Weakly-Supervised Question Answering with Effective Rank and Weighted Loss over Candidates

Qin, Haozhe; Zhu, Jingtao; Shen, Beijun

doi:10.1145/3442381.3449919

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Distant supervision has been used to train MRC models in low-resource settings, and two main kinds of approaches have been proposed to address the mislabeling problem: (1) filtering noisy labels, and (2) modeling answer spans as latent variables. The noise filtering approaches learn to score and rank DS instances based on answer span positions Tay et al, 2018;Swayamdipta et al, 2018;Clark and Gardner, 2018;Lin et al, 2018;Joshi et al, 2017;Chen et al, 2017), question-passage similarities (Hong et al, 2022;Qin et al, 2021;Shao et al, 2021;Deng et al, 2021) and model confidences Zhu et al, 2022). The latent variable-based approaches jointly train MRC models and identify correct answer spans using hard-EM algorithms (Zhao et al, 2021;Min et al, 2019;Cheng et al, 2020).…”

Section: Relate Workmentioning

confidence: 99%

Contrastive Distant Supervision for Debiased and Denoised Machine Reading Comprehension

Bian,

Lin,

Han

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Distant Supervision (DS) is a promising learning approach for MRC by leveraging easilyobtained question-answer pairs. Unfortunately, the heuristically annotated dataset will inevitably lead to mislabeled instances, resulting in answer bias and context noise problems. To learn debiased and denoised MRC models, this paper proposes the Contrastive Distant Supervision algorithm -CDS, which can learn to distinguish confusing and noisy instances via confidence-aware contrastive learning. Specifically, to eliminate answer bias, CDS samples counterfactual negative instances, which ensures that MRC models must take both answer information and question-context interaction into consideration. To denoise distantly annotated contexts, CDS samples confusing negative instances to increase the margin between correct and mislabeled instances. We further propose a confidence-aware contrastive loss to model and leverage the uncertainty of all DS instances during learning. Experimental results show that CDS is effective and can even outperform supervised MRC models without manual annotations.

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Section: Relate Workmentioning

confidence: 99%

Contrastive Distant Supervision for Debiased and Denoised Machine Reading Comprehension

Bian,

Lin,

Han

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Weakly-supervised explainable question answering via question aware contrastive learning and adaptive gate mechanism

Fan,

Zhang,

et al. 2025

Information Sciences

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Tensfa: Detecting and Repairing Tensor Shape Faults in Deep Learning Systems

Wu¹,

Shen²,

Chen³

et al. 2021

2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)

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Software developers frequently adopt deep learning (DL) libraries to incorporate learning solutions into software systems. However, misuses of these libraries can cause various DL faults. Among them, tensor shape faults are most prevalent. Tensor shape faults occur when restriction conditions of operations are not met, leading to many system crashes. To support efficient detection and fixing of these faults, we conduct an empirical study to obtain a deep insight. We construct SFData, a set of 146 buggy programs with crashing tensor shape faults (i.e., those causing programs to crash). By analyzing the faults in SFData, we categorize them into four types and get some valuable observations.

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Weakly-Supervised Question Answering with Effective Rank and Weighted Loss over Candidates

Cited by 4 publications

References 15 publications

Contrastive Distant Supervision for Debiased and Denoised Machine Reading Comprehension

Contrastive Distant Supervision for Debiased and Denoised Machine Reading Comprehension

Weakly-supervised explainable question answering via question aware contrastive learning and adaptive gate mechanism

Tensfa: Detecting and Repairing Tensor Shape Faults in Deep Learning Systems

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