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- Donghong JiSuggested from co-authorship
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Works75 from public data
- 110
- Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax89
A novel unified SRL framework based on the sequence-to-sequence architecture with double enhancement in both the encoder and decoder sides is investigated, and in-depth analysis demonstrates that the model can effectively capture the correlations between syntactic and SRL structures.
- Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks84
An innovative model for discontinuous NER based on pointer networks, where the pointer simultaneously decides whether a token at each decoding frame constitutes an entity mention and where the next constituent token is, which is the kernel source to NER.
- Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition76
This work argues that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement and fusion steps of MM-ERC, and proposes a Contribution-aware Fusion Mechanism and a Context Refusion Mechanism for multimodal and context integration, respectively.
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- OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction49
The model is equipped with an adaptive event fusion module to generate event-aware representations and a distance-aware predictor to integrate relative distance information for word-word relation recognition, which are empirically demonstrated to be effective mechanisms.
- End-to-end Semantic Role Labeling with Neural Transition-based Model45
This paper presents the first work of transition-based neural models for end-to-end SRL, and suggests high-order compositions to extract non-local features, which can enhance the proposed transition model further.
- 44
- Fine-grained depression analysis based on Chinese micro-blog reviews43
A neural model for joint depression degree and cause prediction is designed and compared with several widely-used neural models such as TextCNN, BiLSTM and BERT, which shows that neural models achieve promising results, but there is still room for improvement.
- Latent Target-Opinion as Prior for Document-Level Sentiment Classification: A Variational Approach from Fine-Grained Perspective37
This paper proposes to first learn the latent target-opinion distribution behind the documents, and then leverage such fine-grained prior knowledge into the classification process, using a hierarchical model based on Transformer encoders.
- PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis36
A multimodal conversational ABSA is introduced, where two novel subtasks are proposed: 1) Panoptic Sentiment Sextuple Extraction and 2) Sentiment Flipping Analysis, detecting the dynamic sentiment transformation throughout the conversation with the causal reasons.
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- Multimodal Emotion-Cause Pair Extraction with Holistic Interaction and Label Constraint32
This work proposes a novel model that captures holistic interaction and label constraint (HiLo) features for the MECPE task and innovatively leverages emotion transition features as pivotal cues to enhance causal inference within conversations.
- Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling31
A novel holistic spatio-temporal scene graph (namely HostSG) representation based on the existing dynamic scene graph structures, which well model both the fine-grained spatial semantics and temporal dynamics of videos for VidSRL is explored.
- Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery27
This work innovatively employs uncertainty propagation to select data samples from high-uncertainty regions, which are then labeled using LLMs through a comparison-based prompting scheme, which enhances accuracy in identifying new categories and introduces a soft feedback propagation mechanism to minimize the spread of inaccurate feedback.
- Harnessing Holistic Discourse Features and Triadic Interaction for Sentiment Quadruple Extraction in Dialogues27
A novel framework that not only capitalizes on comprehensive discourse feature modeling, but also captures the intrinsic interaction for optimal quadruple extraction is introduced, and a novel triadic scorer is proposed, strengthening weak token relations within a quadruple, thereby enhancing the cohesion of the quadruple extraction.
- NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal Emotion-Cause Analysis in Conversations25
The architecture of the system developed for participation in Task 3 of SemEval-2024: Multimodal Emotion-Cause Analysis in Conversations is described, which designs an approach of emotion-cause-aware instruction-tuning for LLMs, to enhance the perception of the emotions with their corresponding causal rationales.
- Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis17
A simple yet effective framework to address shortages in ABSA data augmentation, called Refining and Synthesis Data Augmentation (RSDA), which refines generated labeled data using a natu-ral language inference (NLI) filter to control data quality.
- TKDP: Threefold Knowledge-Enriched Deep Prompt Tuning for Few-Shot Named Entity Recognition17
This work proposes incorporating the deep prompt tuning framework with threefold knowledge (namely TKDP), including the internal 1) context knowledge and the external 2) label knowledge & 3) sememe knowledge, and presents a knowledge-enriched model that significantly outperforms 9 strong-performing baseline systems in 5-/10-/20-shot settings.
- Electronic word-of-mouth effects on studio performance leveraging attention-based model17
This study proves that deep learning with the attention mechanism has the best accuracy in both eWOM and stock price movement and presents both a hierarchical two-layer attention network and hierarchical convoluted attention network (HCAN), which quantify the importance of crucial eWom features in capturing valuable information from audience members’ reviews.
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- Unveiling the Cognitive Compass: Theory-of-Mind-Guided Multimodal Emotion Reasoning12
This work introduces HitEmotion, a ToM-grounded hierarchical benchmark that diagnoses capability breakpoints across increasing levels of cognitive depth, and proposes a ToM-guided reasoning chain that tracks mental states and calibrates cross-modal evidence to achieve faithful emotional reasoning.
- MMLSCU: A Dataset for Multi-modal Multi-domain Live Streaming Comment Understanding12
This work introduces the MMLSCU dataset, containing 50,129 intention-annotated comments across multiple modalities (text, images, vi-deos, audio) from eight streaming domains, and implements an end-to-end model to sequentially perform the following tasks: viewer comment intent detection, intent cause mining, viewer comment explanation and streamer policy suggestion.
- FormFactory: An Interactive Benchmarking Suite for Multimodal Form-Filling Agents11
A comprehensive evaluation of state-of-the-art MLLMs is conducted and it is observed that no model surpasses 5% accuracy, underscoring the inherent difficulty of the task and revealing significant limitations in current models' visual layout reasoning and field-value alignment abilities.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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