wanyu/IteraTeR_full_sent huggingface dataset:
Original: Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT  have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . However, we benefitted only in English because of the significant scarcity of high-quality medical documents, such as PubMed, in each language.
Revised: Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature ; however, only English speakers benefit due to the significant scarcity of high-quality medical documents, such as PubMed, in each language.
Edit intent: clarity
Original:  We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively.
Revised:  We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese, and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively.
Edit intent: clarity
Original:  We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively.
Revised:  We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively.
Edit intent: clarity
Original:  Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT) achieves the best scores on 7 of the 10 datasets in terms of the BLUE benchmark.
Revised:  Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University , achieved the best score in terms of the BLUE benchmark.
Edit intent: clarity
Original: Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition ,etc . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Revised: Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition ,etc . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Edit intent: coherence
Original: Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition ,etc . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Revised: Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the most similar chemical compounds similar to a query compound or Fewshot 3D Action Recognition ,etc . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Edit intent: fluency
Original: Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition ,etc . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Revised: Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition etc . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Edit intent: fluency
Original: Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition ,etc . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Revised: Graph similarity computation aims to predict a similarity score between one pair of graphs so as to facilitate downstream applications, such as finding the chemical compounds that are most similar to a query compound or Fewshot 3D Action Recognition , . Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison.
Edit intent: coherence
Original:  Specifically, each of the input graphs is partitioned into a set of subgraphs to directly extract the local structural features firstly . Next, a learnable embedding function is used to map each subgraph into an embedding vector.
Revised:  Specifically, each of the input graphs is partitioned into a set of subgraphs to extract the local structural features firstly . Next, a learnable embedding function is used to map each subgraph into an embedding vector.
Edit intent: clarity
Original:  Specifically, each of the input graphs is partitioned into a set of subgraphs to directly extract the local structural features firstly . Next, a learnable embedding function is used to map each subgraph into an embedding vector.
Revised:  Specifically, each of the input graphs is partitioned into a set of subgraphs to directly extract the local structural features directly . Next, a learnable embedding function is used to map each subgraph into an embedding vector.
Edit intent: coherence
Original:  Specifically, each of the input graphs is partitioned into a set of subgraphs to directly extract the local structural features firstly . Next, a learnable embedding function is used to map each subgraph into an embedding vector.
Revised:  Specifically, each of the input graphs is partitioned into a set of subgraphs to directly extract the local structural features firstly . Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
Edit intent: meaning-changed
Original:  Using approximate Graph Edit Distance (GED) as graph similarity metric, experimental results on graph data sets of different graph size demonstrate PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks .
Revised:  Using approximate Graph Edit Distance (GED) as graph similarity metric, experimental results on graph datasets with different graph sizes demonstrate that PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks .
Edit intent: clarity
Original:  Using approximate Graph Edit Distance (GED) as graph similarity metric, experimental results on graph data sets of different graph size demonstrate PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks . The codes will release when this paper is published .
Revised:  Using approximate Graph Edit Distance (GED) as graph similarity metric, experimental results on graph data sets of different graph size demonstrate PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks using approximate Graph Edit Distance (GED) as the graph similarity metric .
Edit intent: coherence
Original:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a  global view of source sequences .
Revised:  However, it is often overlooked that the decoder obtains only a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a global view of source sequences .
Edit intent: clarity
Original:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a  global view of source sequences .
Revised:  However, it is often overlooked that the decoder only has a single view of the source sequences, i.e. , the representations generated by the last encoder layer , which is supposed to be a global view of source sequences .
Edit intent: clarity
Original:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a  global view of source sequences .
Revised:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer . Although those representations are supposed to be a global view of source sequences .
Edit intent: clarity
Original:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a  global view of source sequences .
Revised:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a comprehensive, global view of source sequences .
Edit intent: clarity
Original:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a  global view of source sequences . Such implementation hinders the decoder from concrete, fine-grained , local source information .
Revised:  However, it is often overlooked that the decoder only has a single view of the source sequences, that is , the representations generated by the last encoder layer , which is supposed to be a global view of source sequences , such practice keeps the decoders from concrete, fine-grained , local source information .
Edit intent: clarity
Original:  In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding , that is, different views of the source sequences are presented to different decoder layers .
Revised:  In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding , that is, different views of the source sequences are presented to different decoder layers .
Edit intent: meaning-changed
Original:  In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding , that is, different views of the source sequences are presented to different decoder layers . We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
Revised:  In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding , that is, different views of the source sequences are presented to different decoder layers and multiple strategies are explored to route the source representations. In particular, the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
Edit intent: coherence
Original:  We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
Revised:  We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on the neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
Edit intent: fluency
Original:  We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
Revised:  We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on neural machine translation task , surpassing the previous state-of-the-art architecture on three machine translation datasets.
Edit intent: coherence
Original:  We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
Revised:  We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three benchmark datasets.
