  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. We also the robustness of our proposed
model in domain transfer setting by training a model on a large-scale dataset
and testing it on four challenging real-world benchmarks. Furthermore, we apply
our proposed strategy to two other languages (German and Chinese), and show its
effectiveness in multilingual scenarios.
