Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
Figure 6: Input for the AMR (t / thing :quant 1 :polarity -) representing the sentence Not one thing, with and without super characters. The +-symbols represent spaces.
['AMR, chars: AMR, super chars:', '(', 't', 'h', 'i', 'n', 'g', '+', ': :quant', 'q +', 'u 1', 'a +', 'n :polarity', 't +', '+ -', '1 )', '+', ':', 'p', 'o', 'l', 'a', 'r', 'i', 't', 'y', '+', '-', ')']
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.