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.

Span detector. We adopt a multi-turn answer module for the span detector BIBREF1 . Formally, at time step INLINEFORM0 in the range of INLINEFORM1 , the state is defined by INLINEFORM2 . The initial state INLINEFORM3 is the summary of the INLINEFORM4 : INLINEFORM5 , where INLINEFORM6 . Here, INLINEFORM7 is computed from the previous state INLINEFORM8 and memory INLINEFORM9 : INLINEFORM10 and INLINEFORM11 . Finally, a bilinear function is used to find the begin and end point of answer spans at each reasoning step INLINEFORM12 : DISPLAYFORM0 DISPLAYFORM1

The final prediction is the average of each time step: INLINEFORM0 . We randomly apply dropout on the step level in each time step during training, as done in BIBREF1 .
What is the architecture of the span detector?

Attention-base translation model We use the system of BIBREF6 , a convolutional sequence to sequence model.
Which translation system do they use to translate to English?

We have training and testing sets in three different languages: English, Chinese and Korean. When fine-tuning, we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged.
what does the model learn in zero-shot setting?