Deep Reinforcement Learning For Sequence to Sequence Models (1805.09461v4)

Abstract: 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.

Deep Reinforcement Learning for Sequence-to-Sequence Models

In this paper, the authors investigate leveraging deep reinforcement learning (RL) to enhance the training of sequence-to-sequence (seq2seq) models. Seq2seq models, characterized by their encoder-decoder architecture, have achieved remarkable success across several applications, including machine translation, text summarization, and speech recognition. These models, however, face the notable challenges of exposure bias and evaluation metric inconsistency, which are targetted by using RL techniques.

Seq2seq Model Challenges

Seq2seq models face two significant issues during training. First, there is the exposure bias, resulting from the discrepancy between training and testing phases, where the former utilizes the ground-truth output (teacher-forcing) while the latter relies on the model's predictions. This misalignment can lead to error accumulation during inference. Second, seq2seq models are traditionally optimized using cross-entropy loss, which does not align with discrete evaluation measures like ROUGE or BLEU, leading to a mismatch between the training objective and evaluation metrics.

Reinforcement Learning Perspective

Reinforcement learning introduces a framework to address these challenges by optimizing policies through reward maximization over a sequence of decisions. It enables the incorporation of evaluation measures directly into the training process, thus aligning with the actual evaluation metrics. The authors explore RL methods, with a focus on policy gradient (PG) and actor-critic (AC) techniques, to improve the seq2seq training pipeline.

1. Policy Gradient Methods: PG methods, particularly the REINFORCE algorithm, are utilized to refine the policy directly by adjusting it to maximize expected rewards based on sequences generated during training. The paper discusses enhancements such as the Self-Critic (SC) approach, which utilizes a baseline computed from a greedy sequence to reduce variance during training.
2. Actor-Critic Models: AC models provide continuous feedback to seq2seq models by leveraging a separate value model to estimate future rewards. The combination of actors generating sequences and critics updating value estimates facilitates robust model training and reduced variance of policy updates.
3. Advanced Q-Learning Techniques: The integration of $Q$-Learning methods such as DQNs introduces off-policy value estimation frameworks. Techniques like Double Q-Learning and Dueling Networks are discussed to mitigate overestimation biases and improve action evaluation. However, the action space in seq2seq tasks presents unique challenges due to its larger size compared to traditional RL tasks.

Implications and Contributions

The paper presents a comprehensive assessment of how RL can enhance seq2seq models by structurally aligning training procedures with performance evaluation metrics, akin to a decision-making problem. The provided methods could significantly advance processes that require handling temporal dependencies and decision sequences, offering a way to use past and learned experiences to optimize stabilizing long-horizon prediction models dynamically.

The authors supplement their contributions with an open-source library targeted at abstractive summarization, featuring different RL components such as advantage-based algorithms and scheduled sampling, offering a flexible ground for experimentation and further research in seq2seq model optimization through RL.

Future Directions

Given the demonstrated improvements, future research may explore a broader application of RL-enhanced seq2seq training outside text-based applications to embrace diverse domains like image and video processing. Additionally, integrating inverse reinforcement learning for constructing more nuanced reward structures could refine output quality substantially, promising novel methodologies for advanced language generation tasks.

In conclusion, this paper not only elucidates the strengths of RL methods in tackling longstanding seq2seq issues but also sets the stage for further exploration into robust model architectures that utilize reinforcement-driven learning paradigms effectively.