Task: Question Answering
Input (English): To further improve the performance of our models, we introduce in-house labeled data that we use to fine-tune BERT. For the gender classification task, we manually label an in-house dataset of 1,100 users with gender tags, including 550 female users, 550 male users. We obtain 162,829 tweets by crawling the 1,100 users' timelines. For the dialect identification task, we randomly sample 20,000 tweets for each class from an in-house dataset gold labeled with the same 15 classes as the shared task. 
 Question: What are the in-house data employed?
Output (English): we manually label an in-house dataset of 1,100 users with gender tags we randomly sample 20,000 tweets for each class from an in-house dataset gold labeled with the same 15 classes as the shared task