Dynamic Memory Networks for Question Answering

Dynamic Memory Networks (DMNs) have shown recent success in question answering. They have achieved state-of-the-art results of the Facebook bAbI dataset and performed well on sentiment analysis and visual question answering [1] [6]. In this project, we implement our own DMN in tensorflow and verify its performance quantitatively and qualitatively. We achieve very similar results to those achieved in Kumar et al. and Xiong et al. [1] [6]. In addition, we build a demo to visualize the attention placed on different input sentences in the episodic memory module, and show that the model places it’s attention on the correct sentences for a variety of different tasks even without any explicit attention feedback during training. Last, we experiment with training a model to accomplish more than one babi task at the same time. We show that DMNs can successfully complete multiple babi tasks with the same model including one step reasoning, two step reasoning and yes/no questions. In addition, we illustrate through the demo that the combined model places the attention on the correct sentences when performing the different tasks.