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arXiv · 2109.04602

Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations

Vladimir Araujo·Andrés Villa·Marcelo Mendoza·Marie-Francine Moens·Alvaro Soto
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Abstract

Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations. As a result, our proposed approach is able to predict future sentences using explicit top-down connections that operate at the intermediate layers of the network. By experimenting with benchmarks designed to evaluate discourse-related knowledge using pre-trained sentence representations, we demonstrate that our approach improves performance in 6 out of 11 tasks by excelling in discourse relationship detection.

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Vladimir Araujo, Andrés Villa, Marcelo Mendoza, Marie-Francine Moens, Alvaro Soto. 2021-09-10. Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations. https://arxiv.org/abs/2109.04602

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