  Concept definition is important in language understanding (LU) adaptation
since literal definition difference can easily lead to data sparsity even if
different data sets are actually semantically correlated. To address this
issue, in this paper, a novel concept transfer learning approach is proposed.
Here, substructures within literal concept definition are investigated to
reveal the relationship between concepts. A hierarchical semantic
representation for concepts is proposed, where a semantic slot is represented
as a composition of {\em atomic concepts}. Based on this new hierarchical
representation, transfer learning approaches are developed for adaptive LU. The
approaches are applied to two tasks: value set mismatch and domain adaptation,
and evaluated on two LU benchmarks: ATIS and DSTC 2\&3. Thorough empirical
studies validate both the efficiency and effectiveness of the proposed method.
In particular, we achieve state-of-the-art performance ($F_1$-score 96.08\%) on
ATIS by only using lexicon features.
