Improved Relation Classification by Deep Recurrent Neural Networkswith Data Augmentation
Table 1: Comparing variants of data augmentation.
['[EMPTY]', 'Depth 1', 'Depth 2']
For data augmentation, it is natural that the inversed Other relation is also in the Other class itself. We deem the Other class contains mainly noise, and is inimical to our model. Then we conducted another experiment where we only augmented the Other class. The result verifies our conjecture as we obtained an even larger degradation of 1.1% in this setting. We observe that a single layer of CNN is also effective, yielding an F1-score slightly worse than our RNN. But the deep architecture hurts the performance of CNNs in this task. One plausible explanation is that, when convolution is performed, the beginning and end of a sentence are typically padded with a special symbol or simply zero. However, the shortest dependency path between two entities is usually not very long (∼4 on average). Hence, sentence boundaries may play a large role in convolution, which makes CNNs vulnerable.