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.

Q: As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 .

A: what sentiment sources do they compare with?
****
Q: 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.

A: which multilingual approaches do they compare with?
****
Q: $D_a$ contained all of the tweets collected on the attack day of the five attacks mentioned in section 4.2. And $D_b$ contained all of the tweets collected before the five attacks. There are 1180 tweets in $D_a$ and 7979 tweets in $D_b$. The tweets on the attack days ($D_a$) are manually annotated and only 50 percent of those tweets are actually about a DDoS attack.

A:
Do twitter users tend to tweet about the DOS attack when it occurs? How much data supports this assumption?
****