ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2402.17016
  4. Cited By
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings

Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings

26 February 2024
Isabelle Mohr
Markus Krimmel
Saba Sturua
Mohammad Kalim Akram
Andreas Koukounas
Michael Gunther
Georgios Mastrapas
Vinit Ravishankar
Joan Fontanals Martínez
Feng Wang
Qi Liu
Ziniu Yu
Jie Fu
Saahil Ognawala
Susana Guzman
Bo Wang
Maximilian Werk
Nan Wang
Han Xiao
ArXivPDFHTML

Papers citing "Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings"

8 / 8 papers shown
Title
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End
  Question Answering
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
Priyanka Sen
Alham Fikri Aji
Amir Saffari
LRM
97
42
0
04 Oct 2022
Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of
  Multilingual Language Models
Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models
Terra Blevins
Hila Gonen
Luke Zettlemoyer
LRM
42
26
0
24 May 2022
MFAQ: a Multilingual FAQ Dataset
MFAQ: a Multilingual FAQ Dataset
Maxime De Bruyn
Ehsan Lotfi
Jeska Buhmann
Walter Daelemans
RALM
28
21
0
27 Sep 2021
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural
  Machine Translation
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
Haoran Xu
Benjamin Van Durme
Kenton W. Murray
39
57
0
09 Sep 2021
Train Short, Test Long: Attention with Linear Biases Enables Input
  Length Extrapolation
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
Ofir Press
Noah A. Smith
M. Lewis
234
690
0
27 Aug 2021
Deduplicating Training Data Makes Language Models Better
Deduplicating Training Data Makes Language Models Better
Katherine Lee
Daphne Ippolito
A. Nystrom
Chiyuan Zhang
Douglas Eck
Chris Callison-Burch
Nicholas Carlini
SyDa
234
447
0
14 Jul 2021
Rethinking embedding coupling in pre-trained language models
Rethinking embedding coupling in pre-trained language models
Hyung Won Chung
Thibault Févry
Henry Tsai
Melvin Johnson
Sebastian Ruder
87
142
0
24 Oct 2020
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
294
6,927
0
20 Apr 2018
1