## 4 Experimental Results
### General Setting  
#### 4.1.1 Datasets  
Three classic datasets (THUCNews, AG News, and TREC-QA) are introduced to test the effects of APTrans. Both THUCNews and AG News datasets belong to news datasets. THUCNews is a Chinese news dataset and AG News is an English news dataset. The distribution of data in the two datasets is uniform and there is no imbalance problem. The reason for choosing these two datasets is that there is a long textual relationship in the news text, and there are some non-written expressions such as colloquial expressions. Experiments on these two datasets can verify the learning ability of APTrans for long-text information and determine whether our method can capture key lexical location information in the text, so as to achieve a good classification effect. The TREC-QA dataset belongs to a short-text dataset in Q&A research. The problem of uneven distribution of category data exists in this dataset, but the typical feature of samples in this dataset is that there are always core words in each sample to summarize the semantic information of the sample. Therefore, we used this dataset for training and testing to judge whether APTrans has the capacity for short-text classification and whether it can achieve high-performance classification by grasping core words in short texts under the condition of uneven data. More details about these three datasets are shown below:  
**THUCNews**[43]: A tremendous Chinese text classification dataset contains approximately 840,000 news files and is divided into 14 categories. This dataset was created by screening the historical data of Sina News RSS subscriptions from 2005 to 2011. Weused stratified random sampling to extract the dataset utilized in our work. The training dataset comprises 40,000 texts, the validation dataset consists of 8000 texts, and the test dataset exhibits 2000 texts, covering a total of 10 categories, including sports, entertainment, property, and education. Chinese sentences of varying lengths can be found in the corpus, and the semantic complexity of the Chinese corpus is high, bringing a certain degree of difficulty to the processing of text classification.  
**AG News**[44]: AG News (AG's News Corpus) is a news dataset that contains four categories for topic classification. Each category contains a total of 30,000 training samples and 1900 test samples. This dataset was originally derived from a public corpus database of AG's articles. Moreover, this dataset contains titles and content of news, resulting in several irregular colloquial expressions, making it difficult for the model to achieve accurate semantic understanding and classification.  
**TREC-QA**[45]: This dataset is a question dataset that contains approximately 6000 common English questions and is widely used in Q&A research. This dataset is mainly divided into two versions: **TREC-6** and **TREC-50**, both of which have a training dataset with 5452 questions and a test dataset with 500 questions. We used **TREC-6** in this study, which contains six categories of coarse labels, including people, locations, digital information, and other issues. Most of these texts are short texts that may pose certain challenges to the semantic analysis capabilities of the model.