Research
Computer Vision
Sequence Level Training with Recurrent Neural Networks
November 20, 2015
Abstract
Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. However, at test time the model is expected to generate the entire sequence from scratch. This discrepancy makes generation brittle, as errors may accumulate along the way. We address this issue by proposing a novel sequence level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE. On three different tasks, our approach outperforms several strong baselines for greedy generation. The method is also competitive when these baselines employ beam search, while being several times faster.
AUTHORS
Publisher
Related Publications
June 11, 2025
Computer Vision
IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
Florian Bordes, Quentin Garrido, Justine Kao, Adina Williams, Mike Rabbat, Emmanuel Dupoux
June 11, 2025
June 10, 2025
Computer Vision
A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mido Assran
June 10, 2025
June 10, 2025
Robotics
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, Nicolas Ballas
June 10, 2025
April 14, 2025
Graphics
Autoregressive Distillation of Diffusion Transformers
Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schoenfeld, Jonas Kohler, Markos Georgopoulos, Albert Pumarola, Ali Thabet, Artsiom Sanakoyeu
April 14, 2025
April 30, 2018
Computer Vision
NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research
Yedid Hoshen, Lior Wolf
April 30, 2018
December 11, 2019
Speech & Audio
Computer Vision
Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research
Eliya Nachmani, Lior Wolf
December 11, 2019
April 30, 2018
NLP
Speech & Audio
Identifying Analogies Across Domains | Facebook AI Research
Yedid Hoshen, Lior Wolf
April 30, 2018
November 01, 2018
NLP
Computer Vision
Non-Adversarial Unsupervised Word Translation | Facebook AI Research
Yedid Hoshen, Lior Wolf
November 01, 2018
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