Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
暂无分享,去创建一个
[1] Yu Cheng,et al. Discourse-Aware Neural Extractive Text Summarization , 2020, ACL.
[2] Mirella Lapata,et al. Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization , 2018, EMNLP.
[3] Franck Dernoncourt,et al. Analyzing Sentence Fusion in Abstractive Summarization , 2019, EMNLP.
[4] Jasmijn Bastings,et al. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? , 2020, BLACKBOXNLP.
[5] Franck Dernoncourt,et al. Learning to Fuse Sentences with Transformers for Summarization , 2020, EMNLP.
[6] Yejin Choi,et al. Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics , 2020, EMNLP.
[7] Fei Liu,et al. Controlling the Amount of Verbatim Copying in Abstractive Summarization , 2019, AAAI.
[8] Leonidas J. Guibas,et al. A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[9] Yoav Goldberg,et al. Understanding Convolutional Neural Networks for Text Classification , 2018, BlackboxNLP@EMNLP.
[10] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[11] Omer Levy,et al. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension , 2019, ACL.
[12] Graham Neubig,et al. Learning to Deceive with Attention-Based Explanations , 2020, ACL.
[13] Ankur P. Parikh,et al. Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation , 2019, ArXiv.
[14] Katja Filippova. Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data , 2020, FINDINGS.
[15] Kathleen McKeown,et al. Content Selection in Deep Learning Models of Summarization , 2018, EMNLP.
[16] Tommi S. Jaakkola,et al. Towards Robust Interpretability with Self-Explaining Neural Networks , 2018, NeurIPS.
[17] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[18] Danqi Chen,et al. of the Association for Computational Linguistics: , 2001 .
[19] Dong Nguyen,et al. Comparing Automatic and Human Evaluation of Local Explanations for Text Classification , 2018, NAACL.
[20] Rico Sennrich,et al. Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation , 2020, ACL.
[21] Jiacheng Xu,et al. Neural Extractive Text Summarization with Syntactic Compression , 2019, EMNLP.
[22] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[23] Ryan McDonald,et al. On Faithfulness and Factuality in Abstractive Summarization , 2020, ACL.
[24] Yangfeng Ji,et al. Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection , 2020, ACL.
[25] Colin Raffel,et al. Extracting Training Data from Large Language Models , 2020, USENIX Security Symposium.
[26] Philipp Koehn,et al. An Analysis of Source Context Dependency in Neural Machine Translation , 2018, EAMT.
[27] Kathleen McKeown,et al. Supervised Sentence Fusion with Single-Stage Inference , 2013, IJCNLP.
[28] Mirella Lapata,et al. Text Summarization with Pretrained Encoders , 2019, EMNLP.
[29] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[30] Tanya Goyal,et al. Annotating and Modeling Fine-grained Factuality in Summarization , 2021, NAACL.
[31] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[32] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[33] Richard Socher,et al. Evaluating the Factual Consistency of Abstractive Text Summarization , 2019, EMNLP.
[34] Yejin Choi,et al. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models , 2020, FINDINGS.
[35] Yoav Goldberg,et al. Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? , 2020, ACL.
[36] Xuanjing Huang,et al. Searching for Effective Neural Extractive Summarization: What Works and What’s Next , 2019, ACL.
[37] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[38] Sebastian Riedel,et al. Language Models as Knowledge Bases? , 2019, EMNLP.
[39] Phil Blunsom,et al. Teaching Machines to Read and Comprehend , 2015, NIPS.
[40] Byron C. Wallace,et al. ERASER: A Benchmark to Evaluate Rationalized NLP Models , 2020, ACL.
[41] Yotam Hechtlinger,et al. Interpretation of Prediction Models Using the Input Gradient , 2016, ArXiv.
[42] Yuval Pinter,et al. Attention is not not Explanation , 2019, EMNLP.
[43] Byron C. Wallace,et al. Attention is not Explanation , 2019, NAACL.
[44] Regina Barzilay,et al. Sentence Fusion for Multidocument News Summarization , 2005, CL.
[45] Alec Radford,et al. Learning to summarize from human feedback , 2020, NeurIPS.
[46] Tim Miller,et al. Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..
[47] Jonathan Berant,et al. oLMpics-On What Language Model Pre-training Captures , 2019, Transactions of the Association for Computational Linguistics.
[48] Christopher D. Manning,et al. Get To The Point: Summarization with Pointer-Generator Networks , 2017, ACL.
[49] Percy Liang,et al. Understanding Black-box Predictions via Influence Functions , 2017, ICML.
[50] Yulia Tsvetkov,et al. Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions , 2020, ACL.
[51] Fandong Meng,et al. Prevent the Language Model from being Overconfident in Neural Machine Translation , 2021, ACL.
[52] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[53] Sameer Singh,et al. Eliciting Knowledge from Language Models Using Automatically Generated Prompts , 2020, EMNLP.
[54] Yao Zhao,et al. PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization , 2020, ICML.
[55] Richard Socher,et al. Neural Text Summarization: A Critical Evaluation , 2019, EMNLP.
[56] Lemao Liu,et al. Evaluating Explanation Methods for Neural Machine Translation , 2020, ACL.
[57] Shrey Desai,et al. Compressive Summarization with Plausibility and Salience Modeling , 2020, EMNLP.
[58] Thomas Wolf,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[59] Shrey Desai,et al. Understanding Neural Abstractive Summarization Models via Uncertainty , 2020, EMNLP.
[60] Greg Durrett,et al. Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals , 2021, ArXiv.