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1910.07181
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BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
16 October 2019
Timo Schick
Hinrich Schütze
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Papers citing
"BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance"
7 / 7 papers shown
Title
HYPEROFA: Expanding LLM Vocabulary to New Languages via Hypernetwork-Based Embedding Initialization
Enes Özeren
Yihong Liu
Hinrich Schütze
31
0
0
21 Apr 2025
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations
Aina Garí Soler
Matthieu Labeau
Chloé Clavel
VLM
42
2
0
22 Feb 2024
Unsupervised Paraphrasing of Multiword Expressions
Takashi Wada
Yuji Matsumoto
Timothy Baldwin
Jey Han Lau
24
0
0
02 Jun 2023
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises
Chenglei Si
Zhengyan Zhang
Yingfa Chen
Xiaozhi Wang
Zhiyuan Liu
Maosong Sun
AAML
26
1
0
14 Feb 2023
Searching for Optimal Subword Tokenization in Cross-domain NER
Ruotian Ma
Yiding Tan
Xin Zhou
Xuanting Chen
Di Liang
Sirui Wang
Wei Yu Wu
Tao Gui
Qi Zhang
OOD
46
14
0
07 Jun 2022
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Bonan Min
Hayley L Ross
Elior Sulem
Amir Pouran Ben Veyseh
Thien Huu Nguyen
Oscar Sainz
Eneko Agirre
Ilana Heinz
Dan Roth
LM&MA
VLM
AI4CE
74
1,030
0
01 Nov 2021
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
6,743
0
26 Sep 2016
1