58 research outputs found
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
We propose to directly map raw visual observations and text input to actions
for instruction execution. While existing approaches assume access to
structured environment representations or use a pipeline of separately trained
models, we learn a single model to jointly reason about linguistic and visual
input. We use reinforcement learning in a contextual bandit setting to train a
neural network agent. To guide the agent's exploration, we use reward shaping
with different forms of supervision. Our approach does not require intermediate
representations, planning procedures, or training different models. We evaluate
in a simulated environment, and show significant improvements over supervised
learning and common reinforcement learning variants.Comment: In Proceedings of the Conference on Empirical Methods in Natural
Language Processing (EMNLP), 201
Simple Recurrent Units for Highly Parallelizable Recurrence
Common recurrent neural architectures scale poorly due to the intrinsic
difficulty in parallelizing their state computations. In this work, we propose
the Simple Recurrent Unit (SRU), a light recurrent unit that balances model
capacity and scalability. SRU is designed to provide expressive recurrence,
enable highly parallelized implementation, and comes with careful
initialization to facilitate training of deep models. We demonstrate the
effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over
cuDNN-optimized LSTM on classification and question answering datasets, and
delivers stronger results than LSTM and convolutional models. We also obtain an
average of 0.7 BLEU improvement over the Transformer model on translation by
incorporating SRU into the architecture.Comment: EMNL
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