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:
In a second step, we will train a neural monolingual translation system, that translates from the output of the PBMT system INLINEFORM0 to a better target sentence INLINEFORM1 .
Question: Do they train the NMT model on PBMT outputs?
answer:
Yes

question:
We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.
Question: What baseline is used?
answer:
Waseem and Hovy BIBREF5 Davidson et al. BIBREF9 Waseem et al. BIBREF10

question:
To show the effectiveness of our approach, we show results on the SICK dataset BIBREF1, a common benchmark for logic-based NLI, and find MonaLog to be competitive with more complicated logic-based approaches (many of which require full semantic parsing and more complex logical machinery).
Question: Do they beat current state-of-the-art on SICK?
answer:
No