Improving Text Auto-Completion with Next Phrase Prediction

• URL: http://arxiv.org/abs/2109.07067v1
• Date: Wed, 15 Sep 2021 04:26:15 GMT
• Title: Improving Text Auto-Completion with Next Phrase Prediction
• Authors: Dong-Ho Lee, Zhiqiang Hu and Roy Ka-Wei Lee
• Abstract summary: Our strategy includes a novel self-supervised training objective called Next Phrase Prediction (NPP) Preliminary experiments have shown that our approach is able to outperform the baselines in auto-completion for email and academic writing domains.
• Score: 9.385387026783103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 academic writing domains.

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