Papers by Lixing Zhu
NarrativePlay: Interactive Narrative Understanding (2024.eacl-demo)
Copied to clipboard
| Challenge: | Existing systems for interactive agents focus on specific capabilities in predetermined scenarios. |
| Approach: | They propose a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment. |
| Outcome: | The proposed system generates human-like responses guided by personality traits extracted from narratives. |
LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods focus on supervised fine-tuning or limited to one-off prediction, which poses a challenge where the context is long. |
| Approach: | They propose a dynamic approach to CoREFerence resolution in chunked long narratives by deploying dual Large Language Models. |
| Outcome: | The proposed model achieves performance gains over existing models and fine-tuning approaches on long narrative datasets, significantly reducing the resources required for inference and training. |
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)
Copied to clipboard
| Challenge: | Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states. |
| Approach: | They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection. |
| Outcome: | The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show . |
Dissecting Failure Dynamics in Large Language Model Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood. |
| Approach: | They propose a framework that probes and redirects critical transitions using uncertainty signals. |
| Outcome: | Empirical evaluations show that GUARD improves reasoning performance . GUard probes critical transitions and redirects them using uncertainty signals . |
OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing N-ToM benchmarks lack ambiguous and artificial narratives, lack of personality traits and preferences, and limited diversity in the questions posed. |
| Approach: | They propose a benchmark to assess Neural Theory-of-Mind (N-ToM) with longer and clearer narrative stories, characters with explicit personality traits, actions triggered by character intentions, and questions designed to challenge LLMs’ abilities of modeling characters’ mental states. |
| Outcome: | The proposed test aims to assess the performance of LLMs in the physical and psychological worlds. |
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to detect vaccine attitudes on social media require abundant annotations and pre-defined aspect categories. |
| Approach: | They propose a semi-supervised approach to detect vaccine attitudes on social media . they use an autoencoding architecture to learn from unlabelled data the topical information of the domain . |
| Outcome: | The proposed model outperforms existing aspect-based models on stance detection and tweet clustering. |
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)
Copied to clipboard
| Challenge: | Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
| Approach: | They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings. |
| Outcome: | The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
Disentangling Aspect and Stance via a Siamese Autoencoder for Aspect Clustering of Vaccination Opinions (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to mining public opinions about vaccines from social media make direct usage of supervision information constraining the models to predefined aspect classes while still not distinguishing those aspects from users’ stances. |
| Approach: | They propose a model for vaccination opinion mining from social media that disentangles users’ stances from opinions via a disentangling attention mechanism and a Swapping-Autoencoder. |
| Outcome: | The proposed model outperforms existing methods on aspect-based opinion mining. |
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies have used a manual segmentation of a tweet sequence into equallyspaced intervals based on either tweet counts or time duration. |
| Approach: | They propose to model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. |
| Outcome: | The proposed model predicts the posting time and the stance labels of future tweets more accurately compared to baselines. |
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) excel in generating coherent texts, but their ability to comprehend the author’s thoughts remains uncertain. |
| Approach: | They conduct a comprehensive survey of narrative understanding tasks, examining their key features, definitions, taxonomy, associated datasets, evaluation metrics, and limitations. |
| Outcome: | The proposed framework could be extended to address novel narrative understanding tasks. |
PANACEA: An Automated Misinformation Detection System on COVID-19 (2023.eacl-demo)
Copied to clipboard
Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He
| Challenge: | Using social media and fact-checking to detect misinformation is not enough to prevent the spread of false information. |
| Approach: | They propose a web-based misinformation detection system PANACEA which has two modules, fact-checking and rumour detection. |
| Outcome: | The system outperforms state-of-the-art methods and adapts graph convolutional networks model to detect rumours based on tweets rather than knowledge bases. |