Papers by Yanran Chen
Focus-Constrained Attention Mechanism for CVAE-based Response Generation (2020.findings-emnlp)
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| Challenge: | Existing models generate high-frequency but trivial responses such as "I don't know" or "I'm ok" due to the discrepancy in discourse-level information, standard models generate one-to-many relationships. |
| Approach: | They propose to transform coarse-grained discourse-level information into fine-grounded word-level knowledge by introducing a fine-grain focus signal and a focus-constrained attention mechanism to take full advantage of focus. |
| Outcome: | The proposed model can generate more diverse and informative responses compared with state-of-the-art models. |
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)
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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li
| Challenge: | Existing fact-checking methods that use large language models often generate subtle factual errors. |
| Approach: | They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation. |
| Outcome: | GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call. |
C3KG: A Chinese Commonsense Conversation Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing commonsense knowledge bases organize tuples in an isolated manner, causing problems for chatbots . |
| Approach: | They create a Chinese commonsense conversation knowledge graph which integrates social commonsensm and dialog flow information. |
| Outcome: | The proposed graph incorporates social commonsense knowledge and dialog flow information. |
Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection (2026.eacl-long)
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| Challenge: | Existing computational approaches focus on logical structures of fallacies and argumentation schemes, ignoring the emotional dimension of argumentation. |
| Approach: | They propose to use large language models to systematically change emotional appeals in fallacious arguments by using a computational approach. |
| Outcome: | The proposed method reduces fallacy detection by 14.5% on average on human arguments with enjoyment over fear or sadness. |
Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checks (2025.findings-acl)
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| Challenge: | Emotions have been shown to play a role in argument convincingness, yet this aspect is underexplored in the natural language processing community. |
| Approach: | They propose a framework that examines the extent to which perceived emotional intensity influences perceived convincingness. |
| Outcome: | The proposed framework examines whether emotions influence persuasiveness in humans . it finds that emotions enhance rather than weaken convincingness in human judgments . |
ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA (2025.emnlp-main)
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Zhao Xinjie, Fan Gao, Xingyu Song, Yingjian Chen, Rui Yang, Yanran Fu, Yuyang Wang, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Multi-hop question answering (QA) is a central challenge in natural language processing . early mistakes can cause errors and undermine the final result, authors say . |
| Approach: | They propose a reversible multi-agent reasoning framework that backtracks to earlier valid states when conflicts arise. |
| Outcome: | Empirical evaluation shows that the framework improves on forward-only benchmarks by 6% . the approach enables agents to backtrack to valid states when conflicts arise . |
Evaluating Diversity in Automatic Poetry Generation (2024.emnlp-main)
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| Challenge: | Existing models for creative text generation are not evaluated regarding how different generated poems are from existing training sets. |
| Approach: | They evaluate the diversity of automatically generated poetry by comparing distributions of generated poetry to distributions in human poetry along structural, lexical, semantic and stylistic dimensions. |
| Outcome: | The proposed model types show that style-conditioning and character-level modeling increases diversity across virtually all dimensions. |
Argument Summarization and its Evaluation in the Era of Large Language Models (2025.emnlp-main)
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Moritz Altemeyer, Steffen Eger, Johannes Daxenberger, Yanran Chen, Tim Altendorf, Philipp Cimiano, Benjamin Schiller
| Challenge: | Large Language Models (LLMs) have revolutionized various Natural Language Generation tasks, including Argument Summarization (ArgSum). |
| Approach: | They propose a prompt-based evaluation scheme and validate it through a human benchmark dataset. |
| Outcome: | The proposed evaluation scheme outperforms existing methods and is validated by a human benchmark dataset. |
Reproducibility Issues for BERT-based Evaluation Metrics (2022.emnlp-main)
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| Challenge: | Reproducibility is of utmost concern in machine learning and natural language processing . lexical-overlap metrics are still the dominant metric in natural language generation . |
| Approach: | They ask whether results and claims from four recent BERT-based evaluation metrics can be reproduced. |
| Outcome: | The proposed metrics outperform the dominant metric, BLEU, and show that they can be reproduced. |
MENLI: Robust Evaluation Metrics from Natural Language Inference (2023.tacl-1)
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| Challenge: | Recent proposed BERT-based evaluation metrics for text generation are vulnerable to adversarial attacks, e.g., relating to information correctness. |
| Approach: | They propose to use BERT-based evaluation metrics for text generation to evaluate text for semantic similarity but are vulnerable to adversarial attacks using Natural Language Inference. |
| Outcome: | The proposed metrics outperform existing summarization metrics but perform below SOTA MT metrics on standard benchmarks. |