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.

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?
Class Membership Tests Class Distinction Test Word Equivalence Test

Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead.  
 Question: What misbehavior is identified?
if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations

Interesting prior work on quantifying social norm violation has taken a heavily data-driven focus BIBREF8 , BIBREF9 .  
 Question: Does this paper propose a new task that others can try to improve performance on?

No, there has been previous work on recognizing social norm violation.