English(EN) Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI

新的神经符号NLI系统提高了命题链接的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新颖的神经符号自然语言推断（NLI）方法，该方法提高了前提句和假设句之间命题链接的准确性。该新系统引入了一个可训练的原子链接器，与先前使用固定基于嵌入的选择或不变的句子级NLI模型不同，它可以联合预测链接存在、源选择和关系类型。该系统仅使用句子级NLI标签进行训练，无需特定的原子对注释，并在SNLI、SICK和MNLI等基准数据集上显示出显著的准确性提高。 AI

影响这项研究可能导致更强大、更准确的自然语言理解系统，尤其是在复杂推理任务中。

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arXiv cs.CL TIER_1 English(EN) · Xuyao Feng, Antonis Bikakis · 2026-10-15 04:00

从句子级监督中学习命题级链接以用于神经符号NLI

arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis. Before symbolic reasoning, the system must infer semantic links between their pr…

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从句子级监督中学习命题级链接以用于神经符号NLI

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