Curricular Transfer Learning for Sentence Encoded Tasks

Jader Martins Camboim de Sá; Matheus Ferraroni Sanches; Rafael Roque de Souza; Júlio Cesar dos Reis; Leandro Aparecido Villas

arXiv · 2308.01849

Curricular Transfer Learning for Sentence Encoded Tasks

Jader Martins Camboim de Sá·Matheus Ferraroni Sanches·Rafael Roque de Souza·Júlio Cesar dos Reis·Leandro Aparecido Villas

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Abstract

Fine-tuning language models in a downstream task is the standard approach for many state-of-the-art methodologies in the field of NLP. However, when the distribution between the source task and target task drifts, \textit{e.g.}, conversational environments, these gains tend to be diminished. This article proposes a sequence of pre-training steps (a curriculum) guided by "data hacking" and grammar analysis that allows further gradual adaptation between pre-training distributions. In our experiments, we acquire a considerable improvement from our method compared to other known pre-training approaches for the MultiWoZ task.

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Jader Martins Camboim de Sá, Matheus Ferraroni Sanches, Rafael Roque de Souza, Júlio Cesar dos Reis, Leandro Aparecido Villas. 2023-08-03. Curricular Transfer Learning for Sentence Encoded Tasks. https://arxiv.org/abs/2308.01849

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