论文标题
改进神经主题细分中的上下文建模
Improving Context Modeling in Neural Topic Segmentation
论文作者
论文摘要
主题细分在关键的NLP任务中至关重要，最近的作品有利于高效的神经监督方法。但是，当前的神经解决方案可以说是在模型上下文中受到限制。在本文中，我们通过添加与一致相关的辅助任务和受到限制的自我注意力来增强基于层次关注Bilstm网络的细分器，以更好地模型上下文。在三个数据集中训练和测试时，我们优化的分段器的表现优于SOTA方法。我们还通过在大规模数据集中训练模型并在四个具有挑战性的现实世界基准上对其进行测试，我们提出的模型在域传输设置中的鲁棒性。此外，我们将提议的策略应用于其他两种语言（德语和中文），并在多语言场景中显示出其有效性。
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.
