Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning
Table 4: EM scores after training on the regular data or on the adversarial data from Jiang and Bansal (2019), and evaluation on the regular dev set or the adv-dev set.
['Train Eval', 'Reg', 'Reg Adv', 'Adv Reg', 'Adv']
To test whether our modular network can perform robust multi-hop reasoning against such adversaries, we evaluate our models on the adversarial dev set. We observe that after adversarial training, both the baseline and our NMN obtain significant improvement on the adversarial evaluation, while our NMN maintains a significant advantage over the BiDAF baseline.