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: The grammar induction method works on the premise of curriculum learning BIBREF7 , where the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns. A context-free grammar (CFG) is induced from the text, which is represented by several layers of semantic annotations.

Output: How did they induce the CFG?


Input: Consider Input: Compared with the BERT-BASE model, BERT-SPC significantly improves the accuracy and F1 score of aspect polarity classification.

Output: How much better is performance of the proposed model compared to the state of the art in these various experiments?


Input: Consider Input: The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification.


Output: How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner?