Research
NLP
Hierarchical Text Generation and Planning for Strategic Dialogue
July 10, 2018
Abstract
End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors. We introduce an approach to learning representations of messages in dialogues by maximizing the likelihood of subsequent sentences and actions, which decouples the semantics of the dialogue utterance from its linguistic realization. We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning. Experiments show that our approach increases the end-task reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language. Our hierarchical latent-variable model outperforms previous work both linguistically and strategically.
AUTHORS
Related Publications
May 12, 2026
Human & Machine Intelligence
NeuralSet: A High-Performing Python Package for Neuro-AI
Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
March 17, 2026
NLP
Omnilingual MT: Machine Translation for 1,600 Languages
Omnilingual MT Team, Niyati Bafna, Ioannis Tsiamas, Mark Duppenthaler, Albert Ventayol-Boada, Alexandre Mourachko, Andrea Caciolai, Arina Turkatenko, Artyom Kozhevnikov, Belen Alastruey, Charles-Eric Saint-James, Chierh CHENG, Christophe Ropers, Cynthia Gao, David Dale, Edan Toledo, Eduardo Sánchez, Gabriel Mejia Gonzalez, Holger Schwenk, Jean Maillard, Joe Chuang, João Maria Janeiro, Kevin Heffernan, Marta R. Costa-jussa, Mary Williamson, Nate Ekberg, Paul-Ambroise Duquenne, Pere Lluís Huguet Cabot, Rashel Moritz, Shireen Yates, Surya Parimi
March 17, 2026
March 17, 2026
Speech & Audio
Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech
Omnilingual SONAR Team, Ioannis Tsiamas, Yen Meng, Vivek Iyer, Guillem Ramirez, Jaehyeong Jo, Alexandre Mourachko, Yu-An Chung, Artyom Kozhevnikov, Belen Alastruey, Christophe Ropers, David Dale, Holger Schwenk, João Maria Janeiro, Kevin Heffernan, Loic Barrault, Marta R. Costa-jussa, Paul-Ambroise Duquenne, Pere Lluís Huguet Cabot
March 17, 2026
February 27, 2026
Human & Machine Intelligence
Unified Vision–Language Modeling via Concept Space Alignment
Yifu Qiu, Holger Schwenk, Paul-Ambroise Duquenne
February 27, 2026
October 31, 2019
NLP
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
October 31, 2019
March 14, 2019
NLP
On the Pitfalls of Measuring Emergent Communication | Facebook AI Research
Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin
March 14, 2019
January 13, 2020
NLP
Scaling up online speech recognition using ConvNets | Facebook AI Research
Vineel Pratap, Qiantong Xu, Jacob Kahn, Gilad Avidov, Tatiana Likhomanenko, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert
January 13, 2020
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 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 © 2026