Papers with fusion model

3 papers
Discourse Relation-Enhanced Neural Coherence Modeling (2025.acl-long)

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Challenge: Existing work on coherence modeling has focused on integrating entity-based models.
Approach: They propose a model that integrates text- and relation-based features for coherence assessment using position-aware attention and a visible matrix.
Outcome: The proposed model improves baselines on two benchmarks and shows that relation features are important for coherence modeling.
Neural Entity Recognition with Gazetteer based Fusion (2021.findings-acl)

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Challenge: Named entity recognition systems can be applied to clinical domains where only limited data is accessible and interpretability is important.
Approach: They propose to use auxiliary gazetteer model to fuse it with NER system . this allows for better robustness and interpretability across different clinical datasets .
Outcome: The proposed model is data efficient and can adapt to new mentions in gazetteers without retraining.
TAeKD: Teacher Assistant Enhanced Knowledge Distillation for Closed-Source Multilingual Neural Machine Translation (2024.lrec-main)

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Challenge: Large language models (LLMs) have produced impressive results in the field of Multilingual Neural Machine Translation (MNMT).
Approach: They propose a Teacher Assistant enhanced Knowledge Distillation method to augment knowledge transfer capacity from closed-source MNMT models.
Outcome: The proposed method outperforms the state-of-the-art KD methods on both WMT22 and FLORES-101 test sets.

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