Text Coherence Analysis Based on Deep Neural Network
This work addresses text coherence assessment, which is important for natural language processing applications, but it appears incremental as it builds on existing neural network approaches.
The paper tackled the problem of text coherence analysis by proposing a deep coherence model that learns sentence representations and coherence modeling simultaneously, achieving a significant improvement over state-of-the-art methods on the Sentence Ordering task.
In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.