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 Input:
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 Output:
Yes


Ex Input:
This action space is of the order of $A=\mathcal {O}(|V| \times |O|^2)$ where $V$ is the number of action verbs, and $O$ is the number of distinct objects in the world that the agent can interact with. As this is too large a space for a RL agent to effectively explore, the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7 
 Question: How is the domain knowledge transfer represented as knowledge graph?

Ex Output:
the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7


Ex Input:
CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data. 
 Question: How big is new question answering dataset?

Ex Output:

239,357 English question-answer pairs