MemexQA: Visual Memex Question Answering
Table 3: Performance comparison on the SQuAD development set
['Method', 'LR', 'Match-LSTM\xa0 wang2016machine ', 'DCN\xa0 xiong2016dynamicb ', 'MPCM\xa0 wang2016multi ', 'BiDAF\xa0 seo2016bidirectional ', 'MemexNet']
We employ a lemmatization model as our query encoder and the bi-directional LSTM networks (word embedding + char CNN) as our question encoder. As we see, although MemexNet is not designed for this task and only uses 5 sentences, it achieves accuracy which is comparable or better than the recent text QA models.