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.
--------
Question: In case of polysemous words, only the first word sense (usually the most common) is taken into account. 
 Question: How do they handle polysemous words in their entity library?

Answer: only the first word sense (usually the most common) is taken into account


Question: A recently published dataset called SCAN BIBREF2 (Simplified version of the CommAI Navigation tasks), tests compositional generalization in a sequence-to-sequence (seq2seq) setting by systematically holding out of the training set all inputs containing a basic primitive verb ("jump"), and testing on sequences containing that verb. 
 Question: How does the SCAN dataset evaluate compositional generalization?

Answer: it systematically holds out inputs in the training set containing basic primitive verb, "jump", and tests on sequences containing that verb.


Question: As the first step, we build three baseline LID systems, one based on the i-vector model, and the other two based on LSTM-RNN, using the speech data of two languages from Babel: Assamese and Georgian (AG). The two RNN LID baselines are: a standard RNN LID system (AG-RNN-LID) that discriminates between the two languages in its output, and a multi-task system (AG-RNN-MLT) that was trained to discriminate between the two languages as well as the phones. 
 Question: Which is the baseline model?

Answer:
The three baseline models are the i-vector model, a standard RNN LID system and a multi-task RNN LID system.