Subword-augmented Embedding for Cloze Reading Comprehension
Table 5: Accuracy on CBT dataset. Results marked with ‡ are of previously published works [Dhingra et al.2017, Cui et al.2016, Yang et al.2017].
['Model', 'CBT-NE Valid', 'CBT-NE Test', 'CBT-CN Valid', 'CBT-CN Test']
S3SS2SSS0Px3 Cbt To verify if our method can only work for Chinese, we also evaluate the effectiveness of the proposed method on benchmark English dataset. We use CBT dataset as our testbed to evaluate the performance. For a fair comparison, we simply set the same parameters as before. We observe that our model outperforms most of the previously public works, with 2.4 % gains on the CBT-NE test set compared with GA Reader which adopts word and character embedding concatenation. Our SAW Reader also achieves comparable performance with FG Reader who adopts neural gates to combine word-level and character-level representations with assistance of extra features including NE, POS and word frequency while our model is much simpler and faster. This result shows our SAW Reader is not restricted to Chinese reading comprehension, but also for other languages.