.


Source: Make the sentence clear: We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense method DPR cite{karpukhin2020dense}.

Target: We demonstrate on open-domain question answering (QA) that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the current state-of-the-art dense methods such as DPR cite{karpukhin2020dense}.


Source: Clarify this text: We show that generating various contexts of a query is beneficial as fusing their results consistently yields a better retrieval accuracy.

Target: We show that generating various contexts of a query is beneficial as fusing their results consistently yields better retrieval accuracy.


Source: Make this sentence more readable: Moreover, GAR achieves the state-of-the-art performance of extractive QA on the Natural Questions and TriviaQA datasets when equipped with an extractive reader.

Target: Moreover, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets when equipped with an extractive reader.


Source: Clarify this sentence: Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose LUT, Listen-Understand-Translate, a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.

Target: Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose Listen-Understand-Translate, a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.


Source: Write a readable version of the sentence: Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose LUT, Listen-Understand-Translate, a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.

Target: Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose LUT, Listen-Understand-Translate, (LUT), a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.


Source: Write a clarified version of the sentence: Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose LUT, Listen-Understand-Translate, a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.

Target: Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose LUT, Listen-Understand-Translate, a unified framework with triple supervision signals to decouple the end-to-end speech-to-text translation task.


Source: Change to clearer wording: To reduce the learning difficulty, we propose COnSecutive Transcription and Translation (COSTT), an integral framework for speech-to-text translation.

Target: To reduce the learning difficulty, we propose COnSecutive Transcription and Translation (COSTT), an integral approach for speech-to-text translation.


Source: Rewrite the sentence more clearly: We introduce texttt N-LTP, an open-source Python Chinese natural language processing toolkit supporting five basic tasks: Chinese word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, and semantic dependency parsing. texttt N-LTP adopts the multi-task framework with the pre-trained model to capture the shared knowledge across all Chinese relevant tasks.

Target: We introduce texttt N-LTP, an open-source Python Chinese natural language processing toolkit supporting five basic tasks: Chinese word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, and semantic dependency parsing. N-LTP adopts the multi-task framework with the pre-trained model to capture the shared knowledge across all Chinese relevant tasks.


Source: Rewrite this sentence for readability: For natural language processing (NLP) taskssuch as sentiment or topic classification, currently prevailing approaches heavily rely on pretraining large self-supervised models on massive external data resources.

Target: For natural language processing 'text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external data resources.


Source: Improve this sentence for readability: For natural language processing (NLP) taskssuch as sentiment or topic classification, currently prevailing approaches heavily rely on pretraining large self-supervised models on massive external data resources. However, this methodology is being critiqued for: exceptional compute and pretraining data requirements ;

Target: For natural language processing (NLP) taskssuch as sentiment or topic classification, currently prevailing approaches heavily rely on pretraining large self-supervised models on massive external data sources, which incurs exceptional pretraining data requirements ;


Source: Write a readable version of the sentence: To minimise overly favourable evaluation, we examine learning on a long-tailed, low-resource, multi-label text classification dataset with noisy, highly sparse labels and many rare concepts.

Target: To minimise overly favourable evaluation, we examine learning on a long-tailed, low-resource, multi-label text classification scenario with noisy, highly sparse labels and many rare concepts.


Source: Change to clearer wording: We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal', self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible.

Target: We also find empirical evidence that zero and few-shot learning markedly benefit from increasing ' dataset-internal', self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible.


Source: Write a readable version of the sentence: We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal', self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible.

Target: We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal', self-supervised pretraining signals, to help reduce the reliance on large external sources of such signals is infeasible.


Source: Make this sentence more readable: We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal', self-supervised training signals, which is of practical importance when retrieving or computing on large external sources of such signals is infeasible.

Target: We also find empirical evidence that zero and few-shot learning markedly benefit from adding more ` dataset-internal', self-supervised training signals, which is of practical importance when retrieving or computing on large external sources.


Source: Make this sentence readable: However, fundamental pretraining method capabilities like few to zero-shot learningor preserving minority concept (long-tail) prediction performance along with accordingly designed evaluation scenarios remain open challenges. We thus propose Contrastive Label-Embedding Self-Supervision (CLESS) pretraining, which enables pretraining from multiple magnitudes smaller, 'task internal' data only, while still strongly improving fully supervised, long-tail, few-shot and self-supervised zero-shot learning abilities.

