文献类型： 会议论文
第一作者： Yingji Zhang
作者： Yingji Zhang 1 ; Marco Valentino 2 ; Danilo S. Carvalho 3 ; Ian Pratt-Hartmann 1 ; Andre Freitas 4 ;
作者机构： 1.Department of Computer Science, University of Manchester, United Kingdom
2.Idiap Research Institute, Switzerland
3.Department of Computer Science, University of Manchester, United Kingdom##Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom
4.Department of Computer Science, University of Manchester, United Kingdom##Idiap Research Institute, Switzerland##Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom
会议名称： [ "Conference of The North American Chapter of The Association for Computational Linguistics: Human Language Technologies" , "Conference of the North American Chapter of the Association for Computational Linguistics"]
主办单位：
页码： 474-489
摘要： The injection of syntactic information in Variational AutoEncoders (VAEs) can result in an overall improvement of performances and generalisation. An effective strategy to achieve such a goal is to separate the encoding of distributional semantic features and syntactic structures into heterogeneous latent spaces via multi-task learning or dual encoder architectures. However, existing works employing such techniques are limited to LSTM-based VAEs. This work investigates latent space separation methods for structural syntactic injection in Transformer-based VAE architectures (i.e., Op-timus) through the integration of graph-based models. Our empirical evaluation reveals that the proposed end-to-end VAE architecture can improve theoverall organisation of the latent space, alleviating the information loss occurring in standard VAE setups, and resulting in enhanced performances on language modelling and downstream generation tasks.
分类号： tp311
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