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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...
In this paper, we propose a convolutional framework for short texts expansion and classification. Particularly, by using additive composition over word embeddings from context with variable window width, the representations of multi-scale semantic units are computed first. Empirically, the semantically related words are usually close to each other in embedding spaces. Thus, the restricted nearest...
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