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 we can see that, all variants of our CRU model could give substantial improvements over the traditional GRU model, where a maximum gain of 2.7%, 1.0%, and 1.9% can be observed in three datasets, respectively.
Question: Do experiment results show consistent significant improvement of new approach over traditional CNN and RNN models?
[A]: Yes

[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

[Q]: We evaluate our models on two typical tasks: text classification and text semantic matching.
Question: What tasks do they experiment with?
[A]:
text classification and text semantic matching