Learning to Deceive with Attention-Based Explanations

Bias in bios: A case study of semantic representation bias in a high-stakes setting

Original

Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 1901

Earlier work this paper cites.

Attention, please! a critical review of neural attention models in natural language processing

Original

Andrea Galassi, Marco Lippi, and Paolo Torroni. 2019 · 1902

Earlier work this paper cites.

On the validity of self-attention as explanation in transformer models

Original

Gino Brunner, Yang Liu, Damián Pascual, Oliver Richter, and Roger Wattenhofer. 2019 · 1908

Earlier work this paper cites.

Framewise phoneme classification with bidirectional lstm and other neural network architectures

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Earlier work this paper cites.

A simple, fast, and effective reparameterization of ibm model 2

Chris Dyer, Victor Chahuneau, and Noah A Smith. 2013 · 2013

Earlier work this paper cites.

Recursive deep models for semantic compositionality over a sentiment treebank

Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013 · 2013

Earlier work this paper cites.

Neural machine translation by jointly learning to align and translate

Original

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014

Earlier work this paper cites.

Learning to transduce with unbounded memory

Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015

Earlier work this paper cites.

Show, attend and tell: Neural image caption generation with visual attention

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Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio. 2015 · 2015

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Guided alignment training for topic-aware neural machine translation

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Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. 2016 · 2016

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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism

Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016 · 2016

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Multi30k: Multilingual english-german image descriptions

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Desmond Elliott, Stella Frank, Khalil Sima’an, and Lucia Specia. 2016 · 2016

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Understanding neural networks through representation erasure

An interpretable knowledge transfer model for knowledge base completion

Original

Qizhe Xie, Xuezhe Ma, Zihang Dai, and Eduard Hovy. 2017 · 2017

Later among the works it cites.

Sequence classification with human attention

Maria Barrett, Joachim Bingel, Nora Hollenstein, Marek Rei, and Anders Søgaard. 2018 · 2018

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

Closest in time.

Attention is not explanation

Sarthak Jain, Ramin Mohammadi, and Byron C Wallace. 2019 · 2019

Closest in time.

On human predictions with explanations and predictions of machine learning models: A case study on deception detection

Vivian Lai and Chenhao Tan. 2019 · 2019

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Is attention interpretable?