Edit intent: clarity
Original: In sequence-to-sequence learning, the attention mechanism has been a great success in bridging the information between the encoderand the decoder. However, it is often overlooked that the decoder obtains only a single view of the source sequences, i.e., the representations generated by the last encoder layer .
Revised: In sequence-to-sequence learning, the decoder relies on the attention mechanism to efficiently extract information from the encoder. While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information. Nonetheless, the decoder still obtains only a single view of the source sequences, i.e., the representations generated by the last encoder layer .
Edit intent: meaning-changed
Original:  However, it is often overlooked that the decoder obtains only a single view of the source sequences, i.e., the representations generated by the last encoder layer . Although those representations are supposed to be a comprehensive, global view of source sequences, such practice keeps the decoders from concrete, fine-grained source information generated by other encoder layers .
Revised:  However, it is often overlooked that the decoder obtains only a single view of the source sequences, which might lead to insufficient training of the encoder layer stack due to the hierarchy bypassing problem .
Edit intent: meaning-changed
Original:  In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding . Concretely, different views of the source sequences are presented to different decoder layers and multiple strategies are explored to route the source representations.
Revised:  In this work, we propose layer-wise cross-view decoding . Concretely, different views of the source sequences are presented to different decoder layers and multiple strategies are explored to route the source representations.
Edit intent: coherence
Original:  In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding . Concretely, different views of the source sequences are presented to different decoder layers and multiple strategies are explored to route the source representations. In particular, the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on the neural machine translation task, surpassing the previous state-of-the-art architecture on three benchmark datasets .
Revised:  In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding , where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences. Systematic experiments show that we successfully address the hierarchy bypassing problem and substantially improve the performance of sequence-to-sequence learning with deep representations on diverse tasks .
Edit intent: coherence
Original:  In this work, we propose layer-wise cross-view decoding, where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences.
Revised:  In this work, we propose layer-wise multi-view decoding, where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences.
Edit intent: clarity
Original:  Systematic experiments  show that we successfully address the hierarchy bypassing problem and substantially improve the performance of sequence-to-sequence learning with deep representations on diverse tasks  .
Revised:  Systematic experiments show that we successfully address the hierarchy bypassing problem and substantially improve the performance of sequence-to-sequence learning with deep representations on diverse tasks , i.e., machine translation, abstractive summarization and image captioning. In particular, our approach surpasses the previous state-of-the-art models on three benchmark machine translation datasets .
Edit intent: meaning-changed
Original:  Recent dMRI studies aim to compare connectivity patterns across thousands of subjects to understand subtle abnormalities in  brain's white matter connectivity across disease populations. Besides connectivity differences, researchers are also interested in investigating distributions of biologically sensitive dMRI derived metrics across subject groups . Existing software products focus solely on the anatomy or are not intuitive and restrict the comparison of multiple subjects.
Revised:  Recent dMRI studies aim to compare connectivity patterns across thousands of subjects to understand subtle abnormalities in brain's white matter connectivity and distributions of biologically sensitive dMRI derived metrics across subject groups . Existing software products focus solely on the anatomy or are not intuitive and restrict the comparison of multiple subjects.
Edit intent: clarity
Original:  Besides connectivity differences, researchers are also interested in investigating distributions of biologically sensitive dMRI derived metrics across subject groups . Existing software products focus solely on the anatomy or are not intuitive and restrict the comparison of multiple subjects.
Revised:  Besides connectivity differences, researchers are also interested in investigating distributions of biologically sensitive dMRI derived metrics . Existing software products focus solely on the anatomy or are not intuitive and restrict the comparison of multiple subjects.
Edit intent: clarity
Original:  Besides connectivity differences, researchers are also interested in investigating distributions of biologically sensitive dMRI derived metrics across subject groups . Existing software products focus solely on the anatomy or are not intuitive and restrict the comparison of multiple subjects.
Revised:  Besides connectivity differences, researchers are also interested in investigating distributions of biologically sensitive dMRI derived metrics across subject groups . Existing software products focus solely on the anatomy , are not intuitive and restrict the comparison of multiple subjects.
Edit intent: fluency
Original:  Besides connectivity differences, researchers are also interested in investigating distributions of biologically sensitive dMRI derived metrics across subject groups . Existing software products focus solely on the anatomy or are not intuitive and restrict the comparison of multiple subjects.
Revised:  Besides connectivity differences, researchers are also interested in investigating distributions of biologically sensitive dMRI derived metrics across subject groups . Existing software products focus solely on the anatomy or are not intuitive or restrict the comparison of multiple subjects.
Edit intent: fluency
Original:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters in 2D and 3D  . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers  .
Revised:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive visualization of brain fiber clusters in 2D and 3D . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers .
Edit intent: clarity
Original:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters in 2D and 3D  . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers  .
Revised:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters combining existing 3D . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers .
Edit intent: coherence