Papers with dialogue coherence
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation (2021.emnlp-main)
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| Challenge: | Neural Chat Translation (NCT) models that use dialogue characteristics of chat are often incoherent and speakerirrelevant. |
| Approach: | They propose to introduce the modeling of dialogue characteristics into the NCT model by capturing the inherent dialogue characteristics. |
| Outcome: | The proposed model can translate conversational text between speakers of different languages. |
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems (2020.emnlp-main)
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| Challenge: | Existing evaluation metrics only consider surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows. |
| Approach: | They propose a graph-enhanced evaluation metric GRADE to evaluate dialogue coherence . GRADE incorporates utterance-level contextualized representations and fine-grained topic-level graph representations to improve communication logic. |
| Outcome: | The proposed evaluation metric outperforms state-of-the-art metrics on measuring diverse dialogue models in terms of Pearson and Spearman correlations with human judgments. |
Coherence of Argumentative Dialogue Snippets: A New Method for Large Scale Evaluation with an Application to Inference Anchoring Theory (2025.findings-emnlp)
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| Challenge: | illocutionary acts and propositional relations impact dialogue coherence, whereas propositional acts do not. |
| Approach: | They propose a method for testing the components of theories of dialogue coherence through utterance substitution and apply it to Inference Anchoring Theory (IAT) |
| Outcome: | The proposed method is applied to 933 dialogue snippets and 87 annotators. |
MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training (2026.acl-long)
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Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, Chandan K. Reddy
| Challenge: | Existing systems for multi-turn Text-to-SQL are limited to a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. |
| Approach: | They propose to train an agentic training framework for long-horizon multi-turn Text-to-SQL that uses a Markov Decision Process to generate a query per turn without execution, explicit verification, and refinement. |
| Outcome: | Experiments on CoSQL and SParC show that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. |