ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2011.09031
18
0
v1v2v3v4 (latest)

Predictions For Pre-training Language Models

18 November 2020
Tonglei Guo
ArXiv (abs)PDFHTML
Abstract

Language model pre-training has proven to be useful in many language understanding tasks. In this paper, we investigate whether it is still helpful to add the specific task's loss in pre-training step. In industry NLP applications, we have large amount of data produced by users. We use the fine-tuned model to give the user-generated unlabeled data a pseudo-label. Then we use the pseudo-label for the task-specific loss and masked language model loss to pre-train. The experiment shows that using the fine-tuned model's predictions for pseudo-labeled pre-training offers further gains in the downstream task. The improvement of our method is stable and remarkable.

View on arXiv
Comments on this paper