Task-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis

• URL: http://arxiv.org/abs/2605.20916v1
• Date: Wed, 20 May 2026 08:59:32 GMT
• Title: Task-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis
• Abstract summary: Implicit sentiment analysis is challenging because sentiment toward an aspect is often inferred from events rather than expressed through explicit opinion words.<n>Motivated by cognitive appraisal theory, we propose an appraisal-aware multi-task learning framework for implicit sentiment analysis.<n>We use a task-conditioned router to select sparse expert mixtures for each task, and a task-separated routing objective to encourage different tasks to learn distinct expert-selection patterns.
• Score: 3.605122187208041
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Implicit sentiment analysis is challenging because sentiment toward an aspect is often inferred from events rather than expressed through explicit opinion words. Existing models typically learn from the final polarity label, which provides limited guidance for reasoning about sentiment from the context. Motivated by cognitive appraisal theory, we propose an appraisal-aware multi-task learning (MTL) framework for implicit sentiment analysis that provides polarity prediction with two complementary auxiliary tasks: implicit sentiment detection and cognitive rationale generation. However, training several objectives with different targets and sharing a single backbone across tasks in MTL limits flexibility and can lead to task interference. To reduce interference among these related but distinct objectives, we adopt task-level mixture-of-experts models in which all tasks share a common set of experts, and task identity controls the sparse combination of these experts. Our method builds on an encoder-decoder architecture and replaces a subset of encoder and decoder blocks with these sparse mixtures. We use a task-conditioned router to select sparse expert mixtures for each task, and a task-separated routing objective to encourage different tasks to learn distinct expert-selection patterns. Experimental results show that our model outperforms recently proposed approaches, with strong gains on the implicit sentiment subset. Our code is available at https://github.com/yaping166/TRMoE-ISA.

Related papers

• MoTE: Mixture of Task Experts for Multi-Task Video Understanding [15.06823492518845]
  We propose a decoder architecture that converts large language model feed-forward networks into task-specific experts while keeping the multimodal backbone shared.<n>We instantiate this design as VideoLLM-MoTE and evaluate it on five COIN benchmarks using explicit task routes.
  arXiv  Detail & Related papers  (2026-08-25T16:00:11Z)
• Task-Instructed Causal Routing of Vision Foundation Models for Multi-Task Learning [28.310784933552444]
  Vision foundation models (VFMs) have demonstrated strong robustness and transferability across a wide range of visual tasks.<n>We propose TIGER (Task-Guided Expert Routing), a framework that coordinates multiple heterogeneous VFMs for multi-task dense prediction.<n>We evaluate TIGER on two multi-task dense prediction benchmarks, NYUD-v2 and Pascal Context, where it consistently outperforms recent multi-task learning baselines.
  arXiv  Detail & Related papers  (2026-06-14T11:55:13Z)
• Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts [58.86896791968143]
  Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks.<n>We identify a puzzling phenomenon termed Seeing but Not Thinking: models accurately perceive image content yet fail in subsequent reasoning.<n>We propose the Routing Distraction hypothesis: when processing visual inputs, the routing mechanism fails to adequately activate task-relevant reasoning experts.
  arXiv  Detail & Related papers  (2026-04-09T17:59:44Z)
• Split-on-Share: Mixture of Sparse Experts for Task-Agnostic Continual Learning [10.01449025634975]
  Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma.<n>We introduce SETA, a framework that resolves the plasticity-stability conflict by decomposing the model into modular subspaces.<n>We show that SETA consistently outperforms state-of-the-art parameter-efficient fine-tuning-based continual learning methods.
  arXiv  Detail & Related papers  (2026-01-24T22:39:22Z)
• RouteMark: A Fingerprint for Intellectual Property Attribution in Routing-based Model Merging [69.2230254959204]
  We propose RouteMark, a framework for IP protection in merged MoE models.<n>Our key insight is that task-specific experts exhibit stable and distinctive routing behaviors under probing inputs.<n>For attribution and tampering detection, we introduce a similarity-based matching algorithm.
  arXiv  Detail & Related papers  (2025-08-03T14:51:58Z)
• RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow [19.502882116487005]
  Remote sensing imagery presents vast, inherently unstructured spatial data.<n>We propose RemoteReasoner, a unified workflow for geospatial reasoning.<n>RemoteReasoner achieves state-of-the-art (SOTA) performance across multi-granularity reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-25T13:58:11Z)
• Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution [23.375053899418504]
  Instead of probing frozen representations from a complex source task, we explore the effectiveness of embeddings from multiple simple source tasks on a single target task.<n>Our findings reveal that task embeddings vary significantly in utility for coreference resolution, with semantic similarity tasks proving most beneficial.
  arXiv  Detail & Related papers  (2025-01-31T17:12:53Z)
• Complexity Experts are Task-Discriminative Learners for Any Image Restoration [80.46313715427928]
  We introduce complexity experts" -- flexible expert blocks with varying computational complexity and receptive fields.<n>This preference effectively drives task-specific allocation, assigning tasks to experts with the appropriate complexity.<n>The proposed MoCE-IR model outperforms state-of-the-art methods, affirming its efficiency and practical applicability.
  arXiv  Detail & Related papers  (2024-11-27T15:58:07Z)
• Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding [52.723297744257536]
  Pre-trained language models (LMs) have shown effectiveness in scientific literature understanding tasks. We propose a multi-task contrastive learning framework, SciMult, to facilitate common knowledge sharing across different literature understanding tasks.
  arXiv  Detail & Related papers  (2023-05-23T16:47:22Z)
• EEML: Ensemble Embedded Meta-learning [5.9514420658483935]
  We propose an ensemble embedded meta-learning algorithm (EEML) that explicitly utilizes multi-model-ensemble to organize prior knowledge into diverse specific experts. We rely on a task embedding cluster mechanism to deliver diverse tasks to matching experts in training process and instruct how experts collaborate in test phase. The experimental results show that the proposed method outperforms recent state-of-the-arts easily in few-shot learning problem.
  arXiv  Detail & Related papers  (2022-06-18T12:37:17Z)
• Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study [75.42182503265056]
  Multi-Task Learning has emerged as a methodology in which multiple tasks are jointly learned by a shared learning algorithm. We deal with heterogeneous MTL, simultaneously addressing detection, classification & regression problems. We build FaceBehaviorNet, the first framework for large-scale face analysis, by jointly learning all facial behavior tasks.
  arXiv  Detail & Related papers  (2021-05-08T22:26:52Z)
• Robust Learning Through Cross-Task Consistency [92.42534246652062]
  We propose a broadly applicable and fully computational method for augmenting learning with Cross-Task Consistency. We observe that learning with cross-task consistency leads to more accurate predictions and better generalization to out-of-distribution inputs.
  arXiv  Detail & Related papers  (2020-06-07T09:24:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.