Papers by Yaxin Fan
DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset (2026.findings-acl)
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| Challenge: | Existing studies on multi-party dialogue discourse parsing focus on textual modality and two-party dialog . et al., 2016) focused on text-based discourse parses, ignoring the complexity and richness of multimodal interactions in real-world scenarios. |
| Approach: | They construct the first publicly available English multimodal dataset for multi-party dialogue discourse parsing based on American TV dramas. |
| Outcome: | The proposed dataset contains 495 dialogue segments with 6,374 utterances and 9.1 hours of parallel video content, covering rich multi-party interaction scenarios. |
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs (2025.findings-acl)
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| Challenge: | Existing studies on social interactions neglect hallucination while struggling with poor generalizability and implicit character fidelity judgments. |
| Approach: | They propose a generalizable and explicit paradigm for uncovering interactive patterns of Large Language Models across diverse worldviews by defining interactive hallucination through stance transfer and SHARP, a benchmark built by extracting relations from commonsense knowledge graphs. |
| Outcome: | The proposed paradigm is generalizable and explicit and demonstrates its effectiveness and stability. |
Improving Dialogue Discourse Parsing through Discourse-aware Utterance Clarification (2025.acl-long)
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| Challenge: | Extensive experiments on the STAC and Molweni datasets demonstrate that our approach effectively resolves ambiguities and significantly outperforms the state-of-the-art (SOTA) baselines. |
| Approach: | They propose a Discourse-aware Clarification Module (DCM) that generates clarifications for the parser through systematic clarification type reasoning and discourse goal reasoning. |
| Outcome: | Extensive experiments on the STAC and Molweni datasets demonstrate that the proposed module significantly outperforms the state-of-the-art (SOTA) framework. |
Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction (2025.acl-long)
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| Challenge: | Unlike traditional dialogue systems, goal-oriented proactive dialogue systems focus on achieving specific objectives by actively guiding and anticipating user needs. |
| Approach: | They propose a model-agnostic two-stage Consistency Reflection and Correction framework that allows the model to reflect on discrepancies between generated responses and dialogue contexts and suggest possible corrections. |
| Outcome: | The proposed framework significantly improves the consistency between generated responses and dialogue contexts on three datasets. |
A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing (2022.coling-1)
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| Challenge: | Existing studies have focused on graph-based and transition-based discourse parsing, but no study has investigated the advantages of both paradigms for conversational discourse paring. |
| Approach: | They propose a distance-aware multi-task framework that incorporates the strengths of transition-based paradigms to facilitate conversational discourse parsing. |
| Outcome: | The proposed framework improves the graph-based paradigm on long-distance dependency links. |
Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation (2024.emnlp-main)
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| Challenge: | Existing generation methods on Incomplete Utterance Rewriting (IUR) can generate coherent utterances, but they often include irrelevant and redundant tokens in rewritten utteras . |
| Approach: | They propose a multi-task learning framework that uses editing operation labels to guide generation model to focus on critical tokens in dialogue context. |
| Outcome: | The proposed model outperforms state-of-the-art models on open-domain and task-oriented dialogues on three datasets. |
Improving Multi-party Dialogue Generation via Topic and Rhetorical Coherence (2024.emnlp-main)
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| Challenge: | Existing studies on multi-party dialogue generation focus on the reply-to structure of dialogue histories, but they neglect the coherence between generated responses and target utterances. |
| Approach: | They propose a Reinforcement Learning approach emphasizing Topic and Rhetorical Coherence to enhance the model's perception of coherence with the target utterance. |
| Outcome: | The proposed approach significantly outperforms the state-of-the-art baselines on two popular datasets. |
Enhancing Goal-oriented Proactive Dialogue Systems via Dynamic Multi-dimensional Consistency Optimization (2025.findings-emnlp)
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| Challenge: | Existing work on goal-oriented proactive dialogue systems failed to address the multi-dimensional consistency issue between generated responses and key contextual elements. |
| Approach: | They propose a Dynamic Multi-dimensional Consistency Reinforcement Learning framework which measures the impact of each consistency dimension on overall dialogue quality and provides feedback to improve response quality. |
| Outcome: | The proposed framework significantly improves the consistency of generated responses on two datasets. |
Simulating Dual-Process Thinking in Dialogue Topic Shift Detection (2025.coling-main)
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| Challenge: | Existing methods for topic shift detection focus on shallow local reasoning, overlooking the importance of considering the global historical structure and local details to elucidate the underlying causes of topic shift. |
| Approach: | They propose a dual-process theory for dialogue topic shift detection that employs Large Language Models to extract and store the global topic structure of historical dialogue, while a reasoning module introduces a LLM to generate reasoning samples between the response and the most recent topic of historical dialog. |
| Outcome: | The proposed framework outperforms the state-of-the-art on three public datasets and is based on a dual-process theory. |
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator (2024.acl-long)
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| Challenge: | Recent efforts to democratize ChatGPT have focused on leveraging real user and ChatGPP dialogues, but the most direct human needs are often ignored. |
| Approach: | They propose a method to simulate human behavior better by using real human-like questions extracted from real human conversations as a learning goal and a user simulator called ‘Socratic’. |
| Outcome: | The proposed model achieves SoTA performance among LLaMA-based 7B models in MT-Bench. |
Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition (2023.emnlp-main)
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| Challenge: | Existing approaches to learn dialogue discourse parsing with related tasks require additional annotation, thus limiting their generality. |
| Approach: | They propose a multitasking framework that integrates dialogue discourse parsing with addressee recognition to reflect relation-based structure of dialogue. |
| Outcome: | The proposed framework outperforms baselines on the Molweni and STAC datasets. |
Enhancing Multi-party Dialogue Discourse Parsing with Explanation Generation (2025.coling-main)
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| Challenge: | Multi-party dialogue discourse parsing is an important and challenging task in natural language processing. |
| Approach: | They propose a model to integrate external knowledge from Large Language Models to analyze dialogue discourse structures and semantic relations between utterances in multi-party conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) models on two public datasets. |
Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study (2024.lrec-main)
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| Challenge: | Large language models have shown remarkable capability in many downstream tasks, yet their ability to understand discourse structures of dialogues remains less explored. |
| Approach: | They aim to systematically inspect ChatGPT’s performance in two discourse analysis tasks: topic segmentation and discourse parsing. |
| Outcome: | The proposed model can give more reasonable topic structures than human annotations but only linearly parses the hierarchical rhetorical structures. |
Two-stage Incomplete Utterance Rewriting on Editing Operation (2025.coling-main)
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| Challenge: | Existing methods to generate rewritten utterances based on dialogue context ignore coreference and ellipsis in dialogues. |
| Approach: | They propose a framework where the first stage generates editing operations and the second stage rewrites incomplete utterances utilizing the generated editing operations. |
| Outcome: | The proposed framework outperforms the existing models on three IUR datasets. |
Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation (2021.emnlp-main)
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| Challenge: | Existing methods of implicit discourse relation recognition (IDRR) focus on three aspects: enhancing discourse units representation, enhancing semantic interaction, and joint learning with other tasks. |
| Approach: | They propose a joint model to recognize the relation label and generate the target sentence containing the meaning of relations simultaneously. |
| Outcome: | The proposed model achieves the best performance against several state-of-the-art systems on Chinese and English datasets. |
Non-Emotion-Centric Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy is a social psychology theory that enables individuals to comprehend each other's experiences and emotions, thereby fostering more intimate interpersonal relationships. |
| Approach: | They propose a framework for empathetic dialogue generation based on contrastive learning and context-sensitive entity and social commonsense that punishes responses with incorrect emotions and improves the quality of emotions. |
| Outcome: | The proposed framework improves the quality of empathetic generation and generates more diverse responses in comparison with the state-of-the-art baselines. |