In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: 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.
[A]: What are the in-house data employed?

[Q]: We perform our experiments using ActivityNet Captions dataset BIBREF2 that is considered as the standard benchmark for dense video captioning task. The dataset contains approximately 20k videos from YouTube and split into 50/25/25 % parts for training, validation, and testing, respectively.
[A]: What domain does the dataset fall into?

[Q]: The train and test data have divided into 70-30 ratio and we got these results as shown in Table TABREF17 for the individual dataset and the combination of both. The pre-trained network was already trained and we used the target data Queensland flood which provided 96% accuracy with 0.118 Test loss in only 11 seconds provided we used only 70% of training labeled data. The second target data is Alberta flood with the same configuration of train-test split which provided 95% accuracy with 0.118 Test loss in just 19 seconds.
[A]:
What were the model's results on flood detection?