Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems

TL;DR

This paper explores the use of self-attentional models for training end-to-end task-oriented dialogue systems, demonstrating improved performance and efficiency over traditional recurrence-based models.

Contribution

It is the first to apply self-attentional models to task-oriented dialogue generation, showing their advantages in accuracy and efficiency.

Findings

Self-attentional models outperform recurrence-based models in dialogue tasks.

Self-attentional models achieve higher evaluation scores.

Models are trained efficiently on multiple datasets.

Abstract

Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrence-based and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism. Self-attentional models have been used in the creation of the state-of-the-art models in many NLP tasks such as neural machine translation, but their usage has not been explored for the task of training end-to-end task-oriented dialogue generation systems yet. In this study, we apply these models on the three different datasets for training task-oriented chatbots. Our finding shows that self-attentional models can be exploited to create end-to-end task-oriented chatbots which not only achieve higher evaluation scores compared to recurrence-based models, but also do so more efficiently.