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.

Input: Consider Input: Before designing this NLP toolkit, we conducted a survey among engineers and identified a spectrum of three typical personas.

Output: How do the authors evidence the claim that many engineers find it a big overhead to choose from multiple frameworks, models and optimization techniques?

Input: Consider Input: 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 .

Output: What is the multilingual baseline?

Input: Consider Input: For training, three types of Recurrent Neural Network (RNN) layers are used an encoder layer, a decoder layer and an output layer. These layers together form a LSTM model. LSTM is typically used in seq2seq translation.
Output: What is the architecture of the system?