An Axiomatic Approach to Regularizing Neural Ranking Models
Axiomatic information retrieval (IR) seeks a set of principle properties desirable in IR models. These properties when formally expressed provide guidance in the search for better relevance estimation functions. Neural ranking models typically contain a large number of parameters. The training of these models involve a search for appropriate parameter values based on large quantities of labeled examples. Intuitively, axioms that can guide the search for better traditional IR models should also help in better parameter estimation for machine learning based rankers. This work explores the use of IR axioms to augment the direct supervision from labeled data for training neural ranking models. We modify the documents in our dataset along the lines of well-known axioms during training and add a regularization loss based on the agreement between the ranking model and the axioms on which version of the document---the original or the perturbed---should be preferred. Our experiments show that the neural ranking model achieves faster convergence and better generalization with axiomatic regularization.
Code (0)
등록된 구현이 없습니다.
Tasks
Information Retrievalparameter estimationRetrievalSimilar Papers 제목 키워드 기반
Towards Axiomatic Explanations for Neural Ranking Models
Recently, neural networks have been successfully employed to improve upon state-of-the-art performance in ad-hoc retrieval tasks via machine-learned ranking functions. While neural retrieval models grow in complexity and…
Document RankingInformation RetrievalRetrievalRankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
Numerous works propose post-hoc, model-agnostic explanations for learning to rank, focusing on ordering entities by their relevance to a query through feature attribution methods. However, these attributions often weakly…
Computational EfficiencyLearning-To-RankA modified axiomatic foundation of the analytic hierarchy process
This paper reports a modified axiomatic foundation of the analytic hierarchy process (AHP), where the reciprocal property of paired comparisons is broken. The novel concept of reciprocal symmetry breaking is proposed to …
Indivisible Participatory Budgeting under Weak Rankings
Participatory budgeting (PB) has attracted much attention in recent times due to its wide applicability in social choice settings. In this paper, we consider indivisible PB which involves allocating an available, limited…
FairnessHigh-Order Tensor Regularization With Application to Attribute Ranking
When learning functions on manifolds, we can improve performance by regularizing with respect to the intrinsic manifold geometry rather than the ambient space. However, when regularizing tensor learning, calculating the …
AttributeVocal Bursts Intensity Prediction