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RISS 인기검색어
- 검색키워드
- 저자 : Bengio, Yoshua (검색결과 2 건)
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- 유료
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Context-dependent word representation for neural machine translation
Choi, Heeyoul,Cho, Kyunghyun,Bengio, Yoshua Elsevier 2017 Computer speech & language Vol.45 No.-
<P><B>Abstract</B></P> <P>We first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedding vector, of a symbol encodes multiple dimensions of similarity, equivalent to encoding more than one meaning of the word. This has the consequence that the encoder and decoder recurrent networks in neural machine translation need to spend substantial amount of their capacity in disambiguating source and target words based on the context which is defined by a source sentence. Based on this observation, in this paper we propose to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence. Additionally, we propose to represent special tokens (such as numbers, proper nouns and acronyms) with typed symbols to facilitate translating those words that are not well-suited to be translated via continuous vectors. The experiments on En–Fr and En–De reveal that the proposed approaches of contextualization and symbolization improves the translation quality of neural machine translation systems significantly.</P>
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Fine-grained attention mechanism for neural machine translation
Choi, Heeyoul,Cho, Kyunghyun,Bengio, Yoshua Elsevier 2018 Neurocomputing Vol.284 No.-
<P><B>Abstract</B></P> <P>Neural machine translation (NMT) has been a new paradigm in machine translation, and the attention mechanism has become the dominant approach with the state-of-the-art records in many language pairs. While there are variants of the attention mechanism, all of them use only temporal attention where one scalar value is assigned to one context vector corresponding to a source word. In this paper, we propose a fine-grained (or 2D) attention mechanism where each dimension of a context vector will receive a separate attention score. In experiments with the task of En-De and En-Fi translation, the fine-grained attention method improves the translation quality in terms of BLEU score. In addition, our alignment analysis reveals how the fine-grained attention mechanism exploits the internal structure of context vectors.</P>
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