Deep Reinforcement Learning for Sequence-to-Sequence Models.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Sequence-to-sequence (seq2seq) models, based on deep neural networks, excel in tasks like machine translation and text summarization.
• Current seq2seq models face challenges including exposure bias and inconsistencies between training and testing metrics.
• Existing improvements like attention and pointer-generation models have limitations.

Purpose of the Study:

• To investigate the application of reinforcement learning (RL) to address inherent problems in seq2seq models.
• To present a novel formulation combining RL's decision-making capabilities with seq2seq architectures for enhanced memory.
• To offer insights into current challenges and propose solutions using advanced RL models.

Main Methods:

• A survey of recent frameworks integrating RL and deep neural networks for seq2seq tasks.
• Formulation of seq2seq problems from a reinforcement learning perspective.
• Implementation of RL models for abstractive text summarization, including source code provision.

Main Results:

• Demonstration of RL's potential to mitigate exposure bias and improve train/test consistency in seq2seq models.
• Experimental validation of RL-based models for abstractive text summarization.
• Analysis of performance and training time for proposed RL models.

Conclusions:

• Reinforcement learning offers a promising approach to overcome limitations in current seq2seq models.
• The integration of RL with deep learning enhances model decision-making and long-term memory.
• Further research into advanced RL models can lead to more robust and efficient seq2seq systems.