  Even in the era of massive language models, it has been suggested that
character-level representations improve the performance of neural models. The
state-of-the-art neural semantic parser for Discourse Representation Structures
uses character-level representations, improving performance in the four
languages (i.e., English, German, Dutch, and Italian) in the Parallel Meaning
Bank dataset. However, how and why character-level information improves the
parser's performance remains unclear. This study provides an in-depth analysis
of performance changes by order of character sequences. In the experiments, we
compare F1-scores by shuffling the order and randomizing character sequences
after testing the performance of character-level information. Our results
indicate that incorporating character-level information does not improve the
performance in English and German. In addition, we find that the parser is not
sensitive to correct character order in Dutch. Nevertheless, performance
improvements are observed when using character-level information.
