MAQInstruct: Instruction-based Unified Event Relation Extraction
This work addresses event relation extraction for natural language processing applications, offering an incremental improvement over existing instruction-based methods.
The paper tackled the challenge of extracting event relations that deviate from known schemas by proposing MAQInstruct, an improved instruction-based framework that reduces inference samples and dependency on generation sequences, resulting in significant performance improvements across multiple large language models.
Extracting event relations that deviate from known schemas has proven challenging for previous methods based on multi-class classification, MASK prediction, or prototype matching. Recent advancements in large language models have shown impressive performance through instruction tuning. Nevertheless, in the task of event relation extraction, instruction-based methods face several challenges: there are a vast number of inference samples, and the relations between events are non-sequential. To tackle these challenges, we present an improved instruction-based event relation extraction framework named MAQInstruct. Firstly, we transform the task from extracting event relations using given event-event instructions to selecting events using given event-relation instructions, which reduces the number of samples required for inference. Then, by incorporating a bipartite matching loss, we reduce the dependency of the instruction-based method on the generation sequence. Our experimental results demonstrate that MAQInstruct significantly improves the performance of event relation extraction across multiple LLMs.