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Found 6 papers, 2 papers with code
RankDistil: Knowledge Distillation for Ranking
Knowledge distillation is an approach to improve the performance of a student model by using the knowledge of a complex teacher. Despite its success in several deep learning applications, the study of distillation is mostly confined to classification settings.
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees
We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets.
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework.
Domain Adaptation for Enterprise Email Search
To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise.
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
Gated-Attention Architectures for Task-Oriented Language Grounding
To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment.
