Improving Text Auto-Completion with Next Phrase Prediction
Dong‐Ho Lee, Zhiqiang Hu, Roy Ka-Wei Lee · 2021
Language models such as GPT-2 have performed well on constructing syntactically sound sentences for text auto-completion task.However, such models often require considerable training effort to adapt to specific writing domains (e.g., medical).In this paper, we propose an intermediate training strategy to enhance pre-trained language models' performance in the text auto-completion task and fastly adapt them to specific domains.Our strategy includes a novel self-supervised training objective called Next Phrase Prediction (NPP), which encourages a language model to complete the partial query with enriched phrases and eventually improve the model's text auto-completion performance.Preliminary experiments have shown that our approach is able to outperform the baselines in auto-completion for email and academicwriting domains.