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: We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences.
Question: What baselines did they consider?

A: LSTM SCIBERT
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Q: One example illustrates that the mistakes the model makes can be associated with changes in the party policy.
Question: Do changes in policies of the political actors account for all of the mistakes the model made?

A: Yes
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Q: We use the method of BIBREF5 to train neural sequence-to-sequence Spanish-English ST models. Obtaining gold topic labels for our data would require substantial manual annotation, so we instead use the human translations from the 1K (train20h) training set utterances to train the NMF topic model with scikit-learn BIBREF14
Question: What is the architecture of the model?

A:
BIBREF5 to train neural sequence-to-sequence NMF topic model with scikit-learn BIBREF14
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