Although some linguists (Rusmali et al., 1985; Crouch, 2009) have fairly attempted to define the morphology and syntax of Minangkabau, information processing in this language is still absent due to the scarcity of the annotated resource. In this work, we release two Minangkabau corpora: sentiment analysis and machine translation that are harvested and constructed from Twitter and Wikipedia. We conduct the first computational linguistics in Minangkabau language employing classic machine learning and sequence-to-sequence models such as LSTM and Transformer. Our first experiments show that the classification performance over Minangkabau text significantly drops when tested with the model trained in Indonesian. Whereas, in the machine translation experiment, a simple word-to-word translation using a bilingual dictionary outperforms LSTM and Transformer model in terms of BLEU score.

本文发布了两个Minangkabau语料库，展开了情感分析和机器翻译实验，并使用经典机器学习和序列到序列模型，如LSTM和Transformer，进行了Minangkabau语言的首次计算语言学研究。实验表明，在与印尼语模型测试时，Minangkabau文本的分类性能显著下降；而在机器翻译实验中，使用双语词典的简单词对译比LSTM和Transformer模型在BLEU得分方面表现更好。

南卡巴瓦语的计算语言学研究：情感分析和机器翻译