Reproduce “Text Infilling” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Wanrong Zhu, Zhiting Hu, Eric Xing
Researchers from Peking University, Carnegie Mellon University, and Petuum Inc. introduced a general text infilling task, proposing a self-attention model equipped with a novel segment-aware position encoding. This model consistently outperformed attentional Seq2Seq and GAN-based baselines, achieving higher BLEU scores and lower perplexity across various infilling scenarios, including complex long-content generation, and receiving higher ratings in human evaluations.
Key insights
- Bidirectional context modeling, enabled by self-attention mechanisms, is critical for producing coherent and contextually appropriate text infills.
- The proposed segment-aware position encoding effectively addresses the challenge of localizing tokens in templates with multiple, variable-length missing segments.
- Generalized text infilling serves as a direct application and generalization of masked language modeling objectives, leveraging similar architectural principles.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.