  Recently, semantic parsing using hierarchical representations for dialog
systems has captured substantial attention. Task-Oriented Parse (TOP), a tree
representation with intents and slots as labels of nested tree nodes, has been
proposed for parsing user utterances. Previous TOP parsing methods are limited
on tackling unseen dynamic slot values (e.g., new songs and locations added),
which is an urgent matter for real dialog systems. To mitigate this issue, we
first propose a novel span-splitting representation for span-based parser that
outperforms existing methods. Then we present a novel lexicon-injected semantic
parser, which collects slot labels of tree representation as a lexicon, and
injects lexical features to the span representation of parser. An additional
slot disambiguation technique is involved to remove inappropriate span match
occurrences from the lexicon. Our best parser produces a new state-of-the-art
result (87.62%) on the TOP dataset, and demonstrates its adaptability to
frequently updated slot lexicon entries in real task-oriented dialog, with no
need of retraining.
