15 accepted papers
A central challenge in large-scale decision-making under incomplete information is estimating reliable probabilities. Recent approaches leverage Large Language Models (LLMs) to generate explanatory factors and elicit coarse-grained probability estimates. Typically, an LLM performs forward abduction …
Text-to-motion generation aims to synthesize realistic and semantically aligned 3D human motions from natural language descriptions. Existing diffusion-based methods often rely on isotropic latent priors and shallow cross-modal supervision, which lead to semantic entanglement, limited controllabilit
The homogeneity and heterogeneity across modalities are critical factors that influence multimodal fusion. In Multimodal Sentiment Analysis (MSA), the inherent textual information within the audio modality induces cross-modality homogeneity with the text modality. Conversely, the mutual independence
Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer- or Graph Neural Networks (GNNs)-based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal tempo
Recent research reveals that a minority of high-entropy tokens significantly influence the reasoning quality of large language models (LLMs). Inspired by this, we propose Prototype Entropy Alignment (PEA), a reinforcement learning framework that models effective reasoning not as a single path but as
Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this end, we propose AGCL, a novel Aspect Graph Construction and Learning method, aimed…
Pre-trained Language Models (PLMs) have achieved remarkable performance in various Natural Language Processing (NLP) tasks, including Aspect-based Sentiment Analysis (ABSA). Therefore, numerous ABSA models based on PLMs have been proposed, primarily focusing on module design to exploit the inherent…
Retrieval-Augmented Generation (RAG) mitigates the hallucination problem of Large Language Models (LLMs) by incorporating external knowledge. Recursive summarization constructs a hierarchical summary tree by clustering text chunks, integrating information from multiple parts of a document to provide…
Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language Processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of e…
This research proposes the METORIE dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, a novel resource designed to improve the reasoning capabilities of large language models (LLMs), such as LLaMA3 and GLM4, in information extraction (IE)…
Deep neural networks (DNNs) face substantial challenges in Long-Tail Visual Recognition (LTVR) due to the inherent class imbalances in real-world data distributions. The Mixture of Experts (MoE) framework has emerged as a promising approach to addressing these issues. However, in MoE systems, expert
Aspect Sentiment Quad Prediction (ASQP) is the most complex subtask of Aspect-based Sentiment Analysis (ABSA), aiming to predict all sentiment quadruples within the given sentence. Due to the complexity of sentence syntaxes and the diversity of sentiment expressions, generative methods gradually bec…
Long-tailed data poses a significant challenge for deep learning models, which tend to prioritize accurate classification of head classes while largely neglecting tail classes. Existing techniques, such as class re-balancing, logit adjustment, and data augmentation, aim to enlarge decision regions o…
Effective extraction and integration of valuable contextual information is the core of models for the Emotion Recognition in Conversation (ERC) task. However, a significant amount of irrelevant information is inevitably introduced when integrating long-range contextual information, perplexing the mo…
In an emotional conversation, emotions are causally transmitted among communication participants, constituting a fundamental conversational feature that can facilitate the comprehension of intricate changes in emotional states during the conversation and contribute to neutralizing emotional semantic…