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 Q]: For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model.
[EX A]: What previously annotated databases are available?

[EX Q]: For a given transcribed utterance, it is firstly encoded with Byte Pair Encoding (BPE) BIBREF14, a compression algorithm splitting words to fundamental subword units (pairs of bytes or BPs) and reducing the embedded vocabulary size. Then we use a BiLSTM BIBREF15 encoder and the output state of the BiLSTM is regarded as a vector representation for this utterance. Finally, a fully connected Feed-forward Neural Network (FNN) followed by a softmax layer, labeled as a multilayer perceptron (MLP) module, is used to perform the domain/intent classification task based on the vector. We name it Oracle simply because we assume that hypotheses are noisy versions of transcription.
[EX A]: Which ASR system(s) is used in this work?

[EX Q]: We mix standard linguistic features, such as Part-Of-Speech (POS) and chunk tag, together with several gazetteers specifically built for classical music, and a series of features representing tokens' left and right context.
[EX A]:
What kind of corpus-based features are taken into account?