Iterative Multi-document Neural Attention for Multiple Answer Prediction
Table 1: Comparison between our model and baselines from Dodge et al. (2015) on the QA and Recs tasks evaluated according to HITS@1 and HITS@100, respectively.
['[BOLD] METHODS', '[BOLD] QA TASK', '[BOLD] RECS TASK']
Despite the advantage of the QA SYSTEM, it is a carefully designed system to handle knowledge base data in the form of triples, but our model can leverage data in the form of documents, without making any assumption about the form of the input data and can be applied to different kind of tasks. Additionally, the model MEMN2N is a neural network whose weights are pre-trained on the same dataset without using the long-term memory and the models JOINT SUPERVISED EMBEDDINGS and JOINT MEMN2N are models trained across all the tasks of the dataset in order to boost performance. Despite that, our model outperforms the three above-mentioned ones without using any supplementary trick. Even though our model performance is higher than all the others on the Recs task, we believe that the obtained result may be improved and so we plan a further investigation. Moreover, the need for further investigation can be justified by the work reported in Searle and Bingham-Walker