Papers by Wenji Mao
Dynamic Routing Transformer Network for Multimodal Sarcasm Detection (2023.acl-long)
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| Challenge: | Existing methods for multimodal sarcasm detection rely on fixed architectures to capture cross-modal incongruity. |
| Approach: | They propose a method that uses dynamic paths to activate different routing transformer modules with hierarchical co-attention adapting to cross-modal incongruity. |
| Outcome: | The proposed method is compared to state-of-the-art methods on a public dataset. |
Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework (2023.emnlp-main)
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| Challenge: | Existing studies on stance detection were conducted mainly in English due to the low-resource problem in most non-English languages. |
| Approach: | They propose to use a cross-lingual teacher and a teacher to transfer knowledge from source to target language to bridge the discrepancy between languages. |
| Outcome: | The proposed framework bridges the discrepancy between languages and generalizes the knowledge to unseen targets in target language. |
ImaRA: An Imaginative Frame Augmented Method for Low-Resource Multimodal Metaphor Detection and Explanation (2025.findings-naacl)
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| Challenge: | Existing methods for multimodal metaphor detection neglect cross-domain and attribute similarity characteristics underlying multimodal understanding. |
| Approach: | They propose an Imaginative FRame Augmented method for multimodal metaphor detection and explanation . they use a cross-modal imagination dataset rich in multimodal multimodal expressions . |
| Outcome: | The proposed method outperforms existing methods with training data on two datasets. |
Modeling Conceptual Attribute Likeness and Domain Inconsistency for Metaphor Detection (2023.emnlp-main)
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| Challenge: | Metaphor detection aims to distinguish between metaphorical and literal expressions in text. |
| Approach: | They propose an attribute likeness and domain inconsistency learning framework for wordpair metaphor detection based on conceptual metaphor theory . they model attribute likeity with an attribute siamese network and devise a domain contrastive learning strategy to learn semantic inconsistentness of concepts in source and target domains . |
| Outcome: | The proposed framework outperforms existing word-pair and token-level methods on four datasets. |
One Unified Model for Diverse Tasks: Emotion Cause Analysis via Self-Promote Cognitive Structure Modeling (2025.naacl-long)
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| Challenge: | Existing models for emotion cause analysis overlook common ground rooted in cognitive emotion theories, in particular, the cognitive structure of emotions. |
| Approach: | They propose a unified model capable of tackling diverse emotion cause analysis tasks . they propose 'self-promote mechanism' that constructs the emotion cognitive structure through LLM . |
| Outcome: | The proposed model outperforms existing models and baselines on multiple emotion cause analysis tasks. |
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (2020.acl-main)
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| Challenge: | Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. |
| Approach: | They propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. |
| Outcome: | The proposed method outperforms existing methods in the extraction of emotion-cause pairs . it emphasizes inter-clause modeling to perform end-to-end extraction . |
Generative Adversarial Training with Perturbed Token Detection for Model Robustness (2023.emnlp-main)
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| Challenge: | Existing adversarial training methods use discrete tokens to deceive models . current approaches use embeddings, whereas actual text-based training uses discrete text tokens. |
| Approach: | They propose a framework that integrates gradient-based learning, adversarial example generation and perturbed token detection to enhance adversariarial robustness. |
| Outcome: | The proposed framework surpasses the state-of-the-art results of ChatGPT by 10% in average accuracy. |
PromISe: Releasing the Capabilities of LLMs with Prompt Introspective Search (2024.lrec-main)
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| Challenge: | Existing evaluation benchmarks for large language models use uniform manual prompts, resulting in underestimation of performance. |
| Approach: | They propose a prompt introspective search framework that integrates self-introspect and self-refine to unlock the capabilities of LLMs. |
| Outcome: | The proposed framework significantly boosts the performance of 12 well-known LLMs compared to baseline methods. |
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)
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| Challenge: | Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction. |
| Approach: | They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning. |
| Outcome: | The proposed agent performs well in both dialogue element modeling and out-of-domain tasks. |
Perspective-driven Preference Optimization with Entropy Maximization for Diverse Argument Generation (2025.findings-emnlp)
