We propose a novel non-parametric/un-trainable language model, named Non-Parametric Pairwise Attention Random Walk Model (NoPPA), to generate sentence embedding only with pre-trained word embedding and pre-counted word frequency. To the best we know, this study is the first successful attempt to break the constraint on bag-of-words assumption with a non-parametric attention mechanism. We evaluate our method on eight different downstream classification tasks. The experiment results show that NoPPA outperforms all kinds of bag-of-words-based methods in each dataset and provides a comparable or better performance than the state-of-the-art non-parametric methods on average. Furthermore, visualization supports that NoPPA can understand contextual topics, common phrases, and word causalities. Our model is available at https://github.com/JacksonWuxs/NoPPA.
@article{arxiv.2302.12903,
title = {NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation},
author = {Xuansheng Wu and Zhiyi Zhao and Ninghao Liu},
journal= {arXiv preprint arXiv:2302.12903},
year = {2023}
}