Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders

• URL: http://arxiv.org/abs/2311.08579v1
• Date: Tue, 14 Nov 2023 22:47:23 GMT
• Title: Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
• Authors: Yingji Zhang, Marco Valentino, Danilo S. Carvalho, Ian Pratt-Hartmann, Andr\'e Freitas
• Abstract summary: In this paper, we investigate latent space separation methods for structural syntactic injection in Transformer-based VAEs. Specifically, we explore how syntactic structures can be leveraged in the encoding stage through the integration of graph-based and sequential models. Our empirical evaluation, carried out on natural language sentences and mathematical expressions, reveals that the proposed end-to-end VAE architecture can result in a better overall organisation of the latent space.
• Score: 5.037881619912574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to 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. In this paper, we investigate latent space separation methods for structural syntactic injection in Transformer-based VAE architectures (i.e., Optimus). Specifically, we explore how syntactic structures can be leveraged in the encoding stage through the integration of graph-based and sequential models, and how multiple, specialised latent representations can be injected into the decoder's attention mechanism via low-rank operators. Our empirical evaluation, carried out on natural language sentences and mathematical expressions, reveals that the proposed end-to-end VAE architecture can result in a better overall organisation of the latent space, alleviating the information loss occurring in standard VAE setups, resulting in enhanced performances on language modelling and downstream generation tasks.

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