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.

Example Input: 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?
Example Output: fastText CWE-LP

Example Input:  Recurrent Neural Network (RNN) Language Models
are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. ActionLSTM
models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. Generative Recurrent Neural Network Grammars (RNNG)
jointly model the word sequence as well as the underlying syntactic structure BIBREF18. 
 Question: What are the baseline models?
Example Output: Recurrent Neural Network (RNN) ActionLSTM Generative Recurrent Neural Network Grammars (RNNG)

Example Input: To deal with this problem, BIBREF1 introduced a semantically conditioned generation model using Hierarchical Disentangled Self-Attention (HDSA) . 
 Question: what semantically conditioned models did they compare with?
Example Output:
Hierarchical Disentangled Self-Attention