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.

Ex Input:
Tests and Gold-Standard Data for Intrinsic Evaluation
Using the gold standard data (described below), we performed three types of tests:

Class Membership Tests: embeddings corresponding two member of the same semantic class (e.g. “Months of the Year", “Portuguese Cities", “Smileys") should be close, since they are supposed to be found in mostly the same contexts.

Class Distinction Test: this is the reciprocal of the previous Class Membership test. Embeddings of elements of different classes should be different, since words of different classes ere expected to be found in significantly different contexts.

Word Equivalence Test: embeddings corresponding to synonyms, antonyms, abbreviations (e.g. “porque" abbreviated by “pq") and partial references (e.g. “slb and benfica") should be almost equal, since both alternatives are supposed to be used be interchangeable in all contexts (either maintaining or inverting the meaning).

Therefore, in our tests, two words are considered:

distinct if the cosine of the corresponding embeddings is lower than 0.70 (or 0.80).

to belong to the same class if the cosine of their embeddings is higher than 0.70 (or 0.80).

equivalent if the cosine of the embeddings is higher that 0.85 (or 0.95). 
 Question: What intrinsic evaluation metrics are used?

Ex Output:
Class Membership Tests Class Distinction Test Word Equivalence Test


Ex Input:
We also looked at where there was complete agreement by all annotators that a triple extraction was incorrect. In total there were 138 of these triples originating from 76 unique sentences.  
 Question: What is the most common error type?

Ex Output:
all annotators that a triple extraction was incorrect


Ex Input:
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. 
 Question: What translationese effects are seen in the analysis?

Ex Output:

potentially indicating a shining through effect explicitation effect