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.

Q: As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages. The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17). 
 Question: What two architectures are used?

A: fastText CWE-LP
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Q: This model uses a particular RNN cell in order to store just relevant information about the given question.  Dynamic Memory stores information of entities present in $T$ . The addition of the $s_t^T q$ term in the gating function is our main contribution.  
 Question: How does the model recognize entities and their relation to answers at inference time when answers are not accessible?

A: gating function Dynamic Memory
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Q: All baselines are Transformer models in their base configuration BIBREF11, using 6 encoder and decoder layers, with model and hidden dimensions of 512 and 2048 respectively, and 8 heads for all attention layers. For initial multi-task experiments, all model parameters were shared BIBREF12, but performance was down by multiple BLEU points compared to the baselines. 
 Question: What baselines non-adaptive baselines are used?

A:
Transformer models in their base configuration BIBREF11, using 6 encoder and decoder layers, with model and hidden dimensions of 512 and 2048 respectively, and 8 heads for all attention layers
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