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.

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?
a vector of frame-level acoustic features

We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message).
Question: What baselines did they consider?
state-of-the-art PDTB taggers

The vector gate is inspired by BIBREF11 and BIBREF12 , but is different to the former in that the gating mechanism acts upon each dimension of the word and character-level vectors, and different to the latter in that it does not rely on external sources of information for calculating the gating mechanism.
Question: Where do they employ feature-wise sigmoid gating?
gating mechanism acts upon each dimension of the word and character-level vectors