Target: However, fundamental pretraining method capabilities like few to zero-shot learningor preserving minority concept (long-tail generalization, since long-tail, few-shot and self-supervised zero-shot learning abilities.


Source: Clarification: We propose a simple method to generate large amounts of multilingual question and answer pairs by a single generative model.

Target: We propose a simple method to generate multilingual question and answer pairs by a single generative model.


Source: Make this sentence readable: These synthetic samples are then applied to augment the available gold multilingual ones to improve the performance of multilingual QA models on target languages.

Target: These synthetic samples can be used to improve the zero-shot performance of multilingual QA models on target languages.


Source: Make the sentence clearer: Our approach only requires existence of automatically translated samples from Englishto the target domain, thus removing the need for human annotations in the target languages. Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets.

Target: Our proposed multi-task training of the generative model only requires the training samples in English, thus removing the need for human annotations in the target languages. Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets.


Source: Clarify the sentence: Our approach only requires existence of automatically translated samples from Englishto the target domain, thus removing the need for human annotations in the target languages. Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets.

Target: Our approach only requires existence of automatically translated samples from Englishto the target domain, thus removing the need for labeled samples in the target languages. Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets.


Source: Make the sentence clearer: Our proposed multi-task training of the generative model only requires the training samples in English, thus removing the need for labeled samples in the target languages, making it applicable to far more languages than those with labeled data.

Target: Our proposed multi-task training of the generative model only requires the training samples in English, thus removing the need for such samples in the target languages, making it applicable to far more languages than those with labeled data.


Source: Make the text more understandable: This paper disassembles the information represented by natural language,

Target: Therefore the study begins with disassembling the information represented by natural language,


Source: Write a clearer version for the sentence: analyzes the classification coding system of attribute information and the abstraction relation between attribute information and entities in the real world, 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.

Target: analyzes the classification coding system of attribute information and the abstraction relation between attribute information and entities in the real world. To have a clear and better discussion, 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.


Source: 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.

Target: 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...


Source: 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.

Target: Knowledge graphs (KGs) have helped neural-symbolic models applied to machine learning tasks, such as question answering and recommender systems.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: Rewrite this sentence clearly: In this paper, we question the faithfulness of such symbolic explanations.

Target: In this paper, we question whether these models are really behaving as we expect.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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.

Target: These results raise doubts about KG-augmented models' ability to leverage KG information and provide plausible explanations.


Source: 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.

Target: Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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.

Target: Our findings raise doubts about KG-augmented models' ability to reason about KG information and provide plausible explanations.


Source: Rewrite this sentence for clarity: Our findings raise doubts about KG-augmented models' ability to leverage KG information and provide plausible explanations.

Target: Our findings raise doubts about KG-augmented models' ability to leverage KG information and give sensible explanations.


Source: 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.

Target: 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.


Source: 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.

Target: 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).


Source: 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.

Target: 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.


Source: Clarify: We show marked improvement on three commonsense reasoning benchmarks and demonstrate the superiority of the learned graph structures with user studies.

Target: We show marked improvement on three commonsense reasoning benchmarks and a user study of fact validness and helpfulness.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: Clarify this text: Overall, we have covered the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data.

Target: We discuss the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data.


Source: Clarify this paragraph: Overall, we have covered the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data.

Target: 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.


Source: 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.

Target: We also provide discussions a variety of important topics regarding the future development of TST.


Source: 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.

Target: 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.


Source: Make this easier to read: We also release our annotated data and code for future researchat URL

Target: We will release our annotated data and code for future researchat URL


Source: 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.

Target: In this work, we demonstrate that the contextualized word vectors of BERT and RoBERTa have a commonpattern.


Source: 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.

Target: For BERT, the 557^{th neuron-level analysis method, which reveals that the outliers are closely related to the positional information.


Source: Clarify this paragraph: We find that these "tails" are closely related to the positional information.

Target: We find that these "tails" are closely related to information captured by positional embeddings.


Source: Clarify this text: Our theory provides precise reasons answering why or why not a triple is correct.

Target: Our theory provides precise reasons explaining why or why not a triple is correct.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: Clarify this paragraph: Many online comments on social media platforms are hateful, humorous, or sarcastic.

