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]: The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?
[EX A]: simply averaging the predictions from the constituent single models

[EX Q]: Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models. The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4  Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting. By helping our model learn attention alignment faster, we can afford to use a smaller overall model to achieve similar quality speech quality. 
 Question: Do they reduce the number of parameters in their architecture compared to other direct text-to-speech models?
[EX A]: Yes

[EX Q]: Figure FIGREF5 shows that E-BERT performs comparable to BERT and ERNIE on unfiltered LAMA. 
 Question: Which of the two ensembles yields the best performance?
[EX A]:
Answer with content missing: (Table 2) CONCAT ensemble