RESEARCH
SPEECH & AUDIO
Multilingual seq2seq training with similarity loss for cross-lingual document classification
July 20, 2018
Abstract
In this paper we continue the line of work where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings. In this framework we introduce a simple method of adding a loss to the learning objective which penalizes distance between representations of bilingually aligned sentences. We evaluate cross-lingual transfer using two approaches, cross-lingual similarity search on an aligned corpus (Europarl) and cross-lingual document classification on a recently published benchmark Reuters corpus, and we find the similarity loss significantly improves performance on both. Our cross-lingual transfer performance is competitive with stateof-the-art, even while there is potential to further improve by investing in a better inlanguage baseline. Our results are based on a set of 6 European languages.
AUTHORS
Publisher
RepL4NLP Workshop
Related Publications
October 19, 2025
RESEARCH
NLP
Controlling Multimodal LLMs via Reward-guided Decoding
Oscar Mañas, Pierluca D'Oro, Koustuv Sinha, Adriana Romero Soriano, Michal Drozdzal, Aishwarya Agrawal
October 19, 2025
October 13, 2025
REINFORCEMENT LEARNING
RESEARCH
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi Jaakkola, Yuandong Tian, Bo Liu
October 13, 2025
September 24, 2025
RESEARCH
NLP
CWM: An Open-Weights LLM for Research on Code Generation with World Models
Jade Copet, Quentin Carbonneaux, Gal Cohen, Jonas Gehring, Jacob Kahn, Jannik Kossen, Felix Kreuk, Emily McMilin, Michel Meyer, Yuxiang Wei, David Zhang, Kunhao Zheng, Jordi Armengol Estape, Pedram Bashiri, Maximilian Beck, Pierre Chambon, Abhishek Charnalia, Chris Cummins, Juliette Decugis, Zacharias Fisches, François Fleuret, Fabian Gloeckle, Alex Gu, Michael Hassid, Daniel Haziza, Badr Youbi Idrissi, Christian Keller, Rahul Kindi, Hugh Leather, Gallil Maimon, Aram Markosyan, Francisco Massa, Pierre-Emmanuel Mazaré, Vegard Mella, Naila Murray, Keyur Muzumdar, Peter O'Hearn, Matteo Pagliardini, Dmitrii Pedchenko, Tal Remez, Volker Seeker, Marco Selvi, Oren Sultan, Sida Wang, Luca Wehrstedt, Ori Yoran, Lingming Zhang, Taco Cohen, Yossi Adi, Gabriel Synnaeve
September 24, 2025
September 24, 2025
CONVERSATIONAL AI
REINFORCEMENT LEARNING
Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
Dulhan Jayalath, Shashwat Goel, Thomas Simon Foster, Parag Jain, Suchin Gururangan, Cheng Zhang, Anirudh Goyal, Alan Schelten
September 24, 2025
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2025