TASK DEFINITION: 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.
PROBLEM: The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. 
 Question: What was their performance on the dataset?

SOLUTION: accuracy of 82.6%

PROBLEM: Compared to the in-context-parsing-based system, the combination with exact string matching yields a gain in recall of over 6%, and the combination with inflectional string matching yields an even bigger gain of almost 8%, at precision losses of 0.6% and 0.8%, respectively. 
 Question: What compleentary PIE extraction methods are used to increase reliability further?

SOLUTION: exact string matching inflectional string matching

PROBLEM: The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated. 
 Question: What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?

SOLUTION:
word embeddings to generate a new feature, i.e., summarizing a local context