Generating Text with Deep Reinforcement Learning

Abstract

We introduce a novel schema for sequence to sequence learning with a Deep Q-Network (DQN), which decodes the output sequence iteratively. The aim here is to enable the decoder to first tackle easier portions of the sequences, and then turn to cope with difficult parts. Specifically, in each iteration, an encoder-decoder Long Short-Term Memory (LSTM) network is employed to, from the input sequence, automatically create features to represent the internal states of and formulate a list of potential actions for the DQN. Take rephrasing a natural sentence as an example. This list can contain ranked potential words. Next, the DQN learns to make decision on which action (e.g., word) will be selected from the list to modify the current decoded sequence. The newly modified output sequence is subsequently used as the input to the DQN for the next decoding iteration. In each iteration, we also bias the reinforcement learning's attention to explore sequence portions which are previously difficult to be decoded. For evaluation, the proposed strategy was trained to decode ten thousands natural sentences. Our experiments indicate that, when compared to a left-to-right greedy beam search LSTM decoder, the proposed method performed competitively well when decoding sentences from the training set, but significantly outperformed the baseline when decoding unseen sentences, in terms of BLEU score obtained.

Accepted to the NIPS2015 Deep Reinforcement Learning Workshop

References in corpus (7)

• Sequence to Sequence Learning with Neural Networks
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• DRAW: A Recurrent Neural Network For Image Generation
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Cited by in corpus (11)

• Survey on reinforcement learning for language processing
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• Efficient (Soft) Q-Learning for Text Generation with Limited Good Data
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• Automatic Conditional Generation of Personalized Social Media Short Texts
• Learning How to Self-Learn: Enhancing Self-Training Using Neural Reinforcement Learning
• Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
• ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs
• A Deep Network with Visual Text Composition Behavior
• Adversarial Machine Learning in Text Analysis and Generation