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The POLAR Framework: Polar Opposites Enable Interpretability of
  Pre-Trained Word Embeddings

The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

27 January 2020
Binny Mathew
Sandipan Sikdar
Florian Lemmerich
M. Strohmaier
ArXivPDFHTML

Papers citing "The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings"

12 / 12 papers shown
Title
Profiling Bias in LLMs: Stereotype Dimensions in Contextual Word Embeddings
Profiling Bias in LLMs: Stereotype Dimensions in Contextual Word Embeddings
Carolin M. Schuster
Maria-Alexandra Dinisor
Shashwat Ghatiwala
Georg Groh
100
1
0
25 Nov 2024
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Max Klabunde
Tobias Schumacher
M. Strohmaier
Florian Lemmerich
88
67
0
10 May 2023
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word
  Semantics Beyond Sentiment
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
Jisun An
Haewoon Kwak
Yong-Yeol Ahn
41
64
0
14 Jun 2018
SPINE: SParse Interpretable Neural Embeddings
SPINE: SParse Interpretable Neural Embeddings
Anant Subramanian
Danish Pruthi
Harsh Jhamtani
Taylor Berg-Kirkpatrick
Eduard H. Hovy
20
130
0
23 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated
  Decisions and the GDPR
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter
Brent Mittelstadt
Chris Russell
MLAU
48
2,332
0
01 Nov 2017
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy
  Detection
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
Vered Shwartz
Enrico Santus
Dominik Schlechtweg
25
102
0
14 Dec 2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word
  Embeddings
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi
Kai-Wei Chang
James Zou
Venkatesh Saligrama
Adam Kalai
CVBM
FaML
40
3,115
0
21 Jul 2016
Image Captioning with Semantic Attention
Image Captioning with Semantic Attention
Quanzeng You
Hailin Jin
Zhaowen Wang
Chen Fang
Jiebo Luo
VLM
157
1,653
0
12 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
420
16,765
0
16 Feb 2016
Sparse Overcomplete Word Vector Representations
Sparse Overcomplete Word Vector Representations
Manaal Faruqui
Yulia Tsvetkov
Dani Yogatama
Chris Dyer
Noah A. Smith
48
195
0
05 Jun 2015
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity
  Estimation
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation
Felix Hill
Roi Reichart
Anna Korhonen
64
1,302
0
15 Aug 2014
Distributed Representations of Words and Phrases and their
  Compositionality
Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov
Ilya Sutskever
Kai Chen
G. Corrado
J. Dean
NAI
OCL
258
33,445
0
16 Oct 2013
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