arXiv cs.CL· Zhongqiu Li, Boyang Wang, Ruiyu Fang, Shiquan Wang, Di Wu, Jiayi Liang, Feilong Zhang, Hongfeng Du, Xiaomeng Huang, Mengjiao Bao, Luwen Pu, Xuewei He, Yu Zhao, Chao Wang, Kaidong Yu, Bingkai Yang, Qingjun Zhu, Shaoyi Du, Shuangyong Song, Yongxiang Li·· 12 小时前AI 评分31
Xing USP：面向信息抽取与意图槽位解析的跨族语义契约
Xing USP: A Cross-Family Semantic Contract for Information Extraction and Intent-Slot Parsing
AI 导读
研究团队提出 USP 跨族语义契约，用统一接口表示类别、字段、取值与实例关联，覆盖命名实体识别、关系抽取、事件抽取和意图槽位解析，并配套五种诊断匹配视图。
正文
Authors:Zhongqiu Li, Boyang Wang, Ruiyu Fang, Shiquan Wang, Di Wu, Jiayi Liang, Feilong Zhang, Hongfeng Du, Xiaomeng Huang, Mengjiao Bao, Luwen Pu, Xuewei He, Yu Zhao, Chao Wang, Kaidong Yu, Bingkai Yang, Qingjun Zhu, Shaoyi Du, Shuangyong Song, Yongxiang Li
Abstract:Structured semantic parsing is typically judged by whether the correct category is assigned, which leaves value recovery and instance-level associations unexamined: a system may recognize an event yet bind an argument to the wrong instance, or identify an intent yet supply an incorrect slot value. We present USP, a cross-family semantic contract that represents categories, fields, values, and instance associations through a common interface for named-entity recognition, relation extraction, event extraction, and intent-slot parsing. The contract is paired with five diagnostic matching views---category agreement, field recognition, field-value pairing, instance-bound assignment, and complete-instance recovery---that ask distinct questions about the same output. Using this contract, we normalize 61 datasets into a unified corpus with shared schemas, prompts, and matching rules, and compare six models on a fixed test subset. Our fine-tuned Xing4.0-29B-Intent attains 57.70 task-balanced Field-Value Micro-F1 (equally weighted across the four task families) and ranks first in every family and on all five views; its margin over the strongest alternative widens from 2.93 points for category matching to 20.73 points for complete-instance matching. This widening gap shows that category agreement alone understates structural deficits. USP thus offers the community a shared representation, a normalized 61-dataset corpus, and a diagnostic evaluation protocol that make structural agreement measurable and comparable across extraction and intent-slot tasks.
| Comments: | 25 pages, 5 figures, 15 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34343 [cs.CL] |
| (or arXiv:2610.34343v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34343 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhongqiu Li [view email]
[v1]
Sun, 25 Oct 2026 14:56:09 UTC (187 KB)
来源：arXiv cs.CL · arxiv.org