  Assessing an AI agent that can converse in human language and understand
visual content is challenging. Generation metrics, such as BLEU scores favor
correct syntax over semantics. Hence a discriminative approach is often used,
where an agent ranks a set of candidate options. The mean reciprocal rank (MRR)
metric evaluates the model performance by taking into account the rank of a
single human-derived answer. This approach, however, raises a new challenge:
the ambiguity and synonymy of answers, for instance, semantic equivalence
(e.g., `yeah' and `yes'). To address this, the normalized discounted cumulative
gain (NDCG) metric has been used to capture the relevance of all the correct
answers via dense annotations. However, the NDCG metric favors the usually
applicable uncertain answers such as `I don't know. Crafting a model that
excels on both MRR and NDCG metrics is challenging. Ideally, an AI agent should
answer a human-like reply and validate the correctness of any answer. To
address this issue, we describe a two-step non-parametric ranking approach that
can merge strong MRR and NDCG models. Using our approach, we manage to keep
most MRR state-of-the-art performance (70.41% vs. 71.24%) and the NDCG
state-of-the-art performance (72.16% vs. 75.35%). Moreover, our approach won
the recent Visual Dialog 2020 challenge. Source code is available at
https://github.com/idansc/mrr-ndcg.
