Given the task definition and input, reply with output. 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.

On another note, we apply our formalization to evaluate multilingual $\textsc {bert} $'s syntax knowledge on a set of six typologically diverse languages. Although it does encode a large amount of information about syntax (more than $81\%$ in all languages), it only encodes at most $5\%$ more information than some trivial baseline knowledge (a type-level representation). This indicates that the task of POS labeling (word-level POS tagging) is not an ideal task for contemplating the syntactic understanding of contextual word embeddings. We see that—in all analysed languages—type level embeddings can already capture most of the uncertainty in POS tagging. We also see that BERT only shares a small amount of extra information with the task, having small (or even negative) gains in all languages. Finally, when put into perspective, multilingual $\textsc {bert} $'s representations do not seem to encode much more information about syntax than a trivial baseline. $\textsc {bert} $ only improves upon fastText in three of the six analysed languages—and even in those, it encodes at most (in English) $5\%$ additional information. 
 Question: Was any variation in results observed based on language typology?

It is observed some variability - but not significant. Bert does not seem to gain much more syntax information than with type level information.