Neural Compositional Denotational Semantics for Question Answering
Table 3: Results for Human Queries (GenX) Our model outperforms LSTM and semantic parsing models on complex human-generated queries, showing it is robust to work on natural language. Better performance than Tree-LSTM (Unsup.) shows the efficacy in representing sub-phrases using explicit denotations. Our model also performs better without an external parser, showing the advantages of latent syntax.
['[BOLD] Model', '[BOLD] Accuracy']
S6SS2SSS0Px3 Performance on Human-generated Language: Our approach outperforms strong LSTM and semantic parsing baselines, despite the semantic parser’s use of hard-coded operators. These results suggest that our method represents an attractive middle ground between minimally structured and highly structured approaches to interpretation. Our model learns to interpret operators such as except that were not considered during development. This shows that our model can learn to parse human language, which contains greater lexical and structural diversity than synthetic questions. Trees induced by the model are linguistically plausible (see Appendix D).