Target: Sentiment analysis of social media comments is very important for review analysis. Many online reviews are sarcastic, humorous, or sarcastic.


Source: 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.

Target: 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.


Source: 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).

Target: 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).


Source: 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.

Target: 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.


Source: Clarify this text: Sentiment analysis of social media comments is very important for review analysis. Many online reviews are sarcastic, humorous, or hateful.

Target: Many online comments on social media platforms are hateful, humorous, or hateful.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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.

Target: The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words.


Source: Clarification: Moreover, we propose an alignment strategy to tackle the label inconsistency during clustering assignments.

Target: Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments.


Source: Make this sentence readable: ( Code available at URL

Target: ( The code is available at URL


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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).

Target: This research examines whether easy-to-measure intrinsic metrics correlate well to extrinsic metrics (which reflect real world bias).


Source: Clarification: This research examines whether intrinsic metrics (which are easy to measure) correlate well to extrinsic metrics (which reflect real world bias).

Target: This research examines whether intrinsic metrics (which are easy to measure) correlate well to real world extrinsic metrics.


Source: 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.

Target: 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.


Source: 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.

Target: 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.


Source: 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.

Target: However, their memory footprint, inference latency, and power consumption are prohibitive efficient inference at the edge, and even at the data center.


Source: Make this easier to read: The model consists of an encoder, a decoder, and a position dependent summarizer (PDS). The three modules are based on basic attention blocks. The encoder extracts high-level representations from the speech.

Target: The model aggregates encoded speech features into the hidden representations corresponding to each token with attention mechanisms. Thus, the model can capture the token relations by self-attention on the aggregated hidden representations from the speech.


Source: Rewrite this sentence for clarity: The encoder extracts high-level representations from the speech. The PDS uses positional encodings corresponding to tokensto convert the acoustic representations into token-level representations. The decoder further captures token-level relationships with the self-attention mechanism. At last, the probability distribution on the vocabulary is computed for each token position. Therefore, speech recognition is re-formulated as a position-wise classification problem. Further, we propose a cross-modal transfer learning method to refine semantics from a large-scale pre-trained language model BERT for improving the performance.

Target: The encoder extracts high-level representations from the whole speech signal rather than autoregressive modeling on tokens. Without explicitly autoregressive language modeling, this model predicts all tokens in the sequence in parallel so that the inference is efficient. Moreover, we propose a cross-modal transfer learning method to refine semantics from a large-scale pre-trained language model BERT for improving the performance.


Source: Use clearer wording: As a result, these models could potentially fail to generalize to real-world out-of-distribution scenarios.

Target: As a result, these models fail to generalize to real-world out-of-distribution scenarios.


Source: Rewrite this sentence for readability: As a result, these models could potentially fail to generalize to real-world out-of-distribution scenarios.

Target: As a result, these models could potentially fail to generalize to real-world out-of-distribution data.


Source: Use clearer wording: In this work, we show that the shortcut learning behavior can be explained by the long-tailed phenomenon.

Target: In this work, we show that the words in the NLU training set can be modeled as a long-tailed phenomenon.


Source: Clarify this paragraph: In this work, we show that the shortcut learning behavior can be explained by the long-tailed phenomenon.

Target: In this work, we show that the shortcut learning behavior can be explained by the long-tailed distribution.


Source: Write a clarified version of the sentence: There are two findings: 1) Trained NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training.

Target: There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training.


Source: Make this sentence more readable: Experimental analysis further indicates that our method can improve the generalization accuracy on OOD data, while preserving the accuracy on in distribution test data.

Target: Experimental analysis further indicates that LGTR can improve the generalization accuracy on OOD data, while preserving the accuracy on in distribution test data.


Source: Clarify the sentence: Experimental analysis further indicates that our method can improve the generalization accuracy on OOD data, while preserving the accuracy on in distribution test data.

Target: Experimental analysis further indicates that our method can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.


Source: Use clearer wording: Previous works on expressive text-to-speech (TTS) have a limitation on robustness and speed when training and inferring. Such drawbacks mostly come from autoregressive decoding, which makes the succeeding step vulnerable to preceding error. To overcome this weakness, we propose STYLER, a novel expressive text-to-speech model with parallelized architecture.

