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You Can Have Your Data and Balance It Too: Towards Balanced and
  Efficient Multilingual Models

You Can Have Your Data and Balance It Too: Towards Balanced and Efficient Multilingual Models

13 October 2022
Tomasz Limisiewicz
Daniel Malkin
Gabriel Stanovsky
ArXivPDFHTML

Papers citing "You Can Have Your Data and Balance It Too: Towards Balanced and Efficient Multilingual Models"

5 / 5 papers shown
Title
Optimal word order for non-causal text generation with Large Language Models: the Spanish case
Optimal word order for non-causal text generation with Large Language Models: the Spanish case
Andrea Busto-Castiñeira
Silvia García-Méndez
Francisco de Arriba-Pérez
Francisco J. González Castaño
36
0
0
21 Feb 2025
Probing Classifiers: Promises, Shortcomings, and Advances
Probing Classifiers: Promises, Shortcomings, and Advances
Yonatan Belinkov
224
404
0
24 Feb 2021
How Good is Your Tokenizer? On the Monolingual Performance of
  Multilingual Language Models
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
Phillip Rust
Jonas Pfeiffer
Ivan Vulić
Sebastian Ruder
Iryna Gurevych
69
234
0
31 Dec 2020
When Being Unseen from mBERT is just the Beginning: Handling New
  Languages With Multilingual Language Models
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
Benjamin Muller
Antonis Anastasopoulos
Benoît Sagot
Djamé Seddah
LRM
124
165
0
24 Oct 2020
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
6,740
0
26 Sep 2016
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