This Reads Like That: Deep Learning for Interpretable Natural Language Processing

25 October 2023

Papers citing "This Reads Like That: Deep Learning for Interpretable Natural Language Processing"

21 / 21 papers shown

Title
SGPT: GPT Sentence Embeddings for Semantic Search Niklas Muennighoff RALM 158 189 0 17 Feb 2022
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks Adrian Hoffmann Claudio Fanconi Rahul Rade Jonas Köhler 59 63 0 05 May 2021
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation Francisco Vargas Ryan Cotterell 71 29 0 20 Sep 2020
Aligning Faithful Interpretations with their Social Attribution Alon Jacovi Yoav Goldberg 62 106 0 01 Jun 2020
Quantifying Attention Flow in Transformers Samira Abnar Willem H. Zuidema 169 803 0 02 May 2020
Learning to Faithfully Rationalize by Construction Sarthak Jain Sarah Wiegreffe Yuval Pinter Byron C. Wallace 82 164 0 30 Apr 2020
MPNet: Masked and Permuted Pre-training for Language Understanding Kaitao Song Xu Tan Tao Qin Jianfeng Lu Tie-Yan Liu 111 1,138 0 20 Apr 2020
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter Victor Sanh Lysandre Debut Julien Chaumond Thomas Wolf 262 7,554 0 02 Oct 2019
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers Iryna Gurevych 1.3K 12,316 0 27 Aug 2019
Interpretable and Steerable Sequence Learning via Prototypes Yao Ming Panpan Xu Huamin Qu Liu Ren AI4TS 63 141 0 23 Jul 2019
Interpretable Neural Predictions with Differentiable Binary Variables Jasmijn Bastings Wilker Aziz Ivan Titov 89 214 0 20 May 2019
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet Wieland Brendel Matthias Bethge SSL FAtt 109 561 0 20 Mar 2019
Attention is not Explanation Sarthak Jain Byron C. Wallace FAtt 148 1,330 0 26 Feb 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 104 1,021 0 26 Feb 2019
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 255 1,187 0 27 Jun 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 248 1,849 0 30 Nov 2017
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions Oscar Li Hao Liu Chaofan Chen Cynthia Rudin 193 593 0 13 Oct 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 158 1,526 1 19 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,027 0 04 Mar 2017
Understanding Neural Networks through Representation Erasure Jiwei Li Will Monroe Dan Jurafsky AAML MILM 105 567 0 24 Dec 2016
Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio AIMat 580 27,338 0 01 Sep 2014