instruction:
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.
question:
We compare the above model to a similar model, where rather than explicitly represent $K$ features as input, we have $K$ features in the form of a genre embedding, i.e. we learn a genre specific embedding for each of the gothic, scifi, and philosophy genres, as studied in BIBREF8 and BIBREF7.
answer:
Is this style generator compared to some baseline?


question:
In our experiments, we use as input the 2210 tokenized sentences of the Stanford Sentiment Treebank test set BIBREF2 , preprocessing them by lowercasing as was done in BIBREF8 .
answer:
Which datasets are used for evaluation?


question:
In our experiments, we compared about 60 elementary metrics, which can be organised as follows:

MT metrics

BLEU, ROUGE, METEOR, TERp

Variants of BLEU: BLEU_1gram, BLEU_2gram, BLEU_3gram, BLEU_4gram and seven smoothing methods from NLTK BIBREF32 .

Intermediate components of TERp inspired by BIBREF18 : e.g. number of insertions, deletions, shifts...

Readability metrics and other sentence-level features: FKGL and FRE, numbers of words, characters, syllables...

Metrics based on the baseline QuEst features (17 features) BIBREF28 , such as statistics on the number of words, word lengths, language model probability and INLINEFORM0 -gram frequency.

Metrics based on other features: frequency table position, concreteness as extracted from BIBREF33 's BIBREF33 list, language model probability of words using a convolutional sequence to sequence model from BIBREF34 , comparison methods using pre-trained fastText word embeddings BIBREF35 or Skip-thought sentence embeddings BIBREF36 .
answer:

what approaches are compared?