Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading
Table 7: Ablation study on SQuAD dev set. Ref: reference model. CatQC: we substitute the input representation mentioned in Eq. (1) by a direct concatenation of question and context embeddings. CatQCDotProduct: starting from the CatQC architecture, we add back the dot-product between question and context embedding to the input representation. NoRnn: weaved RNN layers are replaced with a linear projection. NoConv11: we remove the convolution layer. NoMemNet: we set the number of hops to 0 in the memory network.
['Model', 'Dev EM', 'Dev F1']
To verify that the prediction performance improvements come from the co-encoding module, we conduct an ablation study on the SQuAD dev set. Architecture choices such as the exact input encoding or the presence of modules such as the convolutional layer or the memory network play a minor role in the prediction performance. However, a model without the stacked recurrent neural networks reaches only 33 F1. This shows that the RNNs applied in a weaving pattern are the major factor that enables a good prediction performance.