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.

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 open source LJSpeech Dataset was used to train our TTS model. The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text

Ex Output:
Which dataset(s) do they evaluate on?


Ex Input:
To test if the learned representations can separate phonetic categories, we use a minimal pair ABX discrimination task BIBREF19 , BIBREF20 . For the within-speaker task, all the phones triplets belong to the same speaker (e.g. $x$3 ) Finally the scores for every pair of central phones are averaged and subtracted from 1 to yield the reported within-talker ABX error rate. For the across-speaker task, $x$4 and $x$5 belong to the same speaker, and $x$6 to a different one (e.g. $x$7 ). The scores for a given minimal pair are first averaged across all of the pairs of speakers for which this contrast can be made. As above, the resulting scores are averaged over all contexts over all pairs of central phones and converted to an error rate.

Ex Output:
What is the metric that is measures in this paper?


Ex Input:

 In this section, we describe our approach to classify user reactions into one of eight types of discourse: agreement, answer, appreciation, disagreement, elaboration, humor, negative reaction, or question, or as none of the given labels, which we call “other”, using linguistically-infused neural network models.

Ex Output:

What are the nine types?