自然语言处理

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对比学习

浏览 194 扫码 分享 2023-03-22 13:53:19

    https://zhuanlan.zhihu.com/p/141141365
    https://zhuanlan.zhihu.com/p/136332151

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    • 书签
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    • 学术小记
      • 2022.05
        • Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
        • BRIO: Bringing Order to Abstractive Summarization
        • SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
      • 2022.03
        • 22.03.08 Improving Evidence Retrieval for Automated Explainable Fact-Checking
        • 22.03.07 Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
        • 22.03.05 Controllable Natural Language Generation with Contrastive Prefixes
        • 22.03.05 Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
        • 22.03.05 Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding
        • 22.03.04 SpanBert
        • 22.03.02 Improving Language Models by Retrieving from Trillions of Tokens
        • 22.03.02 Retrieval Augmented NLP
        • 22.03.01 Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
      • 2022.02
        • 22.02.28 Bert-Whitening
        • 22.02.27 各向异性
        • 22.02.27 Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning
        • 22.02.26 Impact of Pretraining Term Frequencies on Few-Shot Reasoning
        • 22.02.19 A Primer in BERTology: What We Know About How BERT Works
        • 22.02.19 模型压缩 From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
      • 2022.01
        • 21.01.28 苏神博客
        • 21.01.23 重读DQN
        • 21.01.22 DATASET DISTILLATION
        • 21.01.22 Adversarial NLI: A New Benchmark for Natural Language Understanding【ACL20】
        • 21.01.21 重读变分推断
        • 21.01.18 ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
      • 2021.12
        • 21.12.30 Robust Neural Machine Translation with Doubly Adversarial Inputs
        • 21.12.30 Towards a Universal Continuous Knowledge Base 连续性知识库
        • 21.12.20 Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
        • 21.12.20 Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
        • 21.12.20 FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
        • 21.12.20 Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
        • 21.12.20 Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
        • 21.12.20 Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization [NIPS19]
        • 21.12.19 Improving Truthfulness of Headline Generation
        • 21.12.18 Multi-Fact Correction in Abstractive Text Summarization.
        • 21.12.18 Reducing Quantity Hallucinations in Abstractive Summarization【EMNLP20】
        • 21.12.18 GO FIGURE: A Meta Evaluation of Factuality in Summarization
        • 21.12.18 Truth or Error? Towards systematic analysis of factual errors in abstractive summaries
        • 21.12.17 Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
        • 21.12.17 Detecting Hallucinated Content in Conditional Neural Sequence Generation
        • 21.12.16 Assessing The Factual Accuracy of Generated Text
        • 21.12.15 Enhancing Factual Consistency of Abstractive Summarization
        • 21.12.15 QuestEval: Summarization Asks for Fact-based Evaluation
        • 21.12.11 Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection 【NAACL2021】
        • 21.12.9 Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
        • 21.12.2 Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
        • 21.12.2 Annotating and Modeling Fine-grained Factuality in Summarization
        • 21.12.1 Inspecting the Factuality of Hallucinated Entities in Abstractive Summarization
        • 21.12.1 Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
      • 2021.11
        • 21.11.30 MOFE: MIXTURE OF FACTUAL EXPERTS FOR CONTROLLING HALLUCINATIONS IN ABSTRACTIVE SUMMARIZATION
        • 21.11.30 Fine-grained Factual Consistency Assessment for Abstractive Summarization Models 【EMNLP21】
        • 21.11.30 Dialogue Inspectional Summarization with Factual Inconsistency Awareness
        • 21.11.30 SUMMAC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization 【TACL】
        • 21.11.29 Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization [EMNLP21]
        • 21.11.28
        • 21.11.27 Factual Probing Is [MASK]: Learning vs. Learning to Recall【NAACL2021】
        • 21.11.27 BARTSCORE: Evaluating Generated Text as Text Generation 【NIPS2021】
        • 21.11.26 Prefix-Tuning: Optimizing Continuous Prompts for Generation [ACL2021]
        • 21.11.24 相似度分析
        • 21.11.22 DEEP: DEnoising Entity Pre-training for Neural Machine Translation
        • 21.11.22 NJU HPC Tips
        • 21.11.22 NLP多进程处理框架 如何高效处理文本？
        • 21.11.19 BM25和TFIDF转载
        • 21.11.19 SJL博客6篇
        • 21.11.18 转载苏巨 Bert初始化以及Norm，梯度消失的讨论
        • 21.11.18 SJL博客8篇
        • 21.11.18 Discourse Understanding and Factual Consistency in Abstractive Summarization EACL2021
