English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To accomplish punctuation restoration, most existing methods focus onintroducing extra information (e.g., part-of-speech) or addressing the classimbalance problem. Recently, large-scale transformer-based pre-trained languagemodels (PLMS) have been utilized widely and obtained remarkable success.However, the PLMS are trained on the large dataset with marks, which may notfit well with the small dataset without marks, causing the convergence to benot ideal. In this study, we propose a Feature Fusion two-stream framework(FF2) to bridge the gap. Specifically, one stream leverages a pre-trainedlanguage model to capture the semantic feature, while another auxiliary modulecaptures the feature at hand. We also modify the computation of multi-headattention to encourage communication among heads. Then, two features withdifferent perspectives are aggregated to fuse information and enhance contextawareness. Without additional data, the experimental results on the popularbenchmark IWSLT demonstrate that FF2 achieves new SOTA performance, whichverifies that our approach is effective.

FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration