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Quantifying the Contextualization of Word Representations with Semantic
  Class Probing

Quantifying the Contextualization of Word Representations with Semantic Class Probing

25 April 2020
Mengjie Zhao
Philipp Dufter
Yadollah Yaghoobzadeh
Hinrich Schütze
ArXivPDFHTML

Papers citing "Quantifying the Contextualization of Word Representations with Semantic Class Probing"

10 / 10 papers shown
Title
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
Muhammad Jehanzeb Mirza
Mengjie Zhao
Zhuoyuan Mao
Sivan Doveh
Wei Lin
...
Yuki Mitsufuji
Horst Possegger
Rogerio Feris
Leonid Karlinsky
James Glass
VLM
84
1
0
08 Oct 2024
SensePOLAR: Word sense aware interpretability for pre-trained contextual
  word embeddings
SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings
Jan Engler
Sandipan Sikdar
Marlene Lutz
M. Strohmaier
32
7
0
11 Jan 2023
Visual Comparison of Language Model Adaptation
Visual Comparison of Language Model Adaptation
Rita Sevastjanova
E. Cakmak
Shauli Ravfogel
Ryan Cotterell
Mennatallah El-Assady
VLM
41
16
0
17 Aug 2022
Metaphors in Pre-Trained Language Models: Probing and Generalization
  Across Datasets and Languages
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages
Ehsan Aghazadeh
Mohsen Fayyaz
Yadollah Yaghoobzadeh
33
51
0
26 Mar 2022
Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces
  with Pseudowords
Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords
Taelin Karidi
Yichu Zhou
Nathan Schneider
Omri Abend
Vivek Srikumar
86
13
0
23 Sep 2021
Positional Artefacts Propagate Through Masked Language Model Embeddings
Positional Artefacts Propagate Through Masked Language Model Embeddings
Ziyang Luo
Artur Kulmizev
Xiaoxi Mao
29
41
0
09 Nov 2020
The Bottom-up Evolution of Representations in the Transformer: A Study
  with Machine Translation and Language Modeling Objectives
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
Elena Voita
Rico Sennrich
Ivan Titov
198
181
0
03 Sep 2019
What you can cram into a single vector: Probing sentence embeddings for
  linguistic properties
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau
Germán Kruszewski
Guillaume Lample
Loïc Barrault
Marco Baroni
201
882
0
03 May 2018
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language
  Understanding
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Jinpeng Wang
Amanpreet Singh
Julian Michael
Felix Hill
Omer Levy
Samuel R. Bowman
ELM
297
6,959
0
20 Apr 2018
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Z. Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
716
6,746
0
26 Sep 2016
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