TiBERT: Tibetan Pre-trained Language Model
This work addresses the problem of limited NLP resources for Tibetan speakers and researchers, though it is incremental as it applies an existing method to a new language.
The authors tackled the lack of a monolingual pre-trained language model for Tibetan, a low-resource language, by collecting large-scale data and training TiBERT, which achieved state-of-the-art performance in text classification and question generation tasks.
The pre-trained language model is trained on large-scale unlabeled text and can achieve state-of-the-art results in many different downstream tasks. However, the current pre-trained language model is mainly concentrated in the Chinese and English fields. For low resource language such as Tibetan, there is lack of a monolingual pre-trained model. To promote the development of Tibetan natural language processing tasks, this paper collects the large-scale training data from Tibetan websites and constructs a vocabulary that can cover 99.95$\%$ of the words in the corpus by using Sentencepiece. Then, we train the Tibetan monolingual pre-trained language model named TiBERT on the data and vocabulary. Finally, we apply TiBERT to the downstream tasks of text classification and question generation, and compare it with classic models and multilingual pre-trained models, the experimental results show that TiBERT can achieve the best performance. Our model is published in http://tibert.cmli-nlp.com/