@inproceedings{liu-etal-2026-distributional,
title = "Distributional Stabilization for Zero-Shot Relation Extraction",
author = "Liu, Yongxin and
Zhang, Huaping and
Li, Qiuchi and
Gao, Chunxiao and
Lv, Haocheng and
Ruohao, Yan and
Zhang, Baohua and
Li, Lei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1276/",
pages = "29095--29111",
ISBN = "979-8-89176-459-0",
abstract = "Joint-encoding zero-shot relation extraction classifies all candidate relation labels in a single Transformer forward pass, achieving strong accuracy and high throughput. However, under a controlled full-vocabulary diagnostic, two distributional pathologies surface: unnormalized dot-product scoring causes recall collapse, with certain types receiving zero predictions, while the per-instance BCE loss leaves gold and negative scores entangled beyond any reliable threshold{---}confidence ambiguity. We propose SCAR, which keeps the baseline pathway intact and stabilizes both distributions via two auxiliary branches: a gradient-isolated coarse-to-fine cascade with Z-score normalization that restores cross-relation comparability, and a training-only contrastive alignment module that separates gold from negative scores. On Wiki-ZSL (m=15), SCAR raises Macro F1 from 75.9{\%} to 83.0{\%} and cuts zero-recall types from 35 to 1, suggesting that small-pool ZSRE protocols can obscure failure modes critical to large-vocabulary deployment."
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<abstract>Joint-encoding zero-shot relation extraction classifies all candidate relation labels in a single Transformer forward pass, achieving strong accuracy and high throughput. However, under a controlled full-vocabulary diagnostic, two distributional pathologies surface: unnormalized dot-product scoring causes recall collapse, with certain types receiving zero predictions, while the per-instance BCE loss leaves gold and negative scores entangled beyond any reliable threshold—confidence ambiguity. We propose SCAR, which keeps the baseline pathway intact and stabilizes both distributions via two auxiliary branches: a gradient-isolated coarse-to-fine cascade with Z-score normalization that restores cross-relation comparability, and a training-only contrastive alignment module that separates gold from negative scores. On Wiki-ZSL (m=15), SCAR raises Macro F1 from 75.9% to 83.0% and cuts zero-recall types from 35 to 1, suggesting that small-pool ZSRE protocols can obscure failure modes critical to large-vocabulary deployment.</abstract>
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%0 Conference Proceedings
%T Distributional Stabilization for Zero-Shot Relation Extraction
%A Liu, Yongxin
%A Zhang, Huaping
%A Li, Qiuchi
%A Gao, Chunxiao
%A Lv, Haocheng
%A Ruohao, Yan
%A Zhang, Baohua
%A Li, Lei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-distributional
%X Joint-encoding zero-shot relation extraction classifies all candidate relation labels in a single Transformer forward pass, achieving strong accuracy and high throughput. However, under a controlled full-vocabulary diagnostic, two distributional pathologies surface: unnormalized dot-product scoring causes recall collapse, with certain types receiving zero predictions, while the per-instance BCE loss leaves gold and negative scores entangled beyond any reliable threshold—confidence ambiguity. We propose SCAR, which keeps the baseline pathway intact and stabilizes both distributions via two auxiliary branches: a gradient-isolated coarse-to-fine cascade with Z-score normalization that restores cross-relation comparability, and a training-only contrastive alignment module that separates gold from negative scores. On Wiki-ZSL (m=15), SCAR raises Macro F1 from 75.9% to 83.0% and cuts zero-recall types from 35 to 1, suggesting that small-pool ZSRE protocols can obscure failure modes critical to large-vocabulary deployment.
%U https://aclanthology.org/2026.emnlp-main.1276/
%P 29095-29111
Markdown (Informal)
[Distributional Stabilization for Zero-Shot Relation Extraction](https://aclanthology.org/2026.emnlp-main.1276/) (Liu et al., EMNLP 2026)
ACL
- Yongxin Liu, Huaping Zhang, Qiuchi Li, Chunxiao Gao, Haocheng Lv, Yan Ruohao, Baohua Zhang, and Lei Li. 2026. Distributional Stabilization for Zero-Shot Relation Extraction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29095–29111, Budapest, Hungary. Association for Computational Linguistics.