As I promised in the last article, I perform experiments about classification of news title in Japanese. The result is very good as I expected. Let me explain the details.
I use “livedoor news corpus” (2) for this experiment. These are five-class of news title in this experiment. These are about life, movie, sports, chats, and electronics. Here is the detail of the class. I would like to classify each title of news according to this class correctly.
Then I train BERT(1) model with a sample of news title written in Japanese. Here is the result. The BERT model, which I used, is the multi-language model. All I have to do is fine-tuning to apply my task. As you can see below, The accuracy ratio is about 88%. It is very good while I use very small sample data (3503 for training, 876 for test). It took less than one minute on colab with GPU.
With 3 epochs, I confirmed that the accuracy ratio is over 88%
Let me take 10 samples for validation and see each of them. These samples are not used for training so they are new to the computer. Nine out of ten are classified correctly. It is so good, isn’t it?
The beauty is that the pre-trained model is not specific for only Japanese. As it is a multi-language model, it should work in many kinds of languages with the same fine-tuning as I did in Japanese. Therefore It should work in your languages, too!
How about this experiment? I continue to do experiments of BERT in many tasks of natural language and update my article soon. Stay tuned!
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
11 Oct 2018, Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova, Google AI Language
- livedoor news corpus CC BY-ND 2.1 JP
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