Curricular Transfer Learning for Sentence Encoded Tasks

• URL: http://arxiv.org/abs/2308.01849v1
• Date: Thu, 3 Aug 2023 16:18:19 GMT
• Title: Curricular Transfer Learning for Sentence Encoded Tasks
• Authors: Jader Martins Camboim de S\'a, Matheus Ferraroni Sanches, Rafael Roque de Souza, J\'ulio Cesar dos Reis, Leandro Aparecido Villas
• Abstract summary: This article proposes a sequence of pre-training steps guided by "data hacking" and grammar analysis. In our experiments, we acquire a considerable improvement from our method compared to other known pre-training approaches for the MultiWoZ task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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