Papers with linear interpolation
Robust and Minimally Invasive Watermarking for EaaS (2025.findings-acl)
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| Challenge: | Existing watermarking methods use a target embedding to create watermarks, but this method results in each embeddable having the same component, making it difficult to remove the watermark. |
| Approach: | They propose to use embedding watermarks to protect EaaS from model extraction attacks . eaas is vulnerable to model extraction, highlighting the need for copyright protection . |
| Outcome: | The proposed method can watermark embeddings against model extraction attacks without sacrificing the quality of the embeddables. |
MetaMixSpeech: Meta Task Augmentation for Low-Resource Speech Recognition (2025.findings-emnlp)
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| Challenge: | Meta-learning has proven to be a powerful paradigm for improving speech recognition performance . however, multilingual meta learning also faces challenges such as task overfitting and learner overfit . |
| Approach: | a new method is proposed to augment meta-training tasks with "more data" the method incorporates both support and query augmentations . |
| Outcome: | The proposed method achieves a 6.35% improvement in the word error rate on FLEURS and Common Voice datasets. |
CLFFRD: Curriculum Learning and Fine-grained Fusion for Multimodal Rumor Detection (2024.lrec-main)
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| Challenge: | Existing multimodal rumor detection models overlook sample difficulty and order when training . Existing models overlook text-level difficulty, image-level and multimodal difficulty when training samples . |
| Approach: | They propose a curriculum learning framework that uses fine-grained fusion to detect rumors . they propose fusion-based methods that combine text and images to enhance semantic cohesion . |
| Outcome: | The proposed framework outperforms state-of-the-art models on English and Chinese benchmark datasets. |
Dynamic Nonlinear Mixup with Distance-based Sample Selection (2022.coling-1)
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| Challenge: | Existing methods to augment data with mixup are limited by the space of synthetic data and its regularization effect. |
| Approach: | They propose a dynamic nonlinear mixup with distance-based sample selection which generates multiple sample pairs based on the distance between each sample. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple public datasets. |
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation (D19-1)
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| Challenge: | Existing approaches to train variational autoencoders (VAEs) have been proposed to alleviate the posterior collapse issue in NLP tasks. |
| Approach: | They propose to introduce a mutual information term between the input and its latent variable to regularize the objective of the VAE. |
| Outcome: | The proposed model performs better on three benchmark datasets and is comparable to state-of-the-art models. |