ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
Table 8: F1 score averaged over the labeled F1 score and the frame F1 score on the development sets of DM and PSD. basic represents our model with embeddings described in 3.1 except lemma and named entity embeddings.
['[EMPTY]', 'DM', 'PSD']
We found that one of the difference is about the lemma annotations of entities, for example, lemmas of “Pierre Vinken” are “Pierre” and “Vinken” in the companion data while the lemmas are named-entity-like tags “Pierre” and “_generic_proper_ne” in the original SDP dataset. Based on this discovery, we experimented on the influence of named entity tags on parsing performance. We used Illinois Named Entity Tagger RatinovRo09 in white list to predict named entity tags and compared the performance on the development sets of DM and PSD. We tuned the hyperparameters for all the embedding conditions in the table, and we found that adding lemma or named entity embeddings results in a slight improvement on DM but does not help on PSD. With both lemma and named entity embeddings, there is a further improvement on both DM and PSD, which shows the named entity tags are helpful for semantic dependency parsing. As a result, we apply named entity information in parsing other frameworks.