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Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation

2020-09-19 · PACLIC 2020 10 · Fajri Koto, Ikhwan Koto

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.

📄 PDF Abstract BibTeX arXiv:2009.09309

Code (1)

fajri91/minangNLP 공식 구현 tf

Tasks

Machine TranslationSentiment AnalysisTranslationWord Translation

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…
Position-Wise Feed-Forward Layer 설명 없음
Sigmoid Activation 설명 없음
Tanh Activation 설명 없음
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…
Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

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