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Word segmentation and POS tagging are crucial steps for natural language processing. Though deep learning facilitates learning a joint model without feature engineering, it still suffers from unreliable word embedding when words are rare or unknown. We introduce two-level backoff models to which morphological information and character-level contexts are integrated. Experimental results on Thai and Chinese show that our backoff models improve the accuracy of both tasks and excels in OOV recovery.