📰 BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP 2020: all accepted papers

32 papers · the most cited first · ← BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP 2020

1. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?Jasmijn Bastings, Katja Filippova150 citations · our summary
2. What Happens To BERT Embeddings During Fine-tuning?Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick et al.139 citations · our summary
3. BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance ↗R. Thomas McCoy, Junghyun Min, Tal Linzen98 citations
4. How does BERT capture semantics? A closer look at polysemous words ↗David Yenicelik, Florian Schmidt, Yannic Kilcher50 citations
5. Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and NegationAtticus Geiger, Kyle Richardson, Christopher Potts44 citations · our summary
6. Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples ↗Jin Yong Yoo, John Morris, Eli Lifland et al.43 citations
7. It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT ↗Hila Gonen, Shauli Ravfogel, Yanai Elazar et al.40 citations
8. BERTnesia: Investigating the capture and forgetting of knowledge in BERT ↗Jonas Wallat, Jaspreet Singh, Avishek Anand30 citations
9. Discovering the Compositional Structure of Vector Representations with Role Learning Networks ↗Paul Soulos, R. Thomas McCoy, Tal Linzen et al.26 citations
10. Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets ↗Chuanrong Li, Lin Shengshuo, Zeyu Liu et al.25 citations
11. The Explanation Game: Towards Prediction Explainability through Sparse Communication ↗Marcos Treviso, André F. T. Martins25 citations
12. Do Language Embeddings capture Scales? ↗Xikun Zhang, Deepak Ramachandran, Ian Tenney et al.22 citations
13. The EOS Decision and Length Extrapolation ↗Benjamin Newman, John Hewitt, Percy Liang et al.22 citations
14. On the Interplay Between Fine-tuning and Sentence-Level Probing for Linguistic Knowledge in Pre-Trained Transformers ↗Marius Mosbach, Anna Khokhlova, Michael A. Hedderich et al.17 citations
15. Emergent Language Generalization and Acquisition Speed are not tied to Compositionality ↗Eugene Kharitonov, Marco Baroni16 citations
16. Structured Self-Attention Weights Encode Semantics in Sentiment Analysis ↗Zhengxuan Wu, Thanh-Son Nguyen, Desmond C. Ong16 citations
17. Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization ↗Tristan Thrush, Ethan Wilcox, Roger Levy13 citations
18. Probing for Multilingual Numerical Understanding in Transformer-Based Language Models ↗Devin Johnson, Denise Mak, Andrew Barker et al.13 citations
19. This is a BERT. Now there are several of them. Can they generalize to novel words? ↗Coleman Haley11 citations
20. Defining Explanation in an AI Context ↗Tejaswani Verma, Christoph Lingenfelder, Dietrich Klakow9 citations
21. Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation ↗Rajiv Movva, Jason Zhao9 citations
22. Examining the rhetorical capacities of neural language models ↗Zining Zhu, Chuer Pan, Mohamed Abdalla et al.8 citations
23. Unsupervised Distillation of Syntactic Information from Contextualized Word Representations ↗Shauli Ravfogel, Yanai Elazar, Jacob Goldberger et al.8 citations
24. Evaluating Attribution Methods using White-Box LSTMs ↗Yiding Hao7 citations
25. diagNNose: A Library for Neural Activation Analysis ↗Jaap Jumelet7 citations
26. Exploring Neural Entity Representations for Semantic Information ↗Andrew Runge, Eduard Hovy5 citations
27. Unsupervised Evaluation for Question Answering with Transformers ↗Lukas Muttenthaler, Isabelle Augenstein, Johannes Bjerva4 citations
28. Controlling the Imprint of Passivization and Negation in Contextualized Representations ↗Hande Celikkanat, Sami Virpioja, Jörg Tiedemann et al.3 citations
29. BERTering RAMS: What and How Much does BERT Already Know About Event Arguments? - A Study on the RAMS Dataset ↗Varun Gangal, Eduard Hovy2 citations
30. Latent Tree Learning with Ordered Neurons: What Parses Does It Produce? ↗Yian Zhang1 citation
31. Second-Order NLP Adversarial Examples ↗John Morris1 citation
32. Leveraging Extracted Model Adversaries for Improved Black Box Attacks ↗Naveen Jafer Nizar, Ari Kobren

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