CM3T shows that multi-head vision adapters plus cross-attention adapters can adapt frozen supervised-pretrained video transformers with a fraction of the trainable parameters of full fine-tuning.
Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion
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abstract
Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection. This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages. (1) With a vast number of annotators available for world languages and the storage-efficient adapter models, this approach is more cost efficient. Models can be updated more frequently and thus stay up-to-date. (2) Adapter fusion provides insights and allows for interpretation regarding the influence of each adapter model on a particular language. The proposed solution often outperformed the top multilingual approaches in our benchmark tasks.
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CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets
CM3T shows that multi-head vision adapters plus cross-attention adapters can adapt frozen supervised-pretrained video transformers with a fraction of the trainable parameters of full fine-tuning.