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

TL;DR

This paper introduces FF2, a two-stream feature fusion framework that enhances punctuation restoration by combining semantic and auxiliary features, achieving state-of-the-art results on benchmark datasets without extra data.

Contribution

The paper proposes a novel two-stream framework that fuses features from pre-trained language models and auxiliary modules, improving punctuation restoration performance.

Findings

Achieves new SOTA on IWSLT benchmark

Effectively combines semantic and auxiliary features

Enhances context awareness without additional data

Abstract

To accomplish punctuation restoration, most existing methods focus on introducing extra information (e.g., part-of-speech) or addressing the class imbalance problem. Recently, large-scale transformer-based pre-trained language models (PLMS) have been utilized widely and obtained remarkable success. However, the PLMS are trained on the large dataset with marks, which may not fit well with the small dataset without marks, causing the convergence to be not ideal. In this study, we propose a Feature Fusion two-stream framework (FF2) to bridge the gap. Specifically, one stream leverages a pre-trained language model to capture the semantic feature, while another auxiliary module captures the feature at hand. We also modify the computation of multi-head attention to encourage communication among heads. Then, two features with different perspectives are aggregated to fuse information and enhance context awareness. Without additional data, the experimental results on the popular benchmark IWSLT demonstrate that FF2 achieves new SOTA performance, which verifies that our approach is effective.