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Article overview
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Compositional Sentence Representation from Character within Large Context Text | Geonmin Kim
; Hwaran Lee
; Jisu Choi
; Soo-young Lee
; | Date: |
2 May 2016 | Abstract: | In this work, we targeted two problems of representing a sentence on the
basis of a constituent word sequence: a data-sparsity problem in
non-compositional word embedding, and no usage of inter-sentence dependency. To
improve these two problems, we propose a Hierarchical Composition Recurrent
Network (HCRN), which consists of a hierarchy with 3 levels of compositional
models: character, word and sentence. In HCRN, word representations are built
from characters, thus resolving the data-sparsity problem. Moreover, an
inter-sentence dependency is embedded into the sentence representation at the
level of sentence composition. In order to alleviate optimization difficulty of
end-to-end learning for the HCRN, we adopt a hierarchy-wise learning scheme.
The HCRN was evaluated on a dialogue act classification task quantitatively and
qualitatively. Especially, sentence representations with an inter-sentence
dependency significantly improved the performance by capturing both implicit
and explicit semantics of sentence. In classifying dialogue act on the
SWBD-DAMSL database, our HCRN achieved state-of-the-art performance with a test
error rate of 22.7%. | Source: | arXiv, 1605.0482 | Services: | Forum | Review | PDF | Favorites |
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