Papers with fusion module
Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in NLU (2022.findings-naacl)
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| Challenge: | a recent study has shown that fine-tuning pre-trained models is parameter-inefficient and expensive. |
| Approach: | They propose a task-attuned token module which integrates pre-trained network representations into a pre-trainer. |
| Outcome: | The proposed model trains only 0.0009% of the parameters and is efficient during computation and scalable during deployment. |
Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation (2023.acl-long)
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| Challenge: | a counterspeech with a certain intent may not be sufficient in every situation due to complex nature of hate speech . a novel framework for intent-conditioned counterseech generation is proposed to address the pervasive issue of hateful speech on the internet. |
| Approach: | They propose a framework for intent-conditioned counterspeech generation that leverages intent-specific representations and a fusion module to incorporate intent-related information into the model. |
| Outcome: | The proposed framework outperforms baselines by 10% across evaluation metrics. |
IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot Captioning (2024.emnlp-main)
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| Challenge: | Existing text-only training methods overlook the modality gap between using text data during training and employing images during inference. |
| Approach: | They propose a novel approach that aligns text features with visually relevant features to mitigate the modality gap between using text data during training and employing images during inference. |
| Outcome: | The proposed method outperforms the state-of-the-art methods in image captioning and video captioning by a significant margin compared to training with text data. |