| dc.description.abstract | In Natural Language Processing ( NLP ), as far as we know, lots of the currently known models support translation or dialogue generation in Chinese. But we know that Chinese is divided into simplified Chinese and traditional Chinese. However, the Chinese supported by these models are mostly simplified Chinese. Although they are all Chinese, their characters and usage are not the same.
Due to a lack of translation and dialogue data in Traditional Chinese, we use a combination of translation and dialogue with English as the pivot in this paper. In other words, we have made a two-way translation between traditional Chinese and English, as well as a pivotal dialogue in English. To accomplish the translation in the training part, we use data collected from the following news sources and online classes: The China Post, Voice Tube, and Hope English. Moreover, we use dailydialog to train the English dialogue. Then, for the final test, we adopt a traditional Chinese dialogue from Hi Tutor and TOCFL. We utilize mBART50 and DialoGPT to generate the traditional Chinese dialogue with fine-tuning.
The results of our fine-tuning models are better than the original models without fine-tuning. Especially when the beam size is 7 in the translation. After fine-tuning the dialogue model, the result shows that the dialogue generated from the small size model is the smoothest. In the final experiment, we use the parameters beam size, top k, and top p to produce the best results in our model, respectively: 7, 10, and 0.95. The bleu score of the final test in our best model is 2.85. Finally, using the best model, we build a traditional Chinese dialogue utilizing English conversations as the pivot. | en_US |