We further train both models on the adversarial training set, and the results are shown in the last two columns of Table 4.
for multi-hop QA, directly matching the semantics of the question and context leads to the entity that bridges the two supporting facts (e.g., "Shirley Temple"), or the entities that need to be compared against each other (e.g., nationalities of Scott and Ed).