Learning Complementary Action Modeling from Automotive Maintenance Instructions
For NLP researchers and automotive domain experts, this work addresses a novel task of modeling procedural complementarity in instructions, but the contribution is incremental as it focuses on a specific domain and uses existing methods.
The paper introduces Complementary Action Modeling (CAM), a task to identify or generate procedural counterparts of automotive maintenance instructions by modifying the action phrase while preserving context. Results show that such instructions are best modeled as procedural associations based on subtle lexical cues, not as ordinary sentence similarity or paraphrasing.
A minute lexical variation can reverse the procedural meaning of an instruction even when the rest of the sentence remains unchanged. In automotive maintenance instructions, this pattern often appears when an action phrase turns an instruction into its procedural counterpart. The entities, modifiers, and surrounding context remain largely invariant, while the action phrase determines the procedural relation. We define this task as Complementary Action Modeling (CAM). Given a maintenance instruction, the goal is to identify or generate its procedural counterpart by modifying the action phrase while preserving the remaining sentence context. This task focuses on three aspects: distinguishing complementarity from surface similarity, controlling generation at the action-phrase level, and evaluating relational correctness using retrieval, overlap-based, and human evaluation. Using a German automotive maintenance dataset, we examine these questions through candidate matching and controlled Seq2Seq generation. The results show that complementary maintenance instructions are best modeled as procedural associations grounded in subtle lexical cues. They should therefore not be treated as ordinary cases of sentence similarity or synonym-based paraphrasing.