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.

[Q]: Experimental Protocol ::: Datasets ::: Training Dataset
(i) TTS System dataset: We trained our TTS system with a mixture of neutral and newscaster style speech. For a total of 24 hours of training data, split in 20 hours of neutral (22000 utterances) and 4 hours of newscaster styled speech (3000 utterances).

(ii) Embedding selection dataset: As the evaluation was carried out only on the newscaster speaking style, we restrict our linguistic search space to the utterances associated to the newscaster style: 3000 sentences.

Experimental Protocol ::: Datasets ::: Evaluation Dataset
The systems were evaluated on two datasets:

(i) Common Prosody Errors (CPE): The dataset on which the baseline Prostron model fails to generate appropriate prosody. This dataset consists of complex utterances like compound nouns (22%), “or" questions (9%), “wh" questions (18%). This set is further enhanced by sourcing complex utterances (51%) from BIBREF24.

(ii) LFR: As demonstrated in BIBREF25, evaluating sentences in isolation does not suffice if we want to evaluate the quality of long-form speech. Thus, for evaluations on LFR we curated a dataset of news samples. The news style sentences were concatenated into full news stories, to capture the overall experience of our intended use case.
[A]: What dataset is used for train/test of this method?


[Q]: Our method is tested on Twitter datasets. This microblogging platform has been widely used to analyze discussions and polarization BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF2. It is a natural choice for these kind of problems, as it represents one of the main fora for public debate in online social media BIBREF15, it is a common destination for affiliative expressions BIBREF16 and is often used to report and read news about current events BIBREF17. An extra advantage of Twitter for this kind of studies is the availability of real-time data generated by millions of users. Other social media platforms offer similar data-sharing services, but few can match the amount of data and the accompanied documentation provided by Twitter. One last asset of Twitter for our work is given by retweets, whom typically indicate endorsement BIBREF18 and hence become a useful concept to model discussions as we can set “who is with who". However, our method has a general approach and it could be used a priori in any social network. In this work we report excellent result tested on Twitter but in future work we are going to test it in other social networks.
[A]: What social media platform is observed?


[Q]: The baseline model for our experiments is explained in the paper by Alec Go [1]. The model uses the Naive Bayes, SVM, and the Maximum Entropy classifiers for their experiment.
[A]:
What previously proposed methods is this method compared against?