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.

To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements. 
 Question: How big is dataset used for fine-tuning BERT?
hundreds of thousands of legal agreements

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?
Yes

At the same time, some information about inflected word forms in the context can be useful, but it is lost during lemmatization, and this leads to the decreased score. Arguably, this means that lemmatization brings along both advantages and disadvantages for WSD with ELMo.  
 Question: Do the authors mention any downside of lemmatizing input before training ELMo?

Yes