In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: Interesting prior work on quantifying social norm violation has taken a heavily data-driven focus BIBREF8 , BIBREF9 . 

Output: Does this paper propose a new task that others can try to improve performance on?


Input: Consider Input: We evaluate the effectiveness of our LAN model on two typical knowledge graph completion tasks, i.e., link prediction and triplet classification.

Output: Which knowledge graph completion tasks do they experiment with?


Input: Consider 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. Neural text-to-speech systems have garnered large research interest in the past 2 years. The first to fully explore this avenue of research was Google's tacotron BIBREF1 system 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


Output: How do they measure the size of models?