wanyu/IteraTeR_full_sent huggingface dataset:
Original: A dialogue system for disease diagnosis aims at making a diagnosis by conversing with patients. Existing disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of medical knowledge, such as symptom-disease relations .
Revised: A dialogue system for disease diagnosis , the widely used knowledge graph is hard to represent the symptom-symptom relations and symptom-disease relations since the edges of traditional knowledge graph are unweighted. Most research on disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of medical knowledge, such as symptom-disease relations .
Edit intent: meaning-changed
Original:  Existing disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of medical knowledge, such as symptom-disease relations . In addition, previous work pays less attention to demographic attributes of a patient, which are important factors in clinical diagnoses. To tackle these issues , this work presents a graph based and demographic attributes aware dialogue system for disease diagnosis.
Revised:  Existing disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of medical knowledge, such as symptom-disease relations and symptom-symptom relations. To tackle this issue , this work presents a graph based and demographic attributes aware dialogue system for disease diagnosis.
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Original:  Specifically, we first build a weighted bidirectional graph based on clinical dialogues to depict the relationship between symptoms and diseases and then present a bidirectional graph based deep Q-network ( BG-DQN ) for dialogue management.
Revised:  Specifically, we build a weighted bidirectional graph based on clinical dialogues to depict the relationship between symptoms and diseases and then present a bidirectional graph based deep Q-network ( BG-DQN ) for dialogue management.
Edit intent: coherence
Original:  Specifically, we first build a weighted bidirectional graph based on clinical dialogues to depict the relationship between symptoms and diseases and then present a bidirectional graph based deep Q-network ( BG-DQN ) for dialogue management.
Revised:  Specifically, we first build a weighted heterogeneous graph based on clinical dialogues to depict the relationship between symptoms and diseases and then present a bidirectional graph based deep Q-network ( BG-DQN ) for dialogue management.
Edit intent: meaning-changed
Original:  Specifically, we first build a weighted bidirectional graph based on clinical dialogues to depict the relationship between symptoms and diseases and then present a bidirectional graph based deep Q-network ( BG-DQN ) for dialogue management.
Revised:  Specifically, we first build a weighted bidirectional graph based on symptom co-occurrence and a proposed symptom frequency-inverse disease frequency. Then this work proposes a graph based deep Q-network ( BG-DQN ) for dialogue management.
Edit intent: meaning-changed
Original:  Specifically, we first build a weighted bidirectional graph based on clinical dialogues to depict the relationship between symptoms and diseases and then present a bidirectional graph based deep Q-network ( BG-DQN ) for dialogue management.
Revised:  Specifically, we first build a weighted bidirectional graph based on clinical dialogues to depict the relationship between symptoms and diseases and then present a bidirectional graph based deep Q-network ( Graph-DQN ) for dialogue management.
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Original:  By extending Graph Convolutional Network (GCN)  to learn the embeddings of diseases and symptoms from both the structural and attribute information in the graph, BG-DQN could capture the relations between diseases and symptoms better.
Revised:  By combining Graph Convolutional Network (GCN) to learn the embeddings of diseases and symptoms from both the structural and attribute information in the graph, BG-DQN could capture the relations between diseases and symptoms better.
Edit intent: clarity
Original:  By extending Graph Convolutional Network (GCN)  to learn the embeddings of diseases and symptoms from both the structural and attribute information in the graph, BG-DQN could capture the relations between diseases and symptoms better.
Revised:  By extending Graph Convolutional Network (GCN) with DQN to learn the embeddings of diseases and symptoms from both the structural and attribute information in the graph, BG-DQN could capture the relations between diseases and symptoms better.
Edit intent: meaning-changed
Original:  By extending Graph Convolutional Network (GCN)  to learn the embeddings of diseases and symptoms from both the structural and attribute information in the graph, BG-DQN could capture the relations between diseases and symptoms better.
Revised:  By extending Graph Convolutional Network (GCN) to learn the embeddings of diseases and symptoms from both the structural and attribute information in the weighted heterogeneous graph, Graph-DQN could capture the relations between diseases and symptoms better.
