Cross-lingual Semantic Parsing
Table 3: Precision, recall, and F1 scores for the evaluation metric S(ϕ,ψ) on the test dataset.
['[EMPTY]', 'Prec.', 'Rec.', 'F1']
Results: Overall, our proposed approach, Seq2Seq+Copy, achieves the best precision, recall, and F1. The two baselines, Seq2Seq+Heuristic and Seq2Seq+Random also achieve reasonable results. These two baselines both employ sequence-to-sequence models to predict meaning representations, which can be considered a replica of state-of-the-art approaches for structured prediction Vinyals et al. Our proposed approach outperforms these two strong baselines: the copying mechanism, aiming for solving coreference in cross-lingual semantic parsing, results in both precision and recall gains. The detailed gains achieved by the copying mechanism are discussed in the following section. In the Pipeline approach, each component is trained independently. During testing, residual errors from each component are propagated through the pipeline. As expected, Pipeline shows a significant performance drop compared to the other end-to-end learning approaches.