Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations
arxiv(2024)
摘要
Current open-domain neural semantics parsers show impressive performance.
However, closer inspection of the symbolic meaning representations they produce
reveals significant weaknesses: sometimes they tend to merely copy character
sequences from the source text to form symbolic concepts, defaulting to the
most frequent word sense based in the training distribution. By leveraging the
hierarchical structure of a lexical ontology, we introduce a novel
compositional symbolic representation for concepts based on their position in
the taxonomical hierarchy. This representation provides richer semantic
information and enhances interpretability. We introduce a neural "taxonomical"
semantic parser to utilize this new representation system of predicates, and
compare it with a standard neural semantic parser trained on the traditional
meaning representation format, employing a novel challenge set and evaluation
metric for evaluation. Our experimental findings demonstrate that the
taxonomical model, trained on much richer and complex meaning representations,
is slightly subordinate in performance to the traditional model using the
standard metrics for evaluation, but outperforms it when dealing with
out-of-vocabulary concepts. This finding is encouraging for research in
computational semantics that aims to combine data-driven distributional
meanings with knowledge-based symbolic representations.
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