Title | ||
---|---|---|
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations. |
Abstract | ||
---|---|---|
We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs). Using a sequence-to-sequence model, and some trivial preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1 (F-score on AMR-triples). We examine five different approaches to improve this baseline result: (i) reordering AMR branches to match the word order of the input sentence increases performance to 58.3; (ii) adding part-of-speech tags (automatically produced) to the input shows improvement as well (57.2); (iii) So does the introduction of super characters (conflating frequent sequences of characters to a single character), reaching 57.4; (iv) optimizing the training process by using pre-training and averaging a set of models increases performance to 58.7; (v) adding silver-standard training data obtained by an off-the-shelf parser yields the biggest improvement, resulting in an F-score of 64.0. Combining all five techniques leads to an F-score of 71.0 on holdout data, which is state-of-the-art in AMR parsing. This is remarkable because of the relative simplicity of the approach. |
Year | Venue | Field |
---|---|---|
2017 | computational linguistics in the netherlands | Training set,Word order,Computer science,Speech recognition,Preprocessor,Single character,Natural language processing,Artificial intelligence,Parsing,Linguistics,Sentence |
DocType | Volume | Citations |
Journal | abs/1705.09980 | 7 |
PageRank | References | Authors |
0.46 | 15 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Rik van Noord | 1 | 16 | 4.73 |
Johan Bos | 2 | 954 | 89.07 |