A Unified Neural Coherence Model
Table 3: Results in accuracy on the Local Discrimination task. ⋆ is a pre-trained model on the global discrimination task.
['[BOLD] Model', '[BOLD] Emb.', 'D [ITALIC] w=1,2,3', 'D [ITALIC] w=1', 'D [ITALIC] w=2', 'D [ITALIC] w=3']
From the table, we see that existing models including our global model perform poorly compared to our proposed local models. They are likely to fail in distinguishing the text segments that are locally coherent and penalize them unfairly. One of the possible explanations of this phenomenon can be found in the nature of the global model. These models (except L&H) are designed to make a decision at a global level, thus they are likely to penalize locally coherent segments of a text. This observation is further bolstered by the performance of our local coherence models, which show higher sensitivity in discriminating locally coherent texts and achieve significantly higher accuracy compared to the baseline models and our global model.