DiffIE: Diffusion-based Open Information Extraction

2026-09-02

AI Summary

本文提出DIFFIE，利用条件离散扩散的随机性作为开放信息抽取机制，通过反向扩散轨迹生成候选三元组并进行聚类和排序，以解决多输出任务中的冗余和效率问题。

Abstract

A single sentence often expresses multiple valid relational triplets, which makes Open Information Extraction (OpenIE) fundamentally a multi-output task. Existing neural systems handle this by autoregressive generation, which is flexible but slow and prone to redundancy, or by fixed-slot prediction, which is efficient but couples the extraction budget to training. We introduce DIFFIE which instead treats the stochasticity of conditional discrete diffusion as the extraction mechanism itself: independent reverse-diffusion trajectories over per-token role tags produce a pool of candidate triplets, which are clustered under lenient matching and ranked to form the output. Both the pool size and the number of returned extractions are inference-time choices, decoupling the extraction budget from training and exposing test-time compute as a tunable axis. DIFFIE achieves the new state of the art in CaRB (1-1) both F1 and AUC, and outperforms the strongest rule-based system (ClausIE) in BenchIE; it also remains competitive in standard CaRB and WiRe57 evaluations, giving the best average score among systems that report all four benchmarks. Ablations show that uniform discrete diffusion outperforms absorbing state diffusion in our setting, and that a matched non-diffusion stochastic tagger does not reproduce its gains. Our results indicate that diffusion stochasticity is an effective mechanism for structured prediction tasks with multiple valid outputs.

Problem

• Open Information Extraction
• multi-output task
• autoregressive generation
• fixed-slot prediction
• extraction budget

Innovation

• conditional discrete diffusion
• reverse-diffusion trajectories
• open information extraction
• stochasticity
• structured prediction