The complete semantic representation of a Tibetan sentence is mainly determined by the addition of a specific functional word. The choice of Tibetan functional words is mainly influenced (both explicitly and implicitly) by the sequence of Tibetan suffixes. In this article, we propose an RNN-based Tibetan radical suffix unit (TRSU) to consider this relationship. Specifically, for the Tibetan radical suffix unit-explicit (TRSU-E) method, the fixed suffix in Tibetan is used to determine the virtual functional words. For the Tibetan radical suffix unit-implicit (TRSU-I) method, the decision is assisted by adding a specific suffix. To test the method, we design a standard Tibetan corpus, which consists of different genres. Our experimental results show that the complexity of our method is reduced by up to 22.2% relative to the best baseline. Furthermore, with the hidden semantic information and implicit suffix, TRSU-I outperforms TRSU-E by reducing the perplexity (PPL) by 3%. Moreover, good results are achieved on the English Penn Treebank data set.

中文翻译：

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考虑后缀与功能词之间关系的藏语语言模型

藏文句子的完整语义表示主要取决于添加特定的功能词。藏语功能词的选择主要受藏语后缀序列的影响（显性和隐性）。在本文中，我们提出了一个基于RNN的藏族根部后缀单位（TRSU）来考虑这种关系。具体来说，对于藏语根部后缀单位-显式（TRSU-E）方法，使用藏语中的固定后缀来确定虚拟功能词。对于藏语根部后缀单位隐式（TRSU-I）方法，通过添加特定后缀来辅助决策。为了测试该方法，我们设计了一个标准的藏语语料库，该语料库由不同体裁组成。我们的实验结果表明，相对于最佳基准，我们的方法的复杂度降低了22.2％。此外，凭借隐藏的语义信息和隐含的后缀，TRSU-I通过将困惑度（PPL）降低了3％，胜过TRSU-E。此外，在英语Penn树库数据集上也取得了良好的结果。