Fetching the paper…
Reading the bibliography…
Decisions of complex language understanding models can be rationalized by limiting their inputs to a relevant subsequence of the original text.
Explaining a black-box using deep variational information bottleneck approach
Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, and Eric Xing. 2019 · 1902
Earlier work this paper cites.
Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 1903
Earlier work this paper cites.
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 · 1905
Earlier work this paper cites.
Eraser: A benchmark to evaluate rationalized nlp models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. 2019 · 1911
Earlier work this paper cites.
Statistical theory of extreme values and some practical applications: a series of lectures , volume 33
Emil Julius Gumbel. 1948 · 1948
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Estimation of reservoir yield and storage distribution using moments analysis
SG Fletcher and K Ponnambalam. 1996 · 1996
Earlier work this paper cites.
The information bottleneck method
Naftali Tishby, Fernando C. Pereira, and William Bialek. 1999 · 1999
Earlier work this paper cites.
Explaining question answering models through text generation
Veronica Latcinnik and Jonathan Berant. 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.
Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C Wallace. 2020 · 2005
Earlier work this paper cites.
Learning attitudes and attributes from multi-aspect reviews
Julian McAuley, Jure Leskovec, and Dan Jurafsky. 2012 · 2012
Earlier work this paper cites.
Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling. 2015 · 2015
Cited alongside, same era.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016 · 2016
Cited alongside, same era.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Cited alongside, same era.
” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Information-bottleneck approach to salient region discovery
Andrey Zhmoginov, Ian Fischer, and Mark Sandler. 2019 · 2016
Cited alongside, same era.
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
Later among the works it cites.
A game theoretic approach to class-wise selective rationalization
Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola. 2019 · 2019
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Inferring which medical treatments work from reports of clinical trials
Eric Lehman, Jay DeYoung, Regina Barzilay, and Byron C Wallace. 2019 · 2019
Later among the works it cites.
Specializing word embeddings (for parsing) by information bottleneck
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Categorical reparametrization with gumble-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
Cited alongside, same era.
A workflow for visual diagnostics of binary classifiers using instance-level explanations
Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanaphongs, and Enrico Bertini. 2017 · 2017
Cited alongside, same era.
Stick-breaking variational autoencoders
Eric Nalisnick and Padhraic Smyth. 2017 · 2017
Cited alongside, same era.
e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Cited alongside, same era.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
Cited alongside, same era.
Xiang Lisa Li and Jason Eisner. 2019 · 2019
Later among the works it cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Later among the works it cites.
Is attention interpretable?
Sofia Serrano and Noah A Smith. 2019 · 2019
Later among the works it cites.
The challenge of crafting intelligible intelligence
Daniel S Weld and Gagan Bansal. 2019 · 2019
Later among the works it cites.
Bottlesum: Unsupervised and self-supervised sentence summarization using the information bottleneck principle
Peter West, Ari Holtzman, Jan Buys, and Yejin Choi. 2019 · 2019
Later among the works it cites.
Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola. 2019 · 2019
Later among the works it cites.