Learning Joint Semantic Parsers from Disjoint Data
Table 5: Labeled parsing performance in F1 score for DM semantic dependencies. id denotes in-domain WSJ test data, and ood denotes out-of-domain brown corpus test data. Bold font indicates best performance.
['[BOLD] Model', '[BOLD] id  [ITALIC] F1', '[BOLD] ood  [ITALIC] F1']
We compare FN performance of our joint learning model (Full) to two baselines: A single-task frame SRL model, trained using a structured hinge objective. A joint model without cross-task parts. It demonstrates the effect of sharing parameters in word embeddings and LSTMs (like in Full). It does not use latent semantic dependency structures, and aims to minimize the sum of training losses from both tasks. We also compare semantic dependency parsing performance against the single task model by Peng et al. Our reimplementation of the Basic model slightly improves performance on in-domain test data. The NoCTP model ties parameters from word embeddings and LSTMs when training on FrameNet and DM, but does not use cross-task parts or joint prediction. NoCTP achieves similar in-domain test performance, and improves over Basic on out-of-domain data. By jointly predicting FrameNet structures and semantic dependency graphs, the Full model outperforms the baselines by more than 0.6% absolute F1 scores under both settings.