A Gold Standard Dataset for the Reviewer Assignment Problem

• URL: http://arxiv.org/abs/2303.16750v2
• Date: Fri, 30 May 2025 08:34:07 GMT
• Title: A Gold Standard Dataset for the Reviewer Assignment Problem
• Authors: Ivan Stelmakh, John Wieting, Sarina Xi, Graham Neubig, Nihar B. Shah,
• Abstract summary: "Similarity score" is a numerical estimate of the expertise of a reviewer in reviewing a paper.<n>Key challenge in comparing existing algorithms and developing better algorithms is the lack of publicly available gold-standard data.<n>We collect a novel dataset of similarity scores that we release to the research community.
• Score: 70.45113777449373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many peer-review venues are using algorithms to assign submissions to reviewers. The crux of such automated approaches is the notion of the "similarity score" -- a numerical estimate of the expertise of a reviewer in reviewing a paper -- and many algorithms have been proposed to compute these scores. However, these algorithms have not been subjected to a principled comparison, making it difficult for stakeholders to choose the algorithm in an evidence-based manner. The key challenge in comparing existing algorithms and developing better algorithms is the lack of publicly available gold-standard data. We address this challenge by collecting a novel dataset of similarity scores that we release to the research community. Our dataset consists of 477 self-reported expertise scores provided by 58 researchers who evaluated their expertise in reviewing papers they have read previously. Using our dataset, we compare several widely used similarity algorithms and offer key insights. First, all algorithms exhibit significant error, with misranking rates between 12%-30% in easier cases and 36%-43% in harder ones. Second, most specialized algorithms are designed to work with titles and abstracts of papers, and in this regime the SPECTER2 algorithm performs best. Interestingly, classical TF-IDF matches SPECTER2 in accuracy when given access to full submission texts. In contrast, off-the-shelf LLMs lag behind specialized approaches.

Related papers

• Towards Comparable Active Learning [6.579888565581481]
  We show that the reported lifts in recent literature generalize poorly to other domains leading to an inconclusive landscape in Active Learning research. This paper addresses these issues by providing an Active Learning framework for a fair comparison of algorithms across different tasks and domains, as well as a fast and perform oracleant algorithm for evaluation.
  arXiv  Detail & Related papers  (2023-11-30T08:54:32Z)
• Regularization-Based Methods for Ordinal Quantification [49.606912965922504]
  We study the ordinal case, i.e., the case in which a total order is defined on the set of n>2 classes. We propose a novel class of regularized OQ algorithms, which outperforms existing algorithms in our experiments.
  arXiv  Detail & Related papers  (2023-10-13T16:04:06Z)
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [51.8723187709964]
  We study the OOD generalization of neural algorithmic reasoning tasks. The goal is to learn an algorithm from input-output pairs using deep neural networks.
  arXiv  Detail & Related papers  (2022-11-01T18:33:20Z)
• Stochastic Differentially Private and Fair Learning [7.971065005161566]
  We provide the first differentially private algorithm for fair learning that is guaranteed to converge. Our framework is flexible enough to permit different fairness, including demographic parity and equalized odds. Our algorithm can be applied to non-binary classification tasks with multiple (non-binary) sensitive attributes.
  arXiv  Detail & Related papers  (2022-10-17T06:54:57Z)
• The CLRS Algorithmic Reasoning Benchmark [28.789225199559834]
  Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms. We propose the CLRS Algorithmic Reasoning Benchmark, covering classical algorithms from the Introduction to Algorithms textbook. Our benchmark spans a variety of algorithmic reasoning procedures, including sorting, searching, dynamic programming, graph algorithms, string algorithms and geometric algorithms.
  arXiv  Detail & Related papers  (2022-05-31T09:56:44Z)
• Algorithmic Fairness Datasets: the Story so Far [68.45921483094705]
  Data-driven algorithms are studied in diverse domains to support critical decisions, directly impacting people's well-being. A growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair Machine Learning hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity)
  arXiv  Detail & Related papers  (2022-02-03T17:25:46Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• A Pragmatic Look at Deep Imitation Learning [0.3626013617212666]
  We re-implement 6 different adversarial imitation learning algorithms. We evaluate them on a widely-used expert trajectory dataset. GAIL consistently performs well across a range of sample sizes.
  arXiv  Detail & Related papers  (2021-08-04T06:33:10Z)
• Estimating leverage scores via rank revealing methods and randomization [50.591267188664666]
  We study algorithms for estimating the statistical leverage scores of rectangular dense or sparse matrices of arbitrary rank. Our approach is based on combining rank revealing methods with compositions of dense and sparse randomized dimensionality reduction transforms.
  arXiv  Detail & Related papers  (2021-05-23T19:21:55Z)
• Comparative Analysis of Extreme Verification Latency Learning Algorithms [3.3439097577935213]
  This paper is a comprehensive survey and comparative analysis of some of the EVL algorithms to point out the weaknesses and strengths of different approaches. This work is a very first effort to provide a review of some of the existing algorithms in this field to the research community.
  arXiv  Detail & Related papers  (2020-11-26T16:34:56Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)
• Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data [9.04391541965756]
  This paper investigates the performance of 15 structure learning algorithms. Each algorithm is tested over multiple case studies, sample sizes, types of noise, and assessed with multiple evaluation criteria. Results suggest traditional synthetic performance may overestimate real-world performance by anywhere between 10% and more than 50%.
  arXiv  Detail & Related papers  (2020-05-18T18:40:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.