Abstract Summary
Prior research on fine-grained complaint analysis haslargely focused on short, context-limited inputs such astweets or product reviews. In this work, we advance thefield by introducing a paradigm that leverages multi-turncustomer-support dialogues as a richer medium for capturingnuanced dissatisfaction. Such conversations provide dynamicsignals°™emotional shifts, iterative follow-ups, anddetailed issue descriptions°™that enable more accuratedetection of aspect categories (e.g., service quality,software issues) and severity levels (e.g., disapproval,accusation). To this end, we extend a publicly availablecustomer-support corpus with fine-grained aspect andseverity annotations in the cellular services domain.Building on this dataset, we propose CompSense, amulti-task Mixture-of-Experts (MoE) framework underpinnedby Large Language Models (LLMs) and enriched withcommonsense-aware contextualization for robust complaintunderstanding. Extensive evaluations show that CompSenseconsistently outperforms task-specific conversational LLMsand decoder-only causal LLM baselines, highlighting thevalue of bidirectional and commonsense-aware modeling. Thiswork marks a step toward practical, real-world systemscapable of sophisticated conversational complaint analysis.
