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.

Example Input: As shown in Table TABREF46, consensus dropout fusion improves the score of NDCG by around 1.0 from the score of the joint model while still yielding comparable scores for other metrics. As also shown in Table TABREF46, the ensemble model seems to take the best results from each model.
Example Output: Which method for integration peforms better ensemble or consensus dropout fusion with shared parameters?

Example Input: We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16.  The results show that our approaches consistently outperform other approaches across languages and datasets, especially surpass pivoting, which is a strong baseline in the zero-shot scenario that multilingual NMT systems often fail to beat BIBREF19, BIBREF20, BIBREF23.
Example Output: which multilingual approaches do they compare with?

Example Input: Numerically, Our model improves the DAR accuracy over Bi-LSTM-CRF by 2.1% and 0.8% on SwDA and MRDA respectively.
Example Output:
By how much do they outperform state-of-the-art solutions on SWDA and MRDA?