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OasisSimp

把“本通知自印发之日起施行”改写成“这个通知从发出当天开始执行”——多数语言连一套评测基准都没有。五种语言的母语者写出第一套开放基准。

arXiv Project / Dataset
5语言
9,519原句
~4平均参考简化
8开源多语 LLM
CC BY 4.0数据许可

一份政府公告、一页用药说明,公开并不等于读得懂——对第二语言读者、学生和有阅读障碍的人来说,复杂的长句本身就是一道门槛。OasisSimp 为五种语言建立了句子简化的开放评测基准:语料全部由母语者撰写,覆盖英语、僧伽罗语、泰米尔语、泰语和普什图语。

语种 · 简化操作复杂句 → 简化句(论文所附英文对照)英语改写Alarmed at the escalating bill for weather-related disaster relief, Public Safety Minister…Public Safety Minister Ralph Goodale is discussing with insurers and provinces…普什图语删除The agricultural land in the district is small but very fertile. Major crops include…There is little agricultural land in Namara district; major crops include maize…僧伽罗语删除、改写Steps were taken to remove the names of two individuals from the voters' register…Two people, who were reported to the Elections Department in 2015 for imprisonment…泰语调序、删除Meanwhile, on Din Daeng Road, from the Din Daeng Expressway Intersection heading towards…There is very heavy traffic on Din Daeng Road in both directions towards the Din Daeng…泰米尔语删除、改写Located in a very attractive location, this training center has a hall that can…Located in an attractive location, in this training center, a hall where 250 people…五个语种中有四个用到删除;只有英语是纯改写。
据论文重绘。每个语种一个样例,句子过长处以省略号截断。普什图语、僧伽罗语、泰语与泰米尔语沿用论文原文旁所附的英文对照。

读得到,不等于读得懂

标题里的那次改写,在自然语言处理中称为句子简化(sentence simplification):把一句话改写得更易读,同时不丢失原意。它是公共信息、教育和无障碍阅读的底层能力:政务部门希望公告人人能懂,出版与教育机构需要分级读物,健康信息的可读性更是直接影响理解成本。

英语的句子简化研究已有多年积累的可用基准;而对僧伽罗语、普什图语、泰米尔语、泰语这样的低资源语言(可用语料和评测数据稀缺的语言),过去几乎没有任何公开评测——连衡量一个简化系统好坏的尺子都不存在。OasisSimp 补上的正是这把尺子。

母语者写出的评测基准

语料全部来自真实场景:政府文件、新闻与 Wikipedia。9,519 个复杂句中的每一句,都由母语者按统一指南写出多条简化参考(多参考——同一句允许多种可接受的简化写法,避免把某一种答案当成唯一标准)。

数据按 80% test / 20% validation 切分,并以 CC BY 4.0 许可完全开放,定位为评测基准而非训练语料。

Table 1. OasisSimp 数据集最终统计
Lang# Comp SentencesAvg. Simp SentencesAvg. Comp LengthAvg. Simp LengthSource Domain
English25002.8624.3517.23News
Sinhala25005.0030.1228.78Govt
Thai14995.0648.2437.77News
Tamil5204.6623.2217.65Govt
Pashto25003.0028.8120.31Wiki

八个开源模型的现状

论文用 SARI(句子简化的标准自动指标,分别衡量新增、保留、删除三类改写操作,对应表中的 ADD / KEEP / DEL)和 BERTScore(基于语义相似度的自动评分)评测了 8 个开源多语大模型。

结果有两层。少样本示例(few-shot——在提问中附上几个示范例子)几乎在所有语言上带来提升,说明风格可以校准;但低资源语言的绝对表现仍然明显落后,尤其当简化需要新增合适的简单表达,而不只是删掉冗余信息时。

Table 3. Results on English (OasisSimp-EN)
Model0 Shot1 Shot5 Shot
SARI Comp.SARIFrefSARI Comp.SARIFrefSARI Comp.SARIFref
ADDKEEPDELADDKEEPDELADDKEEPDEL
Aya ✓9.3244.9875.2343.1854.449.6844.9072.5142.3656.3510.1845.9171.1642.4257.20
Cmd-R ✓9.6944.9572.8942.5155.9010.9943.7177.5744.0955.0311.9145.2877.0944.7656.63
DeepSeek ✓7.0341.4776.3041.6051.887.8041.1276.8241.9151.929.4142.0377.2242.8954.15
EuroLLM ✓9.3245.6068.3641.1056.9810.9946.9869.3542.4457.9611.6346.5570.9343.0458.10
Gemma ✓5.2444.4368.5439.4051.876.5543.2674.4441.4152.349.1944.6777.0643.6455.27
LLaMA6.4843.3168.3439.3854.308.1143.4272.8341.4554.539.9344.7573.7542.8156.00
Mistral ✓8.5643.6677.4643.2352.4910.3143.8278.4344.1854.5511.6144.0178.5944.7455.89
Qwen ✓8.7046.0773.5342.7742.369.5446.4077.2544.3953.0310.8847.0177.0844.9955.27
Table 7. Results on Pashto (OasisSimp-PS)
Model0 Shot1 Shot5 Shot
SARI Comp.SARIFrefSARI Comp.SARIFrefSARI Comp.SARIFref
ADDKEEPDELADDKEEPDELADDKEEPDEL
Aya ✕0.6223.9867.4730.6949.171.0845.6058.6235.1060.831.7753.8147.1734.2568.25
Cmd-R ✕0.7550.8251.7334.4461.910.9354.4144.4433.2667.840.7056.5335.6230.9570.52
DeepSeek ✕0.5241.1960.7134.1438.650.9048.8354.5934.7863.650.9150.1652.5134.5366.26
EuroLLM ✕0.5054.2844.4033.0667.550.6554.8743.2832.9369.720.7855.3742.0932.7570.42
Gemma ✕3.8425.0870.7833.2356.954.4734.7568.5735.9361.475.3946.3961.9537.9166.04
LLaMA ✕0.7018.3470.2829.77-22.403.1546.2861.6737.0451.151.9646.5358.1535.5533.03
Mistral ✕0.9426.3668.1331.8147.731.4241.2063.0435.2261.311.5145.9358.6035.3564.40
Qwen ✕2.3447.4858.9236.2558.022.8149.8855.3436.0164.762.6253.7948.7135.0465.57
  • 多参考很关键。单一简化目标会低估可接受改写空间。
  • 少样本有效但不够。示例能校准风格,却不能消除语言资源差距。
  • ADD 最难。模型擅长删除冗余信息,但在低资源语言中很难新增合适的简单表达。

对多语言 AI 的提醒

低资源语言终于有可复现基准。泰语、普什图语、泰米尔语过去几乎没有可用句子简化数据。
服务真实读者,而不只是排行榜。简化系统直接关系到政府公告、教育材料和健康信息的可读性。
把“英语能力外推”拉回现实。英文表现不能代表多语简化能力,OasisSimp 给出了具体证据。

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项目页提供数据、说明和评测入口,适合多语 NLP 与可访问性研究复用。

arXiv Project / Dataset