Research

NLP

Compositional generalization through meta sequence-to-sequence learning

December 12, 2019

Abstract

People can learn a new concept and use it compositionally, understanding how to “blicket twice” after learning how to “blicket.” In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality, especially when composing new concepts together with existing concepts. In this paper, I show how memory-augmented neural networks can be trained to generalize compositionally through meta seq2seq learning. In this approach, models train on a series of seq2seq problems to acquire the compositional skills needed to solve new seq2seq problems. Meta seq2seq learning solves several of the SCAN tests for compositional learning and can learn to apply implicit rules to variables.

Download the Paper

Related Publications

November 16, 2022

NLP

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Kushal Tirumala, Aram H. Markosyan, Armen Aghajanyan, Luke Zettlemoyer

November 16, 2022

October 31, 2022

NLP

Autoregressive Search Engines: Generating Substrings as Document Identifiers

Fabio Petroni, Giuseppe Ottaviano, Michele Bevilacqua, Patrick Lewis, Scott Yih, Sebastian Riedel

October 31, 2022

December 06, 2020

NLP

Pre-training via Paraphrasing

Michael Lewis, Armen Aghajanyan, Gargi Ghosh, Luke Zettlemoyer, Marjan Ghazvininejad, Sida Wang

December 06, 2020

November 30, 2020

NLP

Where Are You? Localization from Embodied Dialog

Dhruv Batra, Devi Parikh, Meera Hahn, Jacob Krantz, James Rehg, Peter Anderson, Stefan Lee

November 30, 2020

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

December 02, 2018

NLP

Computer Vision

One-Shot Unsupervised Cross Domain Translation | Facebook AI Research

Sagie Benaim, Lior Wolf

December 02, 2018

June 30, 2019

NLP

Variational Training for Large-Scale Noisy-OR Bayesian Networks | Facebook AI Research

Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth

June 30, 2019

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.