  In recent times, sequence-to-sequence (seq2seq) models have gained a lot of
popularity and provide state-of-the-art performance in a wide variety of tasks
such as machine translation, headline generation, text summarization, speech to
text conversion, and image caption generation. The underlying framework for all
these models is usually a deep neural network comprising an encoder and a
decoder. Although simple encoder-decoder models produce competitive results,
many researchers have proposed additional improvements over these
sequence-to-sequence models, e.g., using an attention-based model over the
input, pointer-generation models, and self-attention models. However, such
seq2seq models suffer from two common problems: 1) exposure bias and 2)
inconsistency between train/test measurement. Recently, a completely novel
point of view has emerged in addressing these two problems in seq2seq models,
leveraging methods from reinforcement learning (RL). In this survey, we
consider seq2seq problems from the RL point of view and provide a formulation
combining the power of RL methods in decision-making with sequence-to-sequence
models that enable remembering long-term memories. We present some of the most
recent frameworks that combine concepts from RL and deep neural networks and
explain how these two areas could benefit from each other in solving complex
seq2seq tasks. Our work aims to provide insights into some of the problems that
inherently arise with current approaches and how we can address them with
better RL models. We also provide the source code for implementing most of the
RL models discussed in this paper to support the complex task of abstractive
text summarization.
