# Neural Network-Based Bilingual Lexicon Induction for Indonesian Ethnic Languages
## 2 Bilingual Lexicon Induction  
Creating a bilingual dictionary is the first crucial step toward enriching low-resource languages. Particularly for closely related languages, it has been shown that the constraint-based approach helps induce bilingual lexicons from two bilingual dictionaries via the pivot language [7; 8]. However, implementing the constraint-based approach on a large scale to create multiple bilingual dictionaries is still challenging, particularly in determining the constraint-based approach's execution order to reduce the total costs. Plan optimization using the Markov decision process is essential when composing the order of creation for bilingual dictionaries, considering the methods and their costs [6; 9].  
[PERSON] et al. [10] proposed a method for transforming bilingual lexical induction into a binary classification task in the biomedical domain for English to Dutch. They created a classifier that predicts whether a pair of words translates using character and word level, employing the LSTM method. Their study showed that character-level representations successfully induce bilingual lexicons in the biomedical domain.  
[PERSON] et al. [11] presented a character-level sequence-to-sequence learning method, RNNembed, for English-to-Chinese translation. Specifically, the recurrent neural network (RNN) is embedded into an encoder-decoder framework and generates character-level sequence representation as input. The dimension of the input feature space can be significantly reduced and eliminates the need to handle unknown or rare words in sequences. Experimental results demonstrate that the proposed approach achieves a translation performance that is comparable--or close--to conventional word-based and phrase-based systems.  
[PERSON] et al. [12] proposed a cross-lingual feature extraction (CFE) method to learn the cross-lingual features from monolingual corpora for low-resource UBLI, enabling representations of words with the same meaning to be leveraged by the initialization step. By integrating cross-lingual representations with pre-trained word embeddings in a fully unsupervised initialization on UBLI, the proposed method outperforms existing state-of-the-art methods on low-resource language pairs.  
Addressing the poor alignment of Chinese-Uyghur cross-language word embeddings due to significant morphological differences, [PERSON] et al. [13] proposed a multilingual morphological analyzer based on a morpheme sequence combined with neural network cross-language word embedding vector mapping, and used for Chinese-Uyghur bilingual dictionary extraction. They used robust morpheme segmentation and stemming of bilingual text data to obtain excellent and meaningful word semantic features. Using a small number of Chinese-Uyghur parallel seed dictionaries as weakly supervised signals, respectively, they mapped multilingual word or morpheme vectors to a unified vector space. Experimental results show that the morpheme sequence-based method for the Chinese-Uyghur dictionary induction task significantly improved the accuracy of dictionary alignment compared to the word-based model. [PERSON] et al. [14] actively explored the resource construction and granularity optimization of minority low-resource languages and learned cross-language word embeddings without the supervision of parallel data. A Chinese-Uyghur bilingual dictionary extraction method is proposed based on the neural network cross-language word embedding vector technology and the multilingual morphological analyzer. Experiments showed that the morpheme sequence-based approach significantly improved compared to the baseline model of the word sequence.