Ruibo Wang


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基于BiLSTM聚合模型的汉语框架语义角色识别(Chinese Frame Semantic Role Identification Based on BiLSTM Aggregation Model)
Xuefei Cao (曹学飞) | Hongji Li (李济洪) | Ruibo Wang (王瑞波) | Qian Niu (牛倩)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics


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We Need to Talk About Reproducibility in NLP Model Comparison
Yan Xue | Xuefei Cao | Xingli Yang | Yu Wang | Ruibo Wang | Jihong Li
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

NLPers frequently face reproducibility crisis in a comparison of various models of a real-world NLP task. Many studies have empirically showed that the standard splits tend to produce low reproducible and unreliable conclusions, and they attempted to improve the splits by using more random repetitions. However, the improvement on the reproducibility in a comparison of NLP models is limited attributed to a lack of investigation on the relationship between the reproducibility and the estimator induced by a splitting strategy. In this paper, we formulate the reproducibility in a model comparison into a probabilistic function with regard to a conclusion. Furthermore, we theoretically illustrate that the reproducibility is qualitatively dominated by the signal-to-noise ratio (SNR) of a model performance estimator obtained on a corpus splitting strategy. Specifically, a higher value of the SNR of an estimator probably indicates a better reproducibility. On the basis of the theoretical motivations, we develop a novel mixture estimator of the performance of an NLP model with a regularized corpus splitting strategy based on a blocked 3× 2 cross-validation. We conduct numerical experiments on multiple NLP tasks to show that the proposed estimator achieves a high SNR, and it substantially increases the reproducibility. Therefore, we recommend the NLP practitioners to use the proposed method to compare NLP models instead of the methods based on the widely-used standard splits and the random splits with multiple repetitions.


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Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models
Ruibo Wang | Jihong Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Direct comparison on point estimation of the precision (P), recall (R), and F1 measure of two natural language processing (NLP) models on a common test corpus is unreasonable and results in less replicable conclusions due to a lack of a statistical test. However, the existing t-tests in cross-validation (CV) for model comparison are inappropriate because the distributions of P, R, F1 are skewed and an interval estimation of P, R, and F1 based on a t-test may exceed [0,1]. In this study, we propose to use a block-regularized 3×2 CV (3×2 BCV) in model comparison because it could regularize the difference in certain frequency distributions over linguistic units between training and validation sets and yield stable estimators of P, R, and F1. On the basis of the 3×2 BCV, we calibrate the posterior distributions of P, R, and F1 and derive an accurate interval estimation of P, R, and F1. Furthermore, we formulate the comparison into a hypothesis testing problem and propose a novel Bayes test. The test could directly compute the probabilities of the hypotheses on the basis of the posterior distributions and provide more informative decisions than the existing significance t-tests. Three experiments with regard to NLP chunking tasks are conducted, and the results illustrate the validity of the Bayes test.


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SXUCFN-Core: STS Models Integrating FrameNet Parsing Information
Sai Wang | Ru Li | Ruibo Wang | Zhiqiang Wang | Xia Zhang
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity


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Sequential Tagging of Semantic Roles on Chinese FrameNet
Jihong Li | Ruibo Wang | Yahui Gao
Proceedings of the Eighth Workshop on Asian Language Resouces