pNLP-Mixer: an Efficient all-MLP Architecture for Language

Large pre-trained language models drastically changed the natural language processing(NLP) landscape. Nowadays, they represent the go-to framework to tackle diverse NLP tasks, even with a limited number of annotations. However, using those models in production, either in the cloud or at the edge, remains a challenge due to the memory footprint and/or inference costs. As an alternative, recent work on efficient NLP has shown that small weight-efficient models can reach competitive performance at a fraction of the costs. Here, we introduce pNLP-Mixer, an embbedding-free model based on the MLP-Mixer architecture that achieves high weight-efficiency thanks to a novel linguistically informed projection layer. We evaluate our model on two multi-lingual semantic parsing datasets, MTOP and multiATIS. On MTOP our pNLP-Mixer almost matches the performance of mBERT, which has 38 times more parameters, and outperforms the state-of-the-art of tiny models (pQRNN) with 3 times fewer parameters. On a long-sequence classification task (Hyperpartisan) our pNLP-Mixer without pretraining outperforms RoBERTa, which has 100 times more parameters, demonstrating the potential of this architecture.