TASK DEFINITION: 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.
PROBLEM:  In almost all genres, DenseNMT models are significantly better than the baselines.

SOLUTION: did they outperform previous methods?

PROBLEM: We use the existing large-scale antonym and synonym pairs previously used by Nguyen:16. Originally, the data pairs were collected from WordNet BIBREF9 and Wordnik.

SOLUTION: What dataset do they use to evaluate their method?

PROBLEM: We train three systems (S1, S2 and S3) with the corpora summarised in Table TABREF5. The first two systems are transformer models trained on different amounts of data (6M vs. 18M parallel sentences as seen in the Table). The third system includes a modification to consider the information of full coreference chains throughout a document augmenting the sentence to be translated with this information and it is trained with the same amount of sentence pairs as S1.

SOLUTION:
Which three neural machine translation systems are analyzed?