Sequence-to-sequence neural machine translation for English-Malay

Abstract

Machine translation aims to translate text from a specific language into another language using computer software. In this work, we performed neural machine translation with attention implementation on English-Malay parallel corpus. We attempt to improve the model performance by rectified linear unit (ReLU) attention alignment. Different sequence-to-sequence models were trained. These models include long-short term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU). In the experiment, both bidirectional models, Bi-LSTM and Bi-GRU yield a converge of below 30 epochs. Our study shows that the ReLU attention alignment improves the bilingual evaluation understudy (BLEU) translation score between score 0.26 and 1.12 across all the models as compare to the original Tanh models.

Effect of word embedding vector dimensionality on sentiment analysis through short and long texts

“…Research fields related to natural language processing, e.g. information retrieval [1], [2], document classification [3], named entity recognition [4], machine translation [5], sentiment analysis [6], [7], recommendation systems [8] or audience segmentation [9], [10] have in common that they are problems of perception related to our senses. Thus, they have always represented a great challenge for researchers because it is particularly difficult to describe a text using algorithms and mathematical formulas.…”