Target: Previous works on expressive text-to-speech (TTS) have been tackled on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability. Although several approaches resolve some limitations, none of them has resolved all weaknesses at once. In this paper, we propose STYLER, a novel expressive text-to-speech model with parallelized architecture.


Source: Clarification: To overcome this weakness, we propose STYLER, a novel expressive text-to-speech model with parallelized architecture.

Target: To overcome this weakness, we propose STYLER, an expressive and controllable text-to-speech model with parallelized architecture.


Source: Clarify: To overcome this weakness, we propose STYLER, a novel expressive text-to-speech model with parallelized architecture. Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance.

Target: To overcome this weakness, we propose STYLER, a novel expressive text-to-speech model with robust speech synthesis and high speed. Excluding autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance.


Source: Make the sentence clear: Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance.

Target: Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust on long, unseen data. Disentangled style factor modeling under supervision enlarges the controllability of synthesizing speech with fruitful expressivity.


Source: Rewrite this sentence for clarity: Moreover, our novel noise modeling approach from audio using domain adversarial training and Residual Decoding enabled style transferwithout transferring noise.

Target: Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding enabled style transferwithout transferring noise.


Source: Clarify: Previous works on neural text-to-speech (TTS) have been tackled on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability.

Target: Previous works on neural text-to-speech (TTS) have been addressed on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability.


Source: Clarify this sentence: Although several approaches resolve some limitations, none of them has resolved all weaknesses at once.

Target: Although several approaches resolve some limitations, there has been no attempt to solve all weaknesses at once.


Source: Change to clearer wording: In this paper, we propose STYLER, an expressive and controllable text-to-speech model with robust speech synthesisand high speed. Excluding autoregressive decoding and introducing a novel audio-text aligning method called Mel Calibrator leads speech synthesis more robust on long, unseen data.

Target: In this paper, we propose STYLER, an expressive and controllable TTS framework with high-speed and robust synthesis. Our novel audio-text aligning method called Mel Calibrator leads speech synthesis more robust on long, unseen data.


Source: Improve this sentence for readability: Excluding autoregressive decoding and introducing a novel audio-text aligning method called Mel Calibrator leads speech synthesis more robust on long, unseen data.

Target: Excluding autoregressive decoding and introducing a novel audio-text aligning method called Mel Calibrator and excluding autoregressive decoding enable rapid training and inference and robust synthesis on unseen data.


Source: Use clearer wording: Disentangled style factor modeling under supervision enlarges the controllability of synthesizing speech with fruitful expressivity. Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding enables noise-robust style transfer, decomposing the noise without any additional label.

Target: Disentangled style factor modeling under supervision enlarges the controllability in synthesizing process leading to expressive TTS. On top of it, a novel noise modeling pipeline using domain adversarial training and Residual Decoding enables noise-robust style transfer, decomposing the noise without any additional label.


Source: Make the sentence clear: Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding enables noise-robust style transfer, decomposing the noise without any additional label.

Target: Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding empowers noise-robust style transfer, decomposing the noise without any additional label.


Source: Rewrite this sentence clearly: Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed, robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies. Synthesis samples of our model and experiment results are provided via our demo page.

Target: Various experiments demonstrate that STYLER is more effective in speed and robustness than expressive TTS with autoregressive decoding and more expressive and controllable than reading style non-autoregressive TTS. Synthesis samples of our model and experiment results are provided via our demo page.


Source: Rewrite this sentence for clarity: Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed, robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies. Synthesis samples of our model and experiment results are provided via our demo page.

Target: Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed, robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies. Synthesis samples and experiment results are provided via our demo page.


Source: Improve this sentence for readability: In this paper, we describe a corpus annotation process, which was guided by a linguist, and a hate speech skilled to support the identification of hate speech and offensive language on social media.

Target: In this paper, we describe a corpus annotation process proposed by a linguist, and a hate speech skilled to support the identification of hate speech and offensive language on social media.


Source: Make this sentence readable: In addition, we provide the first robust corpus of this kind for the Brazilian Portuguese language.

Target: In addition, we provide the first robust dataset of this kind for the Brazilian Portuguese language.


Source: Rewrite this sentence clearly: This paper presents a new approach for offensive language and hate speech detection on social media.

Target: This paper provides a new approach for offensive language and hate speech detection on social media.


