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        <namePart type="given">Dong-Ho</namePart>
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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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    <abstract>Language models such as GPT-2 have performed well on constructing syntactically sound sentences for text auto-completion tasks. 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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.378</identifier>
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%0 Conference Proceedings
%T Improving Text Auto-Completion with Next Phrase Prediction
%A Lee, Dong-Ho
%A Hu, Zhiqiang
%A Lee, Roy Ka-Wei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F lee-etal-2021-improving-text-auto
%X Language models such as GPT-2 have performed well on constructing syntactically sound sentences for text auto-completion tasks. 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.
%R 10.18653/v1/2021.findings-emnlp.378
%U https://aclanthology.org/2021.findings-emnlp.378
%U https://doi.org/10.18653/v1/2021.findings-emnlp.378
%P 4434-4438

Markdown (Informal)

[Improving Text Auto-Completion with Next Phrase Prediction](https://aclanthology.org/2021.findings-emnlp.378) (Lee et al., Findings 2021)

• Improving Text Auto-Completion with Next Phrase Prediction (Lee et al., Findings 2021)

ACL

• Dong-Ho Lee, Zhiqiang Hu, and Roy Ka-Wei Lee. 2021. Improving Text Auto-Completion with Next Phrase Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4434–4438, Punta Cana, Dominican Republic. Association for Computational Linguistics.