Edit intent: meaning-changed
Original:  By extending Graph Convolutional Network (GCN)  to learn the embeddings of diseases and symptoms from both the structural and attribute information in the graph, BG-DQN could capture the relations between diseases and symptoms better. Moreover, BG-DQN also encodes the demographic attributes of a patient to assist the disease diagnosis process.
Revised:  By extending Graph Convolutional Network (GCN) to learn the embeddings of diseases and symptoms from both the structural and attribute information in the graph, BG-DQN could capture the symptom-disease relations and symptom-symptom relations better.
Edit intent: clarity
Original:  However, most analyses implicitly assume that the considered  data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community.
Revised:  However, most analyses implicitly assume that the considered relational data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community.
Edit intent: meaning-changed
Original:  However, most analyses implicitly assume that the considered  data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community.
Revised:  However, most analyses implicitly assume that the considered data is error-free , reliable and accurately reflects the system to be analysed . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community.
Edit intent: meaning-changed
Original:  However, most analyses implicitly assume that the considered  data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community.
Revised:  However, most analyses implicitly assume that the considered data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with a range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community.
Edit intent: fluency
Original:  However, most analyses implicitly assume that the considered  data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community.
Revised:  However, most analyses implicitly assume that the considered data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic biases, measurement errors and other inaccuracies that are well documented in our community.
Edit intent: clarity
Original:  However, most analyses implicitly assume that the considered  data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community. In this paper, we model how such systematic uncertainty on edges of an attributed network can impact network analysis, in particular the ranking of nodes. We discuss how erroneous edge observations can be driven by external node attributes and the relative edge positions in the network , thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.
Revised:  However, most analyses implicitly assume that the considered data is error-free and reliable . Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations can be driven by external node attributes and the relative edge positions in the network , thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.
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Original:  We discuss how erroneous edge observations can be driven by external node attributes and the relative edge positions in the network , thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.
Revised:  We discuss how erroneous edge observations that are driven by external node attributes and the relative edge positions in the network , thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.
Edit intent: clarity
Original:  We discuss how erroneous edge observations can be driven by external node attributes and the relative edge positions in the network , thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks. To show how conclusions drawn from network analyses can get distorted due to such inaccuracies, we focus on the effects of edge-uncertainty on minority group representations in degree-based rankings.
Revised:  We discuss how erroneous edge observations can be driven by external node attributes or errors arising from the (hidden) network structure itself. We exemplify how systematic inaccuracies distort conclusions drawn from network analyses can get distorted due to such inaccuracies, we focus on the effects of edge-uncertainty on minority group representations in degree-based rankings.
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Original:  For that purpose, we analyze synthetic and real networks with varying homophily  and group sizes . We find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority , depending on the type of edge error and homophily.
Revised:  For that purpose, we analyze synthetic and real networks with varying homophily and group sizes , we find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority , depending on the type of edge error and homophily.
Edit intent: fluency
Original:  We find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority , depending on the type of edge error and homophily. Our model enables researchers to include systematic edge-uncertainty in their analyses and thereby better account for the role of minorities in social networks .
Revised:  We find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority , depending on the type of edge error considered and level of homophily in the system. We thus conclude that the implications of systematic bias in edge data depend on an interplay between network topology and type of systematic error. This emphasises the need for an error model framework as developed here, which provides a first step towards studying the effects of systematic edge-uncertainty for various network analysis tasks .
Edit intent: clarity
Original:  This assumption fails to hold in all but the most isolated, controlled scenarios, reducing the ability to achieve the goal of empowering real end-users . Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings through Preference-based Reinforcement Learning (PbRL) to infer a more optimal policy than the demonstration .
Revised:  This assumption fails to hold in most real-world scenarios . Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings through Preference-based Reinforcement Learning (PbRL) to infer a more optimal policy than the demonstration .
Edit intent: clarity
Original:  This assumption fails to hold in all but the most isolated, controlled scenarios, reducing the ability to achieve the goal of empowering real end-users . Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings through Preference-based Reinforcement Learning (PbRL) to infer a more optimal policy than the demonstration .
Revised:  This assumption fails to hold in all but the most isolated, controlled scenarios, reducing the ability to achieve the goal of empowering real end-users . Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings and following the Luce-Shepard rule .
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Original:  In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function.
Revised:  We overcome these limitations in developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function.
