Dual Multi-head Co-attention for Multi-choice Reading Comprehension
Table 6: Comparison among different models on DREAM dataset.
['model', 'dev', 'test']
Soft Attention [nmt_attention] and BiDAF [bidaf] are originally based on traditional encoder such as LSTM [lstm], and DCMN [dcmn] is based on pre-trained LM namely BERT [bert]. Soft Attention is originally for Neural Machine Translation, and to apply it into multi-choice MRC, we multiply the attention matrix with passage representations to obtain question-aware passage representations, and then feed them into the decoder. BiDAF directly outputs question-aware passage representations. Note that the proposed DUMA outperforms all other models. Moreover, DCMN adopts a much more complicated model structure design for better matching, while our DUMA achieves the absolutely highest accuracy with a much simpler structure.