Improving Text Auto-Completion with Next Phrase Prediction
This addresses the need for efficient domain adaptation in text auto-completion, though it appears incremental as it builds on existing models.
The paper tackles the problem of adapting pre-trained language models like GPT-2 to specific domains for text auto-completion by proposing an intermediate training strategy with a novel self-supervised objective called Next Phrase Prediction (NPP). Preliminary experiments show it outperforms baselines in email and academic writing domains.
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.