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In the speech synthesis systems, the phrase break (PB) prediction is the first and most important step. Recently, the state-of-the-art PB prediction systems mainly rely on word embeddings. However this method is not fully applicable to Mongolian language, because its word embeddings are inadequate trained, owing to the lack of resources. In this paper, we introduce a bidirectional Long Short Term Memory (BiLSTM) model which combined word embeddings with syllable and morphological embedding representations to provide richer and multi-view information which leverages the agglutinative property. Experimental results show the proposed method outperforms compared systems which only used the word embeddings. In addition, further analysis shows that it is quite robust to the Out-of-Vocabulary (OOV) problem owe to the refined word embedding. The proposed method achieves the state-of-the-art performance in the Mongolian PB prediction.