Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Table 3: Multi-task learning improved accuracy for both BiLSTMs and Graph LSTMs.
['[EMPTY]', '[BOLD] Drug-Gene-Mut.', '[BOLD] Drug-Mut.']
To evaluate whether joint learning with sub-relations can help, we conducted multi-task learning using Graph LSTM-FULL to jointly train extractors for both the ternary interaction and the drug-mutation, drug-gene sub-relations. Multi-task learning resulted in a significant gain for both the ternary interaction and the drug-mutation interaction. Interestingly, the advantage of graph LSTMs over BiLSTMs is reduced with multi-task learning, suggesting that with more supervision signal, even linear-chain LSTMs can learn to capture long-range dependencies that are were made evident by parse features in graph LSTMs. Note that there are many more instances for drug-gene interaction than others, so we only sampled a subset of comparable size. Therefore, we do not evaluate the performance gain for drug-gene interaction, as in practice, one would simply learn from all available data, and the sub-sampled results are not competitive.