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.

[EX Q]: Our attempt at language pre-training fell short of our expectations in all but one tested dataset. Our pre-training was unsuccessful in improving accuracy, even when applied to networks larger than those reported. 
 Question: Does pre-training on general text corpus improve performance?
[EX A]: No

[EX Q]: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . 
 Question: How do they represent input features of their model to train embeddings?
[EX A]: a vector of frame-level acoustic features

[EX Q]: A causal attribution dataset is a collection of text pairs that reflect cause-effect relationships proposed by humans (for example, “virus causes sickness”). These written statements identify the nodes of the network (see also our graph fusion algorithm for dealing with semantically equivalent statements) while cause-effect relationships form the directed edges (“virus” $\rightarrow $ “sickness”) of the causal attribution network. 
 Question: What are causal attribution networks?
[EX A]:
networks where nodes represent causes and effects, and directed edges represent cause-effect relationships proposed by humans