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| Challenge: | Argument generation with diverse perspectives is essential for fostering balanced discourse and mitigating bias. |
| Approach: | They propose a Perspective-aware Preference Optimization with Entropy Maximization framework for diverse argument generation. |
| Outcome: | The proposed framework enhances perspective diversity through preference optimization based on the constructed preference dataset . |
A Theory Guided Scaffolding Instruction Framework for LLM-Enabled Metaphor Reasoning (2024.naacl-long)
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| Challenge: | Existing methods for metaphor detection and reasoning struggle to explain the underlying reasoning process behind the metaphorical/literal judgment. |
| Approach: | They propose a Theory guided Scaffolding Instruction framework that instructs an LLM to infer the underlying reasoning process of metaphor detection guided by metaphor theories for the first time. |
| Outcome: | The proposed method significantly outperforms both the LLM-based reasoning methods and the SOTA methods in metaphor detection. |
TARA: Token-level Attribute Relation Adaptation for Multi-Attribute Controllable Text Generation (2024.findings-emnlp)
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| Challenge: | Existing work on multi-attribute controllable text generation ignores interrelations of attributes . recent work defines attribute relations as promotive, but not fixed . |
| Approach: | They propose a method that explicitly defines attribute relations as inhibtory for multi-attribute CTG . they propose 'tara' which employs token-level attribute relation adaptation and representation to generate text with the balanced multi-attribut . |
| Outcome: | The proposed method generates text with the balanced multi-attribute control. |
Target-Oriented Relation Alignment for Cross-Lingual Stance Detection (2023.findings-acl)
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| Challenge: | Existing work on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detector in low-resource languages. |
| Approach: | They propose a fine-grained method which considers both target-level associations and language-level alignments to learn the in-language and cross-language associations. |
| Outcome: | The proposed method is compared with competing methods under variant settings and shows that it performs better in low-resource languages. |
Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining (2024.acl-long)
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| Challenge: | Existing methods for word-pair metaphor detection provide intermediate explainable clues for detection results. |
| Approach: | They propose a method to bridge word-pair and token-level metaphor detection by modeling word pairs as explainable intermediate information. |
| Outcome: | The proposed method bridges word-pair and token-level metaphor detection by using word pairs . it provides intermediate explainable clues for the detection results, but this is a challenge . |
An LLM-Enabled Knowledge Elicitation and Retrieval Framework for Zero-Shot Cross-Lingual Stance Identification (2024.findings-emnlp)
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| Challenge: | Existing research is conducted in monolingual setting on English datasets, whereas in other low-resource languages, it lacks sufficient data for training quality stance detection models. |
| Approach: | They propose a knowledge elicitation and retrieval framework that leverages the capability of large language models for stance knowledge acquisition and matches the target language input to the most relevant stance information. |
| Outcome: | The proposed framework improves on multilingual datasets and competitive baselines. |
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity (D19-1)
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| Challenge: | Existing methods to verify rumors are needed to identify false rumors. |
| Approach: | They propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter that exploits the temporal dynamics of stance evolution. |
| Outcome: | The proposed framework outperforms previous methods on two benchmark datasets showing that it can predict rumor stance and veracity. |
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association (2020.acl-main)
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| Challenge: | Existing methods for sarcasm detection rely on text data, but are insufficient to detect multimodal sarcasm. |
| Approach: | They propose a method for modeling cross-modality contrast in the associated context by constructing the Decomposition and Relation Network. |
| Outcome: | The proposed model can detect sarcasm in multimodal tweets using a dataset . |
Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026.acl-long)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) is effective for closed-ended tasks, but it is not applicable to open-ended social language games. |
| Approach: | They propose a method that uses a time-scaled evolutionary perception mechanism to detect impasse by quantifying dual-scale value baseline divergence alongside match entropy. |
| Outcome: | Experiments on multiple social language games show that the proposed method outperforms baselines and avoids policy degeneration. |