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.

Example Input: The SnapCaptions dataset is composed of 10K user-generated image (snap) and textual caption pairs where named entities in captions are manually labeled by expert human annotators (entity types: PER, LOC, ORG, MISC).
Question: How large is their MNER SnapCaptions dataset?
Example Output: 10K user-generated image (snap) and textual caption pairs

Example Input: A "coding scheme" is defined, which is a set of labels, annotations, or codes that items in the corpus may have. Schemes include formal definitions or procedures, and often include examples, particularly for borderline cases. Next, coders are trained with the coding scheme, which typically involves interactive feedback. Training sometimes results in changes to the coding scheme, in which the first round becomes a pilot test. Then, annotators independently review at least a portion of the same items throughout the entire process, with a calculation of "inter-annotator agreement" or "inter-rater reliability." Finally, there is a process of "reconciliation" for disagreements, which is sometimes by majority vote without discussion and other times discussion-based.
Question: What are the core best practices of structured content analysis?
Example Output: "coding scheme" is defined coders are trained with the coding scheme Training sometimes results in changes to the coding scheme calculation of "inter-annotator agreement" or "inter-rater reliability." there is a process of "reconciliation" for disagreements

Example Input: On another note, we apply our formalization to evaluate multilingual $ extsc {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 thatin all analysed languagestype 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 $ extsc {bert} $'s representations do not seem to encode much more information about syntax than a trivial baseline. $ extsc {bert} $ only improves upon fastText in three of the six analysed languagesand even in those, it encodes at most (in English) $5\%$ additional information.
Question: Was any variation in results observed based on language typology?
Example Output:
It is observed some variability - but not significant. Bert does not seem to gain much more syntax information than with type level information.