Meta Multi-Task Learning for Sequence Modeling
Table 3: Accuracies of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines. ∗is from [Liu, Qiu, and Huang2017]
['[BOLD] Task', '[BOLD] Single Task LSTM', '[BOLD] Single Task HyperLSTM', '[BOLD] Single Task MetaLSTM', '[BOLD] Single Task Avg.', '[BOLD] Multiple Tasks ASP-MTL∗', '[BOLD] Multiple Tasks PSP-MTL', '[BOLD] Multiple Tasks SSP-MTL', '[BOLD] Multiple Tasks Meta-MTL(ours)', '[BOLD] Transfer Meta-MTL(ours)']
With the help of meta knowledge, we observe an average improvement of 3.1% over the average accuracy of single models, and even better than other competitor multi-task models. This observation indicates that we can save the meta knowledge into a meta network, which is quite useful for a new task.