相关实验视频
通过混合深度学习计算句子相似度得分,特别关注否定句子
Rohit M1, Jeganathan L1, Srinivasa Rao Ummity2
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Scientific reports
|March 1, 2026
概括
本研究引入了否定对齐相似性 (NAS) 计分器,以改进自动化评估系统. 纳斯得分器准确地估计了句子的相似性,即使有否定,提高了评分的公平性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 准确的句子相似性对于自动化评估系统 (AES) 至关重要,以确保公平和一致的评分.
- 现有的基于变压器的句子相似性工具 (SST) 难以处理由否定引起的语义扭曲.
- 否定显著影响语义意义,对当前的相似性模型构成挑战.
研究的目的:
- 提出一种新的否定对齐相似性 (NAS) 计分器,用于强大的,否定意识的语义建模.
- 通过解决句子相似性的否定,提高自动化评估系统 (AES) 的可靠性和公平性.
- 开发一个混合框架,整合多嵌入式融合和上下文编码,以提高语义理解.
主要方法:
- 实现了一个混合语义相似性框架,包含多嵌入式融合 (BERT,SBERT,RoBERTa,DistilBERT,Word2Vec).
- 利用基于BiLSTM的上下文编码来捕捉句子对中的顺序依赖性.
- 策划了一个定制的否定句子相似性数据集 (NSSD),包含8575个在四个技术领域的人类验证的句子对.
主要成果:
- 拟议的NAS评分器在STS基准数据集上实现了0.97的F1评分,在规模规范化后.
- 在否定意识的句子相似性任务中,与基于强大的变压器的基线相比,表现显著改善.
- 验证了多嵌入式融合和BiLSTM上下文编码方法的有效性.
结论:
- 纳斯得分器有效地模拟了通过否定引入的语义转移,从而更可靠地估计了句子相似性.
- 拟议的框架为需要细微的语义理解的自动化评估系统 (AES) 提供了重大进展.
- 这种方法非常适合在AES的现实世界部署,提高分级的准确性和一致性.
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