Source: Make this sentence better readable: In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension (ViCoQA ), consisting of 10,000 questions with answers over 2,000 conversations about health news articles.

Target: In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension (UIT-ViCoQA ), consisting of 10,000 questions with answers over 2,000 conversations about health news articles.


Source: Clarify this text: We analyze ViCoQA in depth with different linguistic aspects.

Target: We analyze UIT-ViCoQA in depth with different linguistic aspects.


Source: Change to clearer wording: Then, we evaluate several baseline models about dialogue and reading comprehension on the ViCoQA corpus.

Target: Then, we evaluate several baseline models about dialogue and reading comprehension on the UIT-ViCoQA corpus.


Source: Rewrite this sentence for readability: Machine reading comprehension (MRC) is a sub-field in natural language processing or computational linguistics. MRC aims to help computers understand unstructured texts and then answer questions related to them.

Target: Machine reading comprehension (MRC) is a sub-field in natural language processing which aims to help computers understand unstructured texts and then answer questions related to them.


Source: Clarification: Then, we evaluate several baseline models about dialogue and reading comprehension on the UIT-ViCoQA corpus.

Target: Then, we evaluate several baseline approaches for conversational machine comprehension on the UIT-ViCoQA corpus.


Source: Make the sentence clear: He also reaffirmed his stance on participation of Serbs in Kosovo's institutions: "For now, there is no room for Serbian representatives in institutions of Kosovo."

Target: Kotunica also reaffirmed his stance on participation of Serbs in Kosovo's institutions: "For now, there is no room for Serbian representatives in institutions of Kosovo."


Source: Make this easier to read: With smaller size than movies and broadband access, popular shows often appear within hours of airing on TV.

Target: With the increasing ubiquity of broadband Internet access, popular shows often appear within hours of airing on TV.


Source: Clarify this sentence: CEO of the MPAA, Dan Glickman told the BBC "Since we began shutting these sites down, the time that it takes to download a file on BitTorrent has increased exponentially which means the experience of downloading copyrighted films and TV shows is not what it used to be.

Target: MPAA CEO Dan Glickman told the BBC "Since we began shutting these sites down, the time that it takes to download a file on BitTorrent has increased exponentially which means the experience of downloading copyrighted films and TV shows is not what it used to be.


Source: Write a better readable version of the sentence: According to GONG, an NGO observing the elections, Milan Bandić will be able to form a government in the city of Zagreb, since his list has won around 46\%.

Target: According to GONG, an NGO observing the elections, Milan Bandić, can form a government in the city of Zagreb, since his list has won around 46\%.


Source: Clarify the sentence: This would give them 27 of 51 seats in the capital city.

Target: This would give the coalition 27 of 51 seats in the capital city.


Source: Make this sentence readable: The twelfth-annual Asia Pacific Regional Internet Conference on Operational Technologies (a.k.a APRICOT), returned to Taiwan this year at Taipei Howard Plaza Hotel ;

Target: The twelfth-annual Asia Pacific Regional Internet Conference on Operational Technologies (a.k.a APRICOT), returned to Taiwan this year at the Taipei Howard Plaza Hotel ;


Source: Make the sentence clear: As Wikinews Journalist Rico Shen reported on the recent "Edison Chen photo scandal" incident, he commented: Workshops with varied topics and different technology levels took place from February 20 to 24, while several main seminars and speeches for industry, governmental, and academic executives ran from February 25 to 29.

Target: When Wikinews journalist Rico Shen reported on the recent "Edison Chen photo scandal" incident, he commented: Workshops with varied topics and different technology levels took place from February 20 to 24, while several main seminars and speeches for industry, governmental, and academic executives ran from February 25 to 29.


Source: Clarify this text: Several industry experts such as Wilfred Kwan (Chief Technology Officer of AsiaNetCom), Chung-laung Liu (ISOC Taiwan Chapter Chair ), and Maemura Akinori (EC Chair of APNIC) will give several speeches related to the Internet industry at the conference.

Target: Several industry experts such as Wilfred Kwan (Chief Technology Officer of AsiaNetCom), Chung-laung Liu (Taiwan Chapter Chair ), and Maemura Akinori (EC Chair of APNIC) will give several speeches related to the Internet industry at the conference.


