Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
Figure 7: Input for the sentence I am not that rich, without and with POS-tags. POS-tags are inserted as super characters. The +-symbols represent spaces (word boundaries).
['sentence: sentence + POS:', 'I', '+ PRP', 'a +', 'm a', '+ m', 'n VBP', 'o +', 't n', '+ o', 't', 'h RB', 'a +', 't', '+ h', 'r a', 'i t', 'c IN', 'h +', '+ r', '. i', 'c', 'h', 'JJ', '+', '.']
We are not necessarily restricted to only using characters as input. For example, we can view the AMR relations (e.g. :ARG0, :mod) as atomic instead of a set of characters. This ensures that the characters for relations (e.g. m, o and d for :mod) do not influence the general character embeddings of the concepts, which might improve performance. This way, we create a hybrid model that is a combination of word and character level input. We might still be able to benefit from syntactic information, even though we use a character-level neural semantic parser. To show this, we parse the sentences with the POS-tagger of the C&C tools Clark et al. Put differently, we create a new super character for each unique tag and add this to the input sentence. On the one hand, this will increase the size of the input. On the other hand, just a single character will add a lot of general, potentially useful, information. For example, proper nouns correlate with the :name relation, while adjectives correlate with the :mod relation.