Optimizing Attention for Sequence Modeling via Reinforcement Learning

doi:10.1109/TNNLS.2021.3053633

Optimizing Attention for Sequence Modeling via Reinforcement Learning

2022-08-01

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DOI：10.1109/TNNLS.2021.3053633

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Attention has been shown highly effective for modeling sequences, capturing the more informative parts in learning a deep representation. However, recent studies show that the attention values do not always coincide with intuition in tasks, such as machine translation and sentiment classification. In this study, we consider using deep reinforcement learning to automatically optimize attention distribution during the minimization of end task training losses. With more sufficient environment states, iterative actions are taken to adjust attention weights so that more informative words receive more attention automatically. Results on different tasks and different attention networks demonstrate that our model is of great effectiveness in improving the end task performances, yielding more reasonable attention distribution. The more in-depth analysis further reveals that our retrofitting method can help to bring explainability for baseline attention.

Keywords:

Task analysis

Machine translation

Training

Reinforcement learning

Optimization

Sentiment analysis

Minimization

Attention mechanism

deep reinforcement learning (RL)

natural language processing (NLP)

neural networks

Optimizing Attention for Sequence Modeling via Reinforcement Learning

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