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.

We discuss two core models for addressing sequence labeling problems and describe, for each, training them in a single-model multilingual setting: (1) the Meta-LSTM BIBREF0 , an extremely strong baseline for our tasks, and (2) a multilingual BERT-based model BIBREF1 .
What is the multilingual baseline?

Our goal was to generate questions without templates and with minimal human involvement using machine learning transformers that have been demonstrated to train faster and better than RNNs. Such a system would benefit educators by saving time to generate quizzes and tests.
What is the motivation behind the work? Why question generation is an important task?

However, recent advances in Deep Learning techniques have shown that the NER task can benefit from the use of neural architectures, such as biLSTM-networks BIBREF3 , BIBREF4 . We use the implementation proposed in BIBREF24 for conducting three different experiments.
Which machine learning algorithms did the explore?