Generating Text with Deep Reinforcement Learning

TL;DR

This paper presents a novel sequence-to-sequence decoding method using deep reinforcement learning with a Deep Q-Network, which iteratively improves generated sequences by focusing on difficult parts, outperforming traditional decoders on unseen data.

Contribution

Introduces a reinforcement learning-based decoding schema for sequence generation that iteratively refines output sequences, emphasizing difficult parts, and demonstrates improved performance on unseen sentences.

Findings

Outperforms baseline on unseen sentences in BLEU score

Effectively focuses on difficult sequence parts during decoding

Achieves competitive results on training data

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.