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:  Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare. 
 Question: What NLP tasks do they consider?
Example Output: text classification for themes including sentiment, web-page, science, medical and healthcare

Example Input: Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation. 
 Question: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
Example Output:  continuous relaxation to top-k-argmax

Example Input: GANN is novel neural network model for APC task aimed to solve the shortcomings of traditional RNNs and CNNs. The GANN applied the Gate Truncation RNN (GTR) to learn informative aspect-dependent sentiment clue representations. GANN obtained the state-of-the-art APC performance on the Chinese review datasets. 
 Question: What was previous state-of-the-art on four Chinese reviews datasets?
Example Output:
GANN obtained the state-of-the-art APC performance on the Chinese review datasets