Edit intent: clarity
Original:  In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function.
Revised:  In this paper, we overcome the limitations of prior work by developing a novel approach that bootstraps off suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function.
Edit intent: clarity
Original:  In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function.
Revised:  In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized data to train an idealized reward function.
Edit intent: clarity
Original:  We empirically validate we can learn an idealized reward function with %DIFDELCMD < \sim0%%% .95 correlation with the ground truth reward versus only \sim 0.75 for prior work.
Revised:  We empirically validate we learn an idealized reward function with %DIFDELCMD < \sim0%%% .95 correlation with the ground truth reward versus only \sim 0.75 for prior work.
Edit intent: clarity
Original:  We empirically validate we can learn an idealized reward function with %DIFDELCMD < \sim0%%% .95 correlation with the ground truth reward versus only \sim 0.75 for prior work.
Revised:  We empirically validate we can learn an idealized reward function with %DIFDELCMD < \sim0%%% ~0.95 correlation with ground-truth reward versus ~ 0.75 for prior work.
Edit intent: clarity
Original:  We can then train policies achieving \sim 200 %DIFDELCMD < \\%%% %DIF < improvement over the suboptimal demonstration and \sim 90\\% improvement over prior work.
Revised:  We can then train policies achieving ~ 200 %DIFDELCMD < \\%%% %DIF < improvement over the suboptimal demonstration and \sim 90\\% improvement over prior work.
Edit intent: clarity
Original: Inductive transfer learning , enabled by self-supervised learning, have taken the entire Natural Language Processing (NLP ) field by storm, with models such as BERT and BART setting new state of the art on countless natural language understanding tasks.
Revised: Inductive transfer learning has taken the entire Natural Language Processing (NLP ) field by storm, with models such as BERT and BART setting new state of the art on countless natural language understanding tasks.
Edit intent: coherence
Original: Inductive transfer learning , enabled by self-supervised learning, have taken the entire Natural Language Processing (NLP ) field by storm, with models such as BERT and BART setting new state of the art on countless natural language understanding tasks.
Revised: Inductive transfer learning , enabled by self-supervised learning, have taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless natural language understanding tasks.
Edit intent: clarity
Original: Inductive transfer learning , enabled by self-supervised learning, have taken the entire Natural Language Processing (NLP ) field by storm, with models such as BERT and BART setting new state of the art on countless natural language understanding tasks. While there are some notable exceptions , most of the available models and research have been conducted for the Englishlanguage .
Revised: Inductive transfer learning , enabled by self-supervised learning, have taken the entire Natural Language Processing (NLP ) field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks. However , most of the available models and research have been conducted for the Englishlanguage .
Edit intent: coherence
Original:  In this work, we introduce BARThez, the first BART model for the Frenchlanguage (to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT , BARThez is particularly well-suited for generative tasks , since not only its encoder but also its decoder is pretrained.
Revised:  In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French. Being based on BART , BARThez is particularly well-suited for generative tasks , since not only its encoder but also its decoder is pretrained.
Edit intent: clarity
Original:  Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT , BARThez is particularly well-suited for generative tasks , since not only its encoder but also its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark , we evaluate BARThez on a novel summarization dataset, OrangeSum, that we release with this paper.
Revised:  Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT , BARThez is particularly well-suited for generative tasks . We evaluate BARThez on five discriminative tasks from the FLUE benchmark , we evaluate BARThez on a novel summarization dataset, OrangeSum, that we release with this paper.
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Original:  In addition to discriminative tasks from the FLUE benchmark , we evaluate BARThez on a novel summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Revised:  In addition to discriminative tasks from the FLUE benchmark , we evaluate BARThez on a novel summarization dataset, OrangeSum, that we created for this research. We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Edit intent: meaning-changed
Original:  We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Revised:  We also continue the pretraining of a multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Edit intent: clarity
Original:  We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Revised:  We also continue the pretraining of an already pretrained multilingual BART on BARThez' corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Edit intent: fluency
Original:  We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Revised:  We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and show our resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Edit intent: clarity
Original:  We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Revised:  We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, mBARThez, to significantly boost BARThez' generative performance. Code, data and models are publicly available .
Edit intent: meaning-changed