Source: Write a readable version of the sentence: ZipcodeZoo offers over 3 million web pages describing species of plants and animals. Pages contain 258, 753 photostaken by 1, 369 photographers, 1, 104 sound recordings, and definitions of 234, 888 terms.

Target: ZipcodeZoo offers over 3 million species of plants and animals. Pages contain 258, 753 photostaken by 1, 369 photographers, 1, 104 sound recordings, and definitions of 234, 888 terms.


Source: Rewrite the sentence more clearly: Oil in productionCrude oil prices in New York rose to a new record of $102.59 per barrel on Thursday, although the figure increased even more during after hours trading.

Target: An oil refineryCrude oil prices in New York rose to a new record of $102.59 per barrel on Thursday, although the figure increased even more during after hours trading.


Source: Make this sentence readable: In less than a month, prices have risen $10, leading the figures to above the record highs set during the 1980s (taking inflation into account).

Target: In less than a month, prices have risen $10, leading the inflation adjusted prices above the record highs set during the 1980s (taking inflation into account).


Source: Write a better readable version of the sentence: In less than a month, prices have risen $10, leading the figures to above the record highs set during the 1980s (taking inflation into account).

Target: In less than a month, prices have risen $10, leading the figures to above the record highs set during the 1980s.


Source: Clarify the sentence: The weak dollar is seen as a major cause of this rise. Congressman Ron Paul of Texas, pointed out to Federal Reserve chairman Ben Bernanke in a committee meeting this week that despite the price of oil's rapid ascent, it had remained flat when compared to the price of gold.

Target: While the weak dollar is seen as a major cause of this rise. Congressman Ron Paul of Texas, pointed out to Federal Reserve chairman Ben Bernanke in a committee meeting this week that despite the price of oil's rapid ascent, it had remained flat when compared to the price of gold.


Source: Write a readable version of the sentence: The weak dollar is seen as a major cause of this rise. Congressman Ron Paul of Texas, pointed out to Federal Reserve chairman Ben Bernanke in a committee meeting this week that despite the price of oil's rapid ascent, it had remained flat when compared to the price of gold.

Target: The weak dollar is seen as a major cause of this rise. Congressman Ron Paul of Texas, pointed out to Federal Reserve chairman Ben Bernanke in a committee meeting this week that despite the price of oil, it had remained flat when compared to the price of gold.


Source: Make this sentence readable: A graph to show the increase in gasoline pricesThere have also been suggestions that reports of a fire at a National Gas Terminal may have contributed to the rising oil price.

Target: There have also been suggestions that reports of a fire at a National Gas Terminal may have contributed to the rising oil price.


Source: Clarify this sentence: Time Evans from Citigroup Futures has stated he believes that this fire at the UK natural gas terminal is creating a strong push in the European market, and that is translating here [the US]."

Target: Time Evans from Citigroup Futures said he believes " that this fire at the UK natural gas terminal is creating a strong push in the European market, and that is translating here [the US]."


Source: Clarify this sentence: 10 other cases of the virus have appeared in dead birds, all Mute Swans from the same area.

Target: Ten other cases of the virus have appeared in dead birds, all Mute Swans from the same area.


Source: Write a clarified version of the sentence: Interpol has released an "Orange Notice" for the Southeast Asian Jemaah Islamiyah terrorist group leader Mas Selamat bin Kastari, who escaped from a detention center Wednesday.

Target: Interpol has issued an "Orange Notice" for the Southeast Asian Jemaah Islamiyah terrorist group leader Mas Selamat bin Kastari, who escaped from a detention center Wednesday.


Source: Rewrite this sentence clearly: Interpol has released an "Orange Notice" for the Southeast Asian Jemaah Islamiyah terrorist group leader Mas Selamat bin Kastari, who escaped from a detention center Wednesday.

Target: Interpol has released an "Orange Notice" for the leader of southeast Asian Jemaah Islamiyah terrorist group leader Mas Selamat bin Kastari, who escaped from a detention center Wednesday.


Source: Make this easier to read: Not only professional runners from Asian European countries participated in this race, several enterprises, governmental and academic teams all supported this race by participating in 6K Fun Run classes.

Target: Not only professional runners from Asian European countries participated in this race, several enterprises, governmental and academic teams also supported this race by participating in 6K Fun Run classes.