TASK DEFINITION: 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.
PROBLEM: We call this lifelong interactive learning and inference (LiLi). Lifelong learning is reflected by the facts that the newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning.

SOLUTION: In what way does LiLi imitate how humans acquire knowledge and perform inference during an interactive conversation?

PROBLEM: Following previous studies BIBREF1, we collect event-related microposts from Twitter using 11 and 8 seed events (see Section SECREF2) for CyberAttack and PoliticianDeath, respectively. Unlabeled microposts are collected by using the keyword 'hack' for CyberAttack, while for PoliticianDeath, we use a set of keywords related to 'politician' and 'death' (such as 'bureaucrat', 'dead' etc.)

SOLUTION: Do they report results only on English data?

PROBLEM: For the NMT experiment, we used the Torch implementation to train a 2-layer LSTM model with 500 hidden units in both encoder and decoder, with 12 epochs.

SOLUTION:
Which NMT models did they experiment with?