ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)

The development of dialogue systems has transitioned from rule-based frameworks to data-driven deep learning models, notably with the advent of transformers like BERT and GPT. Prior works such as Braslavski et al. (2017) and Rao et al. (2018) explored clarification questions within community Q&A platforms, focusing on question characteristics and ranking. Recent IR research introduced datasets like Qulac and MIMICS, emphasizing open-domain information retrieval and clarification in keyword queries. Despite these advances, challenges remain in multi-turn, open-domain dialogues where ambiguity is common, and systems lack proactive clarification capabilities. Existing models primarily respond reactively, often failing to generate contextually appropriate clarifications. This gap motivates the current research, which aims to develop models that can both identify ambiguous requests and generate relevant clarification questions, thereby improving dialogue comprehension and user satisfaction.

The core problem addressed is enabling dialogue systems to detect when a user request is ambiguous and to generate suitable clarification questions that facilitate disambiguation. Existing methods often rely on simple heuristics or static templates, which are insufficient for complex, nuanced language. The challenge involves understanding contextual cues, ranking candidate questions by relevance, and generating diverse, natural questions in real-time. This problem is compounded by the need to balance clarification frequency with user experience, ensuring that systems ask questions only when necessary without causing frustration. Effectively solving this problem requires models that can comprehend multi-turn context, handle linguistic ambiguity, and produce high-quality questions aligned with user intent.

The main innovation is the integration of BERT-based semantic encoding with GPT-2-based question generation within a multi-task learning framework. This allows simultaneous optimization of question relevance and diversity, addressing both identification and generation challenges. The multi-round training process leverages IR metrics like MRR and P@1 to refine ranking, while generation modules produce contextually appropriate questions. Unlike prior isolated approaches, this joint architecture enhances the system’s ability to understand complex requests and generate natural clarifications. Additionally, the evaluation framework combines retrieval and question-answering metrics, providing a comprehensive assessment of model performance. This holistic approach represents a significant step forward in open-domain clarification in dialogue systems.

• �� Input: User request text. • Semantic encoding: BERT extracts contextual features. • Clarification generation: GPT-2 produces multiple candidate questions. • Relevance ranking: Multi-task model combines question-answer matching and ranking objectives. • Training: Uses multi-round optimization with metrics like MRR, P@1, and nDCG. • Evaluation: On ClariQ dataset, assesses relevance, diversity, and contextual understanding. • Feedback loop: Iterative training with user interaction data to refine models.

The experiments utilized the ClariQ dataset, comprising 237 topics and over 3,000 question-answer pairs. Data split into 70% training and 30% validation. Baselines included traditional retrieval models and single-task neural models. The primary metrics were MRR, P@1, and nDCG, complemented by question relevance scores. Hyperparameters such as learning rate (2e-5), batch size (32), and optimizer (Adam) were tuned. Ablation studies demonstrated the importance of multi-task training and context modeling. Cross-category performance was analyzed, confirming robustness across navigational, informational, and ambiguous requests. Results consistently showed the proposed model outperformed baselines, validating the architecture’s effectiveness.

The integrated model achieved an MRR of 0.45 and P@1 of 0.62, outperforming baselines by over 20%. Question relevance accuracy reached 78%, with notable improvements in complex requests. Ablation results confirmed that context understanding and multi-task training contributed significantly to performance gains. The model maintained high relevance and diversity across different request types, indicating strong generalization. These results demonstrate that combining retrieval and generation within a multi-task framework effectively enhances clarification capabilities in open-domain dialogues.

This approach can be directly applied in virtual assistants, customer support chatbots, and search engines to proactively clarify ambiguous user requests, improving response accuracy and user satisfaction. It requires integration with existing dialogue management systems and domain-specific fine-tuning. The methodology supports multi-turn interactions, enabling more natural and efficient conversations. Long-term, such models could evolve into fully autonomous conversational agents capable of handling complex, multi-modal dialogues across various industries, including healthcare, finance, and e-commerce.

The model's performance declines in cases of extreme ambiguity or highly context-dependent requests, indicating a need for more sophisticated contextual understanding. Large annotated datasets are required for training, which can be costly and limit quick adaptation to new domains. Real-time inference remains computationally intensive, posing challenges for deployment in latency-sensitive applications. Future work should focus on optimizing model efficiency, enhancing domain transferability, and incorporating multimodal signals to address these limitations.