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Multi-sense based neural machine translation
Yang Z(杨振); Chen W(陈炜); Wang F(王峰); Wang F(王峰); Chen W(陈炜)
Conference NameInternational Joint Conference on Neural Networks
Conference Date2017-5-4
Conference PlaceAnchorage, AK, USA
Attention mechanism advances the neural machine
translation (NMT) by reducing the confusion introduced by
irrelevant words in long sentences. However, the confusion caused
by ambiguous words hasn’t been handled yet and it may be
a bottleneck for the NMT model. This paper validates the
hypothesis and proposes a simple and flexible framework, which
enables the NMT model to only focus on the relevant sense type
of the input word in current context. Experiments show that the
proposed model achieves substantial improvements on every test
set over competitive baselines. Our contributions come from twofold.
Firstly, to the best of our knowledge, this is the first effort to
introduce the multi-sense representation, which represents each
sense type of the word with a sense-specific embedding, into
NMT. Secondly, We propose a sense search module which can
detect the sense type of the word automatically. Flexibility and
versatility are the most attractive characteristic of the proposed
sense search module. It can be applied to any other semantic
related NLP tasks with little modification.
Indexed ByEI
Document Type会议论文
Corresponding AuthorChen W(陈炜)
Recommended Citation
GB/T 7714
Yang Z,Chen W,Wang F,et al. Multi-sense based neural machine translation[C],2017.
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