LearnDCG: End-to-End Joint Optimization of Ranker and Loss in Neural Ranking
Mohammad Hossein Saliminabi · Dimitrios Androutsos · Ebrahim Bagheri
摘要
Neural rankers are typically trained with surrogate objectives because target evaluation metrics such as Normalized Discounted Cumulative Gain (NDCG) are non-differentiable. Existing approaches rely on hand-designed surrogate losses or fixed smooth relaxations, which can be misaligned with the ta...