A Study of English-Indonesian Neural Machine Translation with Attention (Seq2Seq, ConvSeq2Seq, RNN, and MHA)
Diyah Puspitaningrum · 2021
In recent years, Neural Machine Translation (NMT) with attention mechanisms has emerged in research and industry. This study discusses the essentials of NMT (Seq2Seq, Convolutional Seq2Seq (ConvSeq2Seq), Recurrent Neural Networks (RNN), and Multi-Head Attention (MHA)) while implemented in formal passages in English-Indonesian and Indonesian-English. The experimental results for ConvSeq2Seq achieve up to 38.99 BLEU sentence scores, 43.23 BLEU corpus scores, and 39.48 GLEU corpus scores over the Seq2Seq English-Indonesian. For Indonesian-English, the results for ConvSeq2Seq achieved as follows: up to 42.59 BLEU sentence scores, 42.91 BLEU corpus scores, 41.05 GLEU corpus scores, and 1356.65 WER scores over RNN and MHA. Thus, while ConvSeq2Seq tends to be the supremacy, this literature also describes the combination of architectures and specific fine-tuning strategies as a discussion.