This research paper discusses the Backpack, an alternative to the Transformer model, and its application in Chinese language modeling. The Backpack improves interpretability in English language modeling by breaking down…
Continue readingThe article proposes the use of Transformer-based language models to classify entity legal forms from raw legal entity names. The study evaluated the performance of various BERT variants against traditional…
Continue readingThe study proposes a novel action sequence predictor called Decision ConvFormer (DC), which is based on the MetaFormer architecture. DC uses local convolution filtering as the token mixer and effectively…
Continue readingThe article presents a new approach to speech separation using Single-Path Global Modulation (SPGM). Traditional dual-path models separate long sequences into overlapping chunks, modelling local and global features separately. However,…
Continue readingThis paper introduces DurIAN-E, an improved duration informed attention neural network for expressive and high-quality text-to-speech synthesis. DurIAN-E uses multiple stacked SwishRNN-based Transformer blocks as linguistic encoders and incorporates Style-Adaptive…
Continue readingThe study presents the Associative Transformer (AiT), a model that uses sparse interactions instead of the conventional pairwise attention mechanism, aligning more with biological principles. The AiT model is based…
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