Applying prompt tuning and POS tagger features to health mention classification yields small F1 improvements over plain fine-tuning, but the paper does not compare with actual state-of-the-art systems.
Multi-layer Representation Fusion for Neural Machine Translation
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abstract
Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.
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cs.CL 1years
2025 1verdicts
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Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning
Applying prompt tuning and POS tagger features to health mention classification yields small F1 improvements over plain fine-tuning, but the paper does not compare with actual state-of-the-art systems.