Context-dependent word representation for neural machine translation
نویسندگان
چکیده
منابع مشابه
Context-dependent word representation for neural machine translation
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 t...
متن کاملContext Gates for Neural Machine Translation
In neural machine translation (NMT), generation of a target word depends on both source and target contexts. We find that source contexts have a direct impact on the adequacy of a translation while target contexts affect the fluency. Intuitively, generation of a content word should rely more on the source context and generation of a functional word should rely more on the target context. Due to...
متن کاملContext-Aware Smoothing for Neural Machine Translation
In Neural Machine Translation (NMT), each word is represented as a lowdimension, real-value vector for encoding its syntax and semantic information. This means that even if the word is in a different sentence context, it is represented as the fixed vector to learn source representation. Moreover, a large number of Out-OfVocabulary (OOV) words, which have different syntax and semantic informatio...
متن کاملContext-Dependent Phrasal Translation Lexicons for Statistical Machine Translation
Most current statistical machine translation (SMT) systems make very little use of contextual information to select a translation candidate for a given input language phrase. However, despite evidence that rich context features are useful in stand-alone translation disambiguation tasks, recent studies reported that incorporating context-rich approaches from Word Sense Disambiguation (WSD) metho...
متن کاملWord Representations in Factored Neural Machine Translation
Translation into a morphologically rich language requires a large output vocabulary to model various morphological phenomena, which is a challenge for neural machine translation architectures. To address this issue, the present paper investigates the impact of having two output factors with a system able to generate separately two distinct representations of the target words. Within this framew...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Speech & Language
سال: 2017
ISSN: 0885-2308
DOI: 10.1016/j.csl.2017.01.007