TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis
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| Authors | Xinran Li et al. |
| Year | 2026 |
| HF Upvotes | 5 |
| arXiv | 2605.01717 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) needs to capture the complex interrelationships in multiple rounds of dialogues. Existing methods usually employ simple Graph Convolutional Networks (GCN), which introduce structural noise and fail to consider the temporal sequence of the dialogues, or use standard RoPE, which implicitly captures relative distances in a flat sequence but cannot clearly separate the token-level syntactic order from the utterance-level progression, and may suffer from the Distance Dilution problem. To address these issues, we propose a new framework that combines Thread-Constrained Directed Acyclic Graph (TC-DAG) and Discourse-Aware Rotary Position Embedding (D-RoPE). Specifically, TC-DAG filters out cross-thread noise based on thread constraints, maintains global connectivity through root anchoring, and incorporates the temporal sequence of the dialogues. D-RoPE aligns multi-layer semantics using dual-stream projection and multi-scale frequency signals, captures thread dependencies using tree-like distances, and alleviates the token-level Distance Dilution problem by incorporating utterance-level progressions. Experimental results on two benchmark datasets demonstrate that our framework achieves state-of-the-art performance.
Engineering Breakdown
Plain English
This paper tackles conversational sentiment analysis where you need to extract sentiment quadruples (target, aspect, sentiment, opinion) from multi-turn dialogues. The authors found that existing approaches using standard Graph Convolutional Networks introduce noise by treating all connections equally, and standard positional embeddings can't distinguish between word-level order within a single turn versus turn-level progression across the conversation. They propose TC-DAG (a filtered graph structure that respects conversation threading) and D-RoPE (positional embeddings aware of discourse structure) to fix these problems.
Key Engineering Insight
The critical insight is that conversations have hierarchical structure—tokens within an utterance should be treated differently from relationships between utterances—and standard ML architectures that flatten this distinction lose important signal. By explicitly constraining the graph to conversation threads and designing position embeddings that understand discourse levels, you get clearer representations without the structural noise.
Why It Matters for Engineers
If you're building production chatbots, customer service systems, or sentiment monitoring tools that analyze multi-turn conversations, this matters because getting sentiment wrong costs money and damages user experience. The 'Distance Dilution' problem they identify is real: when your positional encoding can't tell if two tokens are 2 words apart or 2 turns apart, your model makes worse predictions. This paper gives engineers a concrete architectural fix that's more efficient than standard GCNs while being more semantically correct.
Research Context
Prior work on aspect-based sentiment analysis (ABSA) mostly handled single sentences, and conversational extensions usually just stacked standard GCNs on dialogue graphs without considering threading—treating it like an undifferentiated graph problem. This paper advances the field by recognizing conversations are DAGs with temporal structure, not unstructured graphs. It enables more accurate multi-turn dialogue understanding, which is foundational for any system that needs to track sentiment, sarcasm, or opinion shifts across extended conversations.
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