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.

To increase the variety of corpus, we selected 4 English corpora and 4 Mandarin corpora in addition to the low resource language corpora.
Question: Do they test their approach on large-resource tasks?
Yes

For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English .
Question: What monolingual word representations are used?
AraVec for Arabic, FastText for French, and Word2vec Google News for English.

The parameters of the entire MSD (auxiliary-task) decoder are shared across languages.

Since a grouping of the languages based on language family would have left several languages in single-member groups (e.g. Russian is the sole representative of the Slavic family), we experiment with random groupings of two to three languages. Multilingual training is performed by randomly alternating between languages for every new minibatch.
Question: How do they perform multilingual training?
Multilingual training is performed by randomly alternating between languages for every new minibatch