Fetching the paper…
Reading the bibliography…
State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions.
Scispacy: Fast and robust models for biomedical natural language processing
Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019 · 1902
Earlier work this paper cites.
Fine-grained sentiment analysis with faithful attention
Ruiqi Zhong, Steven Shao, and Kathleen McKeown. 2019 · 1908
Earlier work this paper cites.
Attention interpretability across nlp tasks
Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, and Manaal Faruqui. 2019 · 1909
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
A coefficient of agreement for nominal scales
Jacob Cohen. 1960 · 1960
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser. 1989 · 1989
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
Earlier work this paper cites.
An improved algorithm for finding the strongly connected components of a directed graph
David J Pearce. 2005 · 2005
Earlier work this paper cites.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
Earlier work this paper cites.
Modeling annotators: A generative approach to learning from annotator rationales
Omar F Zaidan and Jason Eisner. 2008 · 2008
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman. 2010 · 2010
Earlier work this paper cites.
Active learning for biomedical citation screening
Byron C Wallace, Kevin Small, Carla E Brodley, and Thomas A Trikalinos. 2010 · 2010
Earlier work this paper cites.
The constrained weight space svm: learning with ranked features
Kevin Small, Byron C Wallace, Carla E Brodley, and Thomas A Trikalinos. 2011 · 2011
Earlier work this paper cites.
Active learning
Burr Settles. 2012 · 2012
Earlier work this paper cites.
Distributional semantics resources for biomedical text processing
Sampo Pyysalo, F Ginter, Hans Moen, T Salakoski, and Sophia Ananiadou. 2013 · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Active learning with rationales for text classification
Manali Sharma, Di Zhuang, and Mustafa Bilgic. 2015 · 2015
Cited alongside, same era.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Cited alongside, same era.
Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
The mythos of model interpretability
Zachary C Lipton. 2016 · 2016
Cited alongside, same era.
Why is that relevant? collecting annotator rationales for relevance judgments
Tyler McDonnell, Matthew Lease, Mucahid Kutlu, and Tamer Elsayed. 2016 · 2016
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
FEVER: a Large-scale Dataset for Fact Extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
Later among the works it cites.
Interpretable neural predictions with differentiable binary variables
Joost Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
Closest in time.
Scibert: Pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Closest in time.
A game theoretic approach to class-wise selective rationalization
Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola. 2019 · 2019
Closest in time.
Seeing things from a different angle: Discovering diverse perspectives about claims
Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, and Dan Roth. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“why should i trust you?”: Explaining the predictions of any classifier
Marco Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2016 · 2016
Cited alongside, same era.
Rationale-augmented convolutional neural networks for text classification
Ye Zhang, Iain Marshall, and Byron C Wallace. 2016 · 2016
Cited alongside, same era.
A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi Jaakkola. 2017 · 2017
Cited alongside, same era.
”what is relevant in a text document?”: An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
Cited alongside, same era.
Visualizing and understanding neural machine translation
Yanzhuo Ding, Yang Liu, Huanbo Luan, and Maosong Sun. 2017 · 2017
Cited alongside, same era.
Closest in time.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 2019
Closest in time.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Closest in time.
A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim. 2019 · 2019
Closest in time.
Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
Closest in time.
Inferring which medical treatments work from reports of clinical trials
Eric Lehman, Jay DeYoung, Regina Barzilay, and Byron C Wallace. 2019 · 2019
Closest in time.
Interrogating the explanatory power of attention in neural machine translation
Pooya Moradi, Nishant Kambhatla, and Anoop Sarkar. 2019 · 2019
Closest in time.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Closest in time.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Closest in time.
Towards debiasing fact verification models
Tal Schuster, Darsh J Shah, Yun Jie Serene Yeo, Daniel Filizzola, Enrico Santus, and Regina Barzilay. 2019 · 2019
Closest in time.
Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
Closest in time.
Do human rationales improve machine explanations?
Julia Strout, Ye Zhang, and Raymond Mooney. 2019 · 2019
Closest in time.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Closest in time.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Closest in time.
Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola. 2019 · 2019
Closest in time.
On identifiability in transformers
Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer. 2020 · 2020
Closest in time.
Learning to Faithfully Rationalize by Construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. 2020 · 2020
Closest in time.
Learning to deceive with attention-based explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, and Zachary C. Lipton. 2020 · 2020
Closest in time.