📰 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP 2018: all accepted papers

56 papers · the most cited first · ← EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP 2018

1. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language UnderstandingAlex Wang, Amanpreet Singh, Julian Michael et al.4,074 citations · our summary
2. An Analysis of Encoder Representations in Transformer-Based Machine TranslationAlessandro Raganato, Jörg Tiedemann265 citations · our summary
3. Understanding Convolutional Neural Networks for Text Classification ↗Alon Jacovi, Oren Sar Shalom, Yoav Goldberg226 citations
4. On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis ↗Jose Camacho-Collados, Mohammad Taher Pilehvar164 citations
5. What do RNN Language Models Learn about Filler–Gap Dependencies?Ethan Wilcox, Roger Levy, Takashi Morita et al.154 citations · our summary
6. Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis ↗Kelly Zhang, Samuel R. Bowman112 citations
7. Rearranging the Familiar: Testing Compositional Generalization in Recurrent NetworksJoão Loula, Marco Baroni, Brenden Lake106 citations · our summary
8. Importance of Self-Attention for Sentiment Analysis ↗Gaël Letarte, Frédérik Paradis, Philippe Giguère et al.67 citations
9. Interpreting Neural Networks with Nearest Neighbors ↗Eric Wallace, Shi Feng, Jordan Boyd-Graber64 citations
10. Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items ↗Jaap Jumelet, Dieuwke Hupkes61 citations
11. Evaluating the Ability of LSTMs to Learn Context-Free Grammars ↗Luzi Sennhauser, Robert Berwick51 citations
12. An Operation Sequence Model for Explainable Neural Machine Translation ↗Felix Stahlberg, Danielle Saunders, Bill Byrne47 citations
13. Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation ↗Adam Poliak, Aparajita Haldar, Rachel Rudinger et al.42 citations
14. Can LSTM Learn to Capture Agreement? The Case of Basque ↗Shauli Ravfogel, Yoav Goldberg, Francis Tyers38 citations
15. Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell ↗Pia Sommerauer, Antske Fokkens33 citations
16. Does Syntactic Knowledge in Multilingual Language Models Transfer Across Languages? ↗Prajit Dhar, Arianna Bisazza31 citations
17. Extracting Syntactic Trees from Transformer Encoder Self-Attentions ↗David Mareček, Rudolf Rosa31 citations
18. Context-Free Transductions with Neural Stacks ↗Yiding Hao, William Merrill, Dana Angluin et al.25 citations
19. Closing Brackets with Recurrent Neural Networks ↗Natalia Skachkova, Thomas Trost, Dietrich Klakow24 citations
20. Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style ↗Reid Pryzant, Sugato Basu, Kazoo Sone22 citations
21. LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation ↗Pankaj Gupta, Hinrich Schütze22 citations
22. Analyzing Learned Representations of a Deep ASR Performance Prediction Model ↗Zied Elloumi, Laurent Besacier, Olivier Galibert et al.21 citations
23. Interpretable Textual Neuron Representations for NLP ↗Nina Poerner, Benjamin Roth, Hinrich Schütze21 citations
24. Predicting and interpreting embeddings for out of vocabulary words in downstream tasks ↗Nicolas Garneau, Jean-Samuel Leboeuf, Luc Lamontagne21 citations
25. Grammar Induction with Neural Language Models: An Unusual Replication ↗Phu Mon Htut, Kyunghyun Cho, Samuel R. Bowman17 citations
26. Introspection for convolutional automatic speech recognition ↗Andreas Krug, Sebastian Stober16 citations
27. Interpretable Structure Induction via Sparse Attention ↗Ben Peters, Vlad Niculae, André F. T. Martins15 citations
28. Learning and Evaluating Sparse Interpretable Sentence Embeddings ↗Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko et al.15 citations
29. Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures ↗Danilo Croce, Daniele Rossini, Roberto Basili14 citations
30. Nightmare at test time: How punctuation prevents parsers from generalizing ↗Anders Søgaard, Miryam de Lhoneux, Isabelle Augenstein14 citations
31. `Indicatements' that character language models learn English morpho-syntactic units and regularities ↗Yova Kementchedjhieva, Adam Lopez12 citations
32. Iterative Recursive Attention Model for Interpretable Sequence Classification ↗Martin Tutek, Jan Šnajder11 citations
33. Language Models Learn POS First ↗Naomi Saphra, Adam Lopez11 citations
34. Evaluating Textual Representations through Image Generation ↗Graham Spinks, Marie-Francine Moens10 citations
35. Debugging Sequence-to-Sequence Models with Seq2Seq-Vis ↗Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch et al.9 citations
36. Exploiting Attention to Reveal Shortcomings in Memory Models ↗Kaylee Burns, Aida Nematzadeh, Erin Grant et al.9 citations
37. Learning Explanations from Language Data ↗David Harbecke, Robert Schwarzenberg, Christoph Alt9 citations
38. End-to-end Image Captioning Exploits Distributional Similarity in Multimodal Space ↗Pranava Swaroop Madhyastha, Josiah Wang, Lucia Specia8 citations
39. Linguistic representations in multi-task neural networks for ellipsis resolution ↗Ola Rønning, Daniel Hardt, Anders Søgaard8 citations
40. Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models ↗Avery Hiebert, Cole Peterson, Alona Fyshe et al.7 citations
41. Probing sentence embeddings for structure-dependent tense ↗Geoff Bacon, Terry Regier7 citations
42. State Gradients for RNN Memory Analysis ↗Lyan Verwimp, Hugo Van hamme, Vincent Renkens et al.7 citations
43. Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models ↗Shun Kiyono, Sho Takase, Jun Suzuki et al.7 citations
44. Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue ↗Dieuwke Hupkes, Sanne Bouwmeester, Raquel Fernández6 citations
45. Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information ↗Mario Giulianelli, Jack Harding, Florian Mohnert et al.6 citations
46. When does deep multi-task learning work for loosely related document classification tasks? ↗Emma Kerinec, Chloé Braud, Anders Søgaard6 citations
47. Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System ↗Barbara Rychalska, Dominika Basaj, Anna Wróblewska et al.5 citations
48. Jump to better conclusions: SCAN both left and right ↗Jasmijn Bastings, Marco Baroni, Jason Weston et al.5 citations
49. Limitations in learning an interpreted language with recurrent models ↗Denis Paperno5 citations
50. Evaluating Grammaticality in Seq2seq Models with a Broad Coverage HPSG Grammar: A Case Study on Machine Translation ↗Johnny Wei, Khiem Pham, Brendan O'Connor et al.3 citations
51. Explicitly modeling case improves neural dependency parsing ↗Clara Vania, Adam Lopez2 citations
52. How much should you ask? On the question structure in QA systems. ↗Barbara Rychalska, Dominika Basaj, Anna Wróblewska et al.2 citations
53. Interpretable Word Embedding Contextualization ↗Kyoung-Rok Jang, Sung-Hyon Myaeng, Sang-Bum Kim2 citations
54. Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model ↗Steven Derby, Paul Miller, Brian Murphy et al.2 citations
55. Rule induction for global explanation of trained models ↗Madhumita Sushil, Simon Šuster, Walter Daelemans2 citations
56. Portable, layer-wise task performance monitoring for NLP models ↗Tom Lippincott

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