  In precision-oriented tasks like answer ranking, it is more important to rank
many relevant answers highly than to retrieve all relevant answers. It follows
that a good ranking strategy would be to learn how to identify the easiest
correct answers first (i.e., assign a high ranking score to answers that have
characteristics that usually indicate relevance, and a low ranking score to
those with characteristics that do not), before incorporating more complex
logic to handle difficult cases (e.g., semantic matching or reasoning). In this
work, we apply this idea to the training of neural answer rankers using
curriculum learning. We propose several heuristics to estimate the difficulty
of a given training sample. We show that the proposed heuristics can be used to
build a training curriculum that down-weights difficult samples early in the
training process. As the training process progresses, our approach gradually
shifts to weighting all samples equally, regardless of difficulty. We present a
comprehensive evaluation of our proposed idea on three answer ranking datasets.
Results show that our approach leads to superior performance of two leading
neural ranking architectures, namely BERT and ConvKNRM, using both pointwise
and pairwise losses. When applied to a BERT-based ranker, our method yields up
to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model
trained without a curriculum). This results in models that can achieve
comparable performance to more expensive state-of-the-art techniques.
