A Joint Many-Task Model:Growing a Neural Network for Multiple NLP Tasks
Table 5: Semantic relatedness results.
['Method', 'MSE ↓']
The result of “JMTDE” is already better than the previous state-of-the-art results. Both of Zhou et al. and Tai et al. However, our method uses the simple max-pooling strategy, which suggests that it is worth investigating such simple methods before developing complex methods for simple tasks. Currently, our JMT model does not explicitly use the learned dependency structures, and thus the explicit use of the output from the dependency layer should be an interesting direction of future work. The previous state-of-the-art result in Yin et al. Again, our simple max-pooling strategy achieves the state-of-the-art result boosted by the joint training. These results show the importance of jointly handling related tasks.