@misc{corona2020modular,
author = {Corona, Rodolfo and Fried, Daniel and Devin, Coline and Klein, Dan and Darrell, Trevor},
title = {Modular Networks for Compositional Instruction Following},
howpublished = {arXiv},
year = {2020},
month = {October},
abstract = {Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard, non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.},
url = {http://approjects.co.za/?big=en-us/research/publication/modular-networks-for-compositional-instruction-following/},
}