Jacob Andreas

Teachable Reinforcement Learning via Advice Distillation

2022 · arXiv (Cornell University)

Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate …

Pushdown Layers: Encoding Recursive Structure in Transformer Language Models

2023 · arXiv (Cornell University)

Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism. Consequently, Transformer language models poorly capture long-tail recursive structure and exhibit sample-inefficient …

Learning to Compose Neural Networks for Question Answering

2016

Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

A Minimal Span-Based Neural Constituency Parser

2017

In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but …