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_id stringlengths 1 5	task stringclasses 7 values	src stringlengths 22 884	tgt stringlengths 0 697
81400	clarity	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.	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.
81401	clarity	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.	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.
81402	clarity	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.	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.
81403	clarity	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.	Stance detection on social media is an emerging opinion mining paradigm for various social and political applications wheresentiment analysis might be sub-optimal.
81404	clarity	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.	Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens in model pre-training.
81405	clarity	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.	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.
81406	clarity	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.	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.
81407	clarity	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.	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.
81408	clarity	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.	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.
81409	clarity	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.	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.
81410	clarity	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 ;	(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 ;
81411	clarity	Clarify this sentence: Finally, we demonstrate the semantic meanings of latent dialogue acts to show the ability of explanation.	Finally, we demonstrate the semantic meanings of latent dialogue acts to show the explanability for HDNO.
81412	clarity	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.	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.
81413	clarity	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.	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.
81414	clarity	Make the text more understandable: SSNs do not require any modifications to a model's loss function or architecture, making them easy to use.	SSNs do not require any loss function or architecture, making them easy to use.
81415	clarity	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.	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.
81416	clarity	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.	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.
81417	clarity	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.	This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
81418	clarity	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.	Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/ other test sets.
81419	clarity	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.	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.
81420	clarity	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.	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.
81421	clarity	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.	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.
81422	clarity	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).	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.
81423	clarity	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.	We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study.
81424	clarity	Clarify the sentence: All of the data, KGs, resources, and shared services are publicly available.	All of the data, KGs, reports, resources and shared services are publicly available.
81425	clarity	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.	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.
81426	clarity	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.	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.
81427	clarity	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.	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.
81428	clarity	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.	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.
81429	clarity	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 ).	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 ).
81430	clarity	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 ).	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 ).
81431	clarity	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 ).	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 ).
81432	clarity	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).	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).
81433	clarity	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).	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).
81434	clarity	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.	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.
81435	clarity	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.	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.
81436	clarity	Make the sentence clear: Machines show an increasingly broad set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP).	Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP).
81437	clarity	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.	We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects.
81438	clarity	Clarify: Word meanings must also be grounded in vision and action, and capable of flexible combinations in ways that current systems are not.	Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not.
81439	clarity	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.	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.