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.

Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set. We see how NMT translations increase the number of mentions about $30\%$ with respect to human references showing even a more marked explicitation effect than human translations do.
What translationese effects are seen in the analysis?

We evaluate sentence-level semantics using averaged bag of vectors (BoV) representations on the Semantic Textual Similarity (STSB) task BIBREF21 and Word Content (WC) probing task (identify from a list of words which is contained in the sentence representation) from SentEval BIBREF22. Syntax: Similarly, we use the Google Syntactic analogies (GSyn) BIBREF9 to evaluate word-level syntactic information, and Depth (Dep) and Top Constituent (TopC) (of the input sentence's constituent parse tree) probing tasks from SentEval BIBREF22 for sentence-level syntax.
What semantic and syntactic tasks are used as probes?

We notice that the concatenation consistently outperforms the gated-attention mechanism for both training and testing instructions. We suspect that the gated-attention is useful in the scenarios where objects are described in terms of multiple attributes, but it has no to harming effect when it comes to the order connectors.

Why they conclude that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting?