Papers by Fang Kong
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors (2025.emnlp-main)
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Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu, Kuofeng Gao, Bin Chen, Shu-Tao Xia, Yaowei Wang, Min Zhang
| Challenge: | Existing methods to detect large language models (LLMs) generated for plagiarism use paraphrases to rewrite them to evade detection. |
| Approach: | They propose a training-free method that effectively fools text detectors using off-the-shelf LLMs by rewriting them to evade detection. |
| Outcome: | The proposed method deceives text detectors using off-the-shelf LLMs by rewriting them to produce human-like sentences that are less discernible by detectors. |
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)
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| Challenge: | Existing dialogue models struggle to interpret context accurately due to irrelevant or misclassified knowledge, limiting their effectiveness in real-world scenarios. |
| Approach: | They propose a framework that dynamically filters relevant commonsense knowledge and extracts personalized information to improve empathetic dialogue generation. |
| Outcome: | The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset. |
Multi-Hop Question Generation via Dual-Perspective Keyword Guidance (2025.findings-acl)
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| Challenge: | Existing work fails to fully utilize the guiding potential of keywords and neglect to differentiate the distinct roles of question-specific and document-specific keywords. |
| Approach: | They propose a dual-perspective keyword-guided framework that integrates question and document keywords into the multi-hop question generation process. |
| Outcome: | The proposed framework integrates question and document keywords into the multi-hop question generation process. |
A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure (2020.acl-main)
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| Challenge: | Text-level discourse parsing of discourse rhetorical structure (DRS) is a fundamental research topic in natural language processing. |
| Approach: | They propose a top-down neural architecture for text-level discourse parsing . they cast the parser as a recursive split point ranking task . |
| Outcome: | The proposed top-down approach is more suitable for text-level discourse parsing. |
Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading (2020.coling-main)
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| Challenge: | Existing methods on discourse parsing in English suffer from long discourse units and fewer explicit connectives. |
| Approach: | They propose to use two reading modes to construct Chinese paragraph level discourse trees. |
| Outcome: | The proposed model outperforms baselines on Chinese discourse trees. |
Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition (2021.acl-short)
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| Challenge: | Existing approaches to Chinese Named Entity Recognition (NER) lack explicit word boundary and tenses information. |
| Approach: | They propose a boundary enhanced approach for Chinese Named Entity Recognition . they add an additional Graph Attention Network(GAT) layer to capture internal dependency of phrases . |
| Outcome: | The proposed approach improves Chinese Named Entity Recognition (NER) on OntoNotes and Weibo corpora. |
Adversarial Learning for Discourse Rhetorical Structure Parsing (2021.acl-long)
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| Challenge: | Existing top-down discourse rhetorical structure parsers make local decisions and ignore global parsing. |
| Approach: | They propose a method to transform gold standard and predicted constituency trees into tree diagrams with two color channels. |
| Outcome: | The proposed method improves performance on RST-DT and CDTB corpora and can leverage global context. |
EDTC: A Corpus for Discourse-Level Topic Chain Parsing (2021.findings-emnlp)
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| Challenge: | Discourse analysis is a fundamental part of natural language processing. |
| Approach: | They propose a discourse-level topic chain parsing system which can be automated . they propose lexical cohesion modeling instead of lexically measuring topic structure . |
| Outcome: | The proposed system is robust and reliable, and can provide high reliability and low confidence scores. |
Retrievals Can Be Detrimental: Unveiling the Backdoor Vulnerability of Retrieval-Augmented Diffusion Models (2026.acl-long)
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| Challenge: | Retrieval-augmented diffusion models (RDMs) have been developed to enhance performance with reduced parameters. |
| Approach: | They propose to integrate retrieval-augmented diffusion models with Retrieval-augmented generation (RAG) that enhances performance with reduced parameters. |
| Outcome: | The proposed framework achieves outstanding attack effects while maintaining benign utility. |
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. |
Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation (2025.findings-emnlp)
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| Challenge: | Existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges. |
| Approach: | They propose a generalizable fine-grained problem enhancement method that augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora. |
| Outcome: | The proposed method improves both automatic and human evaluation metrics across different models. |
A Novel Three-stage Framework for Few-shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing methods for Named Entity Recognition (NER) rely on labeled data, but data scarcity is a major challenge. |
| Approach: | They propose a framework for Few-shot Named Entity Recognition that can learn from limited labeled data and generalize to new domains. |
| Outcome: | The proposed framework surpasses existing methods on several benchmarks. |
Danger Depends on the Mind: A Theory-of-Mind Grounded Dataset and Model for Context-Dependent Dangerous Speech (2026.findings-acl)
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| Challenge: | Existing methods for dangerous speech detection rely on binary labels that ignore who is speaking and in what mental state. |
| Approach: | They propose a context-dependent variant of dangerous speech detection by grounding it in Theory-of-Mind. |
| Outcome: | The proposed model outperforms proprietary and open-source models with significantly fewer parameters. |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.acl-long)
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Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong
| Challenge: | Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data. |
| Approach: | They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process. |
| Outcome: | Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities. |
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)
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| Challenge: | Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives . |
| Approach: | They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. |
| Outcome: | The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus. |
QuASAR: A Question-Driven Structure-Aware Approach for Table-to-Text Generation (2025.acl-long)
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| Challenge: | Existing methods for table-to-text generation fail to capture the structure of tabular data or rely on complex attention mechanisms, limiting their applicability. |
| Approach: | They propose a question-driven self-supervised approach to enhance the model’s structural perception and representation capabilities by focusing on structure-related queries. |
| Outcome: | The proposed model improves its model's structural perception and representation capabilities by guiding it to capture local and global table structures. |
MECH: A Cost-Effective Multi-Task Cascade Framework for Classroom Opinion Evolution Recognition (2026.acl-long)
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| Challenge: | Existing studies focus on dialogue act annotation, overlooking the deeper dimension of opinion evolution. |
| Approach: | They propose a framework for Classroom Opinion Evolution Recognition that translates "Action-Opinion" dualism into a risk-aware routing mechanism. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy of 78.55% while reducing API costs by 44.4%. |
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. |
Discourse Parsing Enhanced by Discourse Dependence Perception (2022.aacl-main)
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| Challenge: | Top-down neural models still suffer from the top-down error propagation issue . previous studies gradually switch from feature-based machine learning methods to deep neural models . |
| Approach: | They propose a top-down framework that learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders. |
| Outcome: | The proposed framework learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders on a Chinese discourse corpus. |