        • 21.11.17 SJL博客3篇
        • 21.11.16 Factual Error Correction for Abstractive Summarization Models EMNLP20 short
        • 21.11.16 NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
        • 21.11.15 初步做的论文Servey
        • 21.11.6 EMNLP2021 论文预讲笔记
        • 21.11.5 Fairseq Code review
        • 21.11.4 PEGASUS Pre-training with Extracted Gap-sentences for Abstractive Summarization【ICML 2020】
        • 21.11.3 On Faithfulness and Factuality in Abstractive Summarization【ACL 2020】
        • 21.11.1 BERTSCORE: EVALUATING TEXT GENERATION WITH BERT 【ICLR2020】
        • 21.11.1 Focus Attention: Promoting Faithfulness and Diversity in Summarization【ACL2021】 pending
        • 21.11.1 采样策略
      • 2021.10
        • 21.10.31 Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation 【EMNLP2021】
        • 21.10.29 Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
        • 21.10.27 MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization 【EMNLP2021】
        • 21.10.26 Improving Factual Consistency of Abstractive Summarization via Question Answering 【pending】
        • 21.10.25 Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
        • 21.10.24 CTRLSUM: TOWARDS GENERIC CONTROLLABLE TEXT SUMMARIZATION
        • 21.10.20 Training Dynamics for Text Summarization Models
        • 21.10.15 GSum: A General Framework for Guided Neural Abstractive Summarization 【NAACL 2021】
        • 21.10.10 VAETransformer进一步实验
        • 21.10.3 CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization 【EMNLP2021】
        • 21.10.1 Controllable Neural Dialogue Summarization with Personal Named Entity Planning 【EMNLP2021】
      • 2021.9
        • 21.9.29 VAE Transformer
        • 21.9.26 Enriching and Controlling Global Semantics for Text Summarization
        • 21.9.25 VAE变分编码器
        • 21.9.24 GAN原理和实践
        • 21.9.22 A Bag of Tricks for Dialogue Summarization【EMNLP2021】
        • 21.9.16 相似度匹配若干新算法
        • 21.9.16 R-Drop
        • 21.9.14 在BERT里Seq2Seq
        • 21.9.13 prompt方法
      • Before
        • GNN in NLP
        • NJU HPC 高性能计算Guide
        • Fairseq 源码分析
        • 对抗学习 in NLP
        • Trick in DL NLP
        • 对比学习
        • 自监督学习Self-Supervise Learning
        • CCL 2020 Note
        • Seq2Seq Translation
      • 奇怪的技巧
    • 懒得整理
      • 方向实践
        • 关系抽取 Relation Extraction
          • Bert in RE
          • Relation Extraction
        • Q&A
          • QA资料
        • 文本摘要
          • Lead算法和Rouge评估实现
          • Match_Sum复现
          • Search strategy
          • BPE Subword
          • 结营报告
        • NER
          • 初试实体识别
          • ChineseBertNer
          • ChineseBertNER2
          • 新数据的BertNer
        • TextMatching
          • ESIM
          • Text_Matching 比赛
          • Text_Matching调参优化
          • Text_Matching总结
      • 其他
        • Neo4j
          • neo4j图形数据库入门
        • Attention
        • 建立Wiki词典
        • 导入Wiki词典
        • 阅读信息
      • NLP's Model
        • Word2Vec
          • Word2vec's Skip_Gram模型
          • Skip_Gram中文应用
          • Skip_Gram模型（补）
          • Skip_Gram中文实用
        • Bert
          • Classification Bert
          • Bert for Ner 代码分析
          • ChineseBertNer
          • 配置Bert
          • Bert初试
          • Bert魔改
          • 服务器部署环境
          • Bert 再战Kaggle（句子分类）
          • Bert pretrain
        • LSTM
          • LSTM 入门（词性标注）
          • LSTM Kaggle 实战（句子分类）
          • LSTM 再战Kaggle（句子分类)
      • NLP's API
        • NLTK入门
        • jieba入门
        • hanlp
    • 以前的论文笔记
      • 事实一致性
        • The Factual Inconsistency Problem in Abstractive Text Summarization A Survey
      • 对话摘要 Dialog Summarization
        • Improving Abstractive Dialogue Summarization with Graph Structures
        • ABSTRACTIVE DIALOG SUMMARIZATION WITH SEMANTIC SCAFFOLDS
      • Two-stage encoding Extractive Summarization
      • Neural Document Summarization by Jointly Learning to Score and Select Sentences
      • CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
      • CoLAKE: Contextualized Language and Knowledge Embedding
      • A Closer Look at Data Bias in Neural Extractive Summarization Models
      • Deep Communicating Agents for Abstractive Summarization
      • Abstractive News Summarization based on Event Semantic Link Network
      • A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
      • BART Denoising
      • What Have We Achieved on Text Summarization?
      • Abstractive Summarization: A Survey of the State of the Art
      • Summary Level Training of Sentence Rewriting for Abstractive Summarization
      • Heterogeneous Graph Neural Networks
      • STRUCTURED NEURAL SUMMARIZATION
      • Text Summarization with Pretrained Encoders
      • SummaRuNNer
      • Attention is All you Need
      • Toward Making the Most of Context in NMT
      • Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
      • Text Summarization Techniques
      • Extractive Summarization as Text Matching
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