instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word.
Question: Which word embeddings are analysed?
answer:
Continuous Bag-of-Words (CBOW)

question:
The 16 classes are inspired by the OntoNotes5 corpus BIBREF7 as well as the ACE (Automatic Content Extraction) English Annotation Guidelines for Entities Version 6.6 2008.06.13 BIBREF8.
Question: How did they determine the distinct classes?
answer:
inspired by the OntoNotes5 corpus BIBREF7 as well as the ACE (Automatic Content Extraction) English Annotation Guidelines for Entities Version 6.6 2008.06.13 BIBREF8

question:
SCA BIBREF5 softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words, i.e., replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary.
Question: How does soft contextual data augmentation work?
answer:
softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary