Improvement of a dedicated model for open domain persona-aware dialogue generation

TL;DR

This paper explores enhancements to a Transformer-based dedicated model for open domain persona-aware dialogue generation, focusing on speed and performance improvements tailored for short multi-turn dialogues, with open-source code provided.

Contribution

It introduces specific modifications to Transformer architecture optimized for short dialogue sequences in persona-aware dialogue generation.

Findings

Improved model training efficiency and speed.

Enhanced dialogue generation quality.

Open-source implementation available.

Abstract

This paper analyzes some speed and performance improvement methods of Transformer architecture in recent years, mainly its application in dedicated model training. The dedicated model studied here refers to the open domain persona-aware dialogue generation model, and the dataset is multi turn short dialogue, The total length of a single input sequence is no more than 105 tokens. Therefore, many improvements in the architecture and attention mechanism of transformer architecture for long sequence processing are not discussed in this paper. The source code of the experiments has been open sourced: https://github.com/ghosthamlet/persona