Papers with unified multi-task framework
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) are limited to high-resource languages like English and Chinese. |
| Approach: | They propose a framework to make full use of annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition. |
| Outcome: | The proposed framework makes full use of both annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). |
TransLLM: A Unified Multi-Task Large Language Model for Urban Transportation via Learnable Prompting (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models lack generalization capabilities and lack structured spatiotemporal data. |
| Approach: | They propose a unified multi-task framework that synergizes spatiotemporal encoding with LLM reasoning through learnable prompt composition. |
| Outcome: | The proposed framework outperforms baseline models on seven datasets and three tasks on supervised and zero-shot settings with excellent generalization and robustness. |