Improving Context Modeling in Neural Topic Segmentation

Understand

Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches.

• However, current neural solutions are arguably limited in how they model context.
• In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention.
• Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets.