Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction

2026-09-06

AI Summary

该研究针对低秩瓶颈限制事件交互信号传递的问题，提出了一种卷积瓶颈交互架构CBI，通过轻量级深度可分离卷积和元素级乘法增强事件表示和交互。

Abstract

Temporal relation extraction determines whether an event occurs before, after, or simultaneously with another event, and therefore relies on accurately modeling how the two events interact. Mainstream systems achieve this by concatenating event spans or using shallow fusion, which works well when all model parameters are trainable. However, in parameter-efficient fine-tuning, low-rank bottlenecks restrict information flow and prevent these interaction signals from passing through, leading to clear performance drops. To address this limitation, we propose a theoretically grounded architecture, Convolutional Bottleneck Interaction (CBI), which first applies lightweight depthwise convolution to enhance event representations and then uses element-wise multiplication to capture effective event-event interactions inside the bottleneck. Across five datasets and seven backbone models in the Adapter and LoRA settings, CBI provides consistent and substantial gains, up to +31.7 micro F1, while adding minimal computational cost, showing that explicit interaction inside low-rank spaces is crucial for temporal relation extraction. The code is available at https://github.com/VRCMF/CIF.git.

Problem

• low-rank bottlenecks
• temporal relation extraction
• parameter-efficient fine-tuning

Innovation

• Convolutional Bottleneck Interaction
• Temporal Relation Extraction
• Parameter-Efficient Fine-Tuning
• Low-Rank Bottlenecks