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.

Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. As we would expect, on the probe for predicting chunk tags, mSynC achieves 96.9 $F_1$ vs. 92.2 $F_1$ for ELMo-transformer, indicating that mSynC is indeed encoding shallow syntax. Overall, the results further confirm that explicit shallow syntax does not offer any benefits over ELMo-transformer.
What are improvements for these two approaches relative to ELMo-only baselines?

We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively.
What profile metadata is used for this analysis?

During inference, we adopt beam search with a beam size of 5 for De-En, En-Fr and En-Vi translation tasks.

What are three main machine translation tasks?