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]: 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]: a vector of frame-level acoustic features


[Q]: In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3 . 
 Question: What was their performance on emotion detection?
[A]: Answer with content missing: (Table 3) Best author's model B-M average micro f-score is 0.409, 0.459, 0.411 on Affective, Fairy Tales and ISEAR datasets respectively. 


[Q]: hierarchical In this paper, we work with the dataset of the 2019 GermEval shared task on hierarchical text classification BIBREF0 and use the predefined set of labels to evaluate our approach to this classification task. 
 Question: What dataset do they use?
[A]:
2019 GermEval shared task on hierarchical text classification