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.

The words are represented as fixed length vectors INLINEFORM0 resulting from the concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation. Since we cannot directly concatenate token-based embeddings (provided in GloVe) with the lemma#PoS-based representation available in DepecheMood, we proceeded to re-build the latter in token-based form, applying the exact same methodology albeit with two differences: we started from a larger dataset (51.9K news articles instead of 25.3K) and used a frequency cut-off, i.e. keeping only those tokens that appear at least 5 times in the corpus.
How do they incorporate lexicon into the neural network?

The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora.
Do they test their approach on a dataset without incomplete data?

To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features.

Which features do they use to model Twitter messages?