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.

Example Input: The system consists of: (i) Local, shallow features based mostly on orthographic, word shape and n-gram features plus their context; and (ii) three types of simple clustering features, based on unigram matching: (i) Brown BIBREF32 clusters, taking the 4th, 8th, 12th and 20th node in the path; (ii) Clark BIBREF33 clusters and, (iii) Word2vec BIBREF34 clusters, based on K-means applied over the extracted word vectors using the skip-gram algorithm.
Example Output: What shallow local features are extracted?

Example Input: We conducted our experiments on the CMU ARCTIC database BIBREF33, which contains parallel recordings of professional US English speakers sampled at 16 kHz. One female (slt) was chosen as the target speaker and one male (bdl) and one female (clb) were chosen as sources. We selected 100 utterances each for validation and evaluation, and the other 932 utterances were used as training data. For the TTS corpus, we chose a US female English speaker (judy bieber) from the M-AILABS speech dataset BIBREF34 to train a single-speaker Transformer-TTS model. With the sampling rate also at 16 kHz, the training set contained 15,200 utterances, which were roughly 32 hours long.
Example Output: What datasets are experimented with?

Example Input: Evaluation is done using the development set, consisting of 22 documents and 1112 PIE candidates, and the test set, which consists of 23 documents and 1127 PIE candidates. For each method the best set of parameters and/or options is determined using the development set, after which the best variant by F1-score of each method is evaluated on the test set.

Since these documents in the corpus are exhaustively annotated for PIEs (see Section SECREF40), we can calculate true and false positives, and false negatives, and thus precision, recall and F1-score.
Example Output:
Are PIEs extracted automatically subjected to human evaluation?