In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[EX Q]: 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.
Question: How did they induce the CFG?
[EX A]: 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

[EX Q]: Bi-LSTM BIBREF11 is a baseline for neural models. Bi-LSTM$_{+ att. + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task. GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge. CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge.
Question: How does the neural network architecture accomodate an unknown amount of senses per word?
[EX A]: converts WSD to a sequence learning task leverage gloss knowledge by extending gloss knowledge

[EX Q]: The selection model selects the best answer from the set $\lbrace a_i
brace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants. We train another neural network to pick the best answer from the candidates. We frame the task as binary classification, distinguishing between above and below average performance. In training, we compute the F1 score of the answer for every instance. If the rewrite produces an answer with an F1 score greater than the average score of the other rewrites the instance is assigned a positive label.
Question: how are multiple answers from multiple reformulated questions aggregated?
[EX A]:
The selection model selects the best answer from the set $\lbrace a_i
brace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants.