FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration
This work addresses punctuation restoration for natural language processing applications, representing an incremental improvement over existing methods.
The paper tackled punctuation restoration by proposing a Feature Fusion two-stream framework (FF2) that combines semantic features from a pre-trained language model with auxiliary features, achieving new state-of-the-art performance on the IWSLT benchmark without extra data.
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.