Concept Transfer Learning for Adaptive Language Understanding
release_caodbuxndbhhxi6vbds6aqoaqa
by
Su Zhu, Kai Yu
2017
Abstract
Semantic transfer is an important problem of the language understanding (LU),
which is about how the recognition pattern of a semantic concept benefits other
associated concepts. In this paper, we propose a new semantic representation
based on combinatory concepts. Semantic slot is represented as a composition of
different atomic concepts in different semantic dimensions. Specifically, we
propose the concept transfer learning methods for extending combinatory
concepts in LU. The concept transfer learning makes use of the common ground of
combinatory concepts shown in the literal description. Our methods are applied
to two adaptive LU problems: semantic slot refinement and domain adaptation,
and respectively evaluated on two benchmark LU datasets: ATIS and DSTC 2&3. The
experiment results show that the concept transfer learning is very efficient
for semantic slot refinement and domain adaptation in the LU.
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