Deep Multi-Task Learning with Shared Memory
Table 4: Accuracies of our models on product reviews dataset. The last column gives the improvement relative to LSTM and ME-LSTM respectively.
['[BOLD] Model Single Task', '[BOLD] Model LSTM', 'Books 78.0', 'DVDs 79.5', 'Electronics 81.2', 'Kitchen 81.8', 'AvgΔ -']
The row of “Single Task” shows the results of the baseline for each individual task. With the help of global shared memory (ARC-I), the performances of these four tasks are improved by an average of 2.9%(2.6%) compared with LSTM(ME-LSTM). ARC-II achieves best performances on three sub-tasks, and its average improvement is 3.7%(3.5%). Compared with MT-CNN and MT-DNN, our models achieve a better performance. We think the reason is that our models can not only share lexical information but share complicated patterns of sentences by reading/writing operations of external memory. Furthermore, these results on product reviews are consistent with that on movie reviews, which shows our architectures are robust.