Efficient transformer with reinforced position embedding for language models

TL;DR

This paper introduces an efficient transformer architecture with reinforced positional embedding that reduces parameters and training time while improving translation performance across multiple datasets.

Contribution

The paper presents a novel transformer design using reinforced positional embedding, achieving roughly threefold parameter reduction and faster training with improved accuracy.

Findings

Significant reduction in training time and parameters.

Consistently lower or comparable training and validation losses.

Enhanced learning efficiency across diverse datasets.

Abstract

In this paper, we propose an efficient transformer architecture that uses reinforced positional embedding to obtain superior performance with half the number of encoder decoder layers. We demonstrate that concatenating positional encoding with trainable token embeddings, normalizing columns in the token embedding matrix, and using the normalized token embedding matrix as the value of the attention layer improve the training and validation loss and the training time in an encoder-decoder Transformer model for a Portuguese-English translation task with 10 epochs or 12 hours of training across 10 trials. Our method, with roughly a threefold parameter reduction compared to the baseline model, yields a mean training loss of 1.21, a mean validation loss of 1.51, and an average training time of 1352.27 seconds per epoch, surpassing the baseline model with the same embedding dimension that employs addition of positional encoding and token embeddings, which achieves a mean training loss of 1.96, a validation loss of 2.18, and an average training time of 4297.79 seconds per epoch. Additionally, we evaluated our proposed architecture and the baseline across 14 diverse translation datasets from TensorFlow. The results indicate that our method consistently achieves lower or comparable training and validation losses, suggesting enhanced learning efficiency.