@article{Zhang_Zhang_Ma_Gu_Su_Song_2019, title={A Generalized Language Model in Tensor Space}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4735}, DOI={10.1609/aaai.v33i01.33017450}, abstractNote={<p>In the literature, tensors have been effectively used for capturing the context information in language models. However, the existing methods usually adopt relatively-low order tensors, which have limited expressive power in modeling language. Developing a higher-order tensor representation is challenging, in terms of deriving an effective solution and showing its generality. In this paper, we propose a language model named Tensor Space Language Model (TSLM), by utilizing tensor networks and tensor decomposition. In TSLM, we build a high-dimensional semantic space constructed by the tensor product of word vectors. Theoretically, we prove that such tensor representation is a generalization of the <em>n</em>-gram language model. We further show that this high-order tensor representation can be decomposed to a recursive calculation of conditional probability for language modeling. The experimental results on Penn Tree Bank (PTB) dataset and WikiText benchmark demonstrate the effectiveness of TSLM.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhang, Lipeng and Zhang, Peng and Ma, Xindian and Gu, Shuqin and Su, Zhan and Song, Dawei}, year={2019}, month={Jul.}, pages={7450-7458} }