Convolutional over Recurrent Encoder for Neural Machine Translation
نویسندگان
چکیده
منابع مشابه
Convolutional over Recurrent Encoder for Neural Machine Translation
Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN) called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the ...
متن کاملA Convolutional Encoder Model for Neural Machine Translation
The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT’16 EnglishRomanian translation w...
متن کاملMulti-channel Encoder for Neural Machine Translation
Attention-based Encoder-Decoder has the effective architecture for neural machine translation (NMT), which typically relies on recurrent neural networks (RNN) to build the blocks that will be lately called by attentive reader during the decoding process. This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN. On the...
متن کاملRecurrent Neural Machine Translation
The vanilla attention-based neural machine translation has achieved promising performance because of its capability in leveraging varying-length source annotations. However, this model still suffers from failures in long sentence translation, for its incapability in capturing long-term dependencies. In this paper, we propose a novel recurrent neural machine translation (RNMT), which not only pr...
متن کاملConvolutional Encoders for Neural Machine Translation
We propose a general Convolutional Neural Network (CNN) encoder model for machine translation that fits within in the framework of Encoder-Decoder models proposed by Cho, et. al. [1]. A CNN takes as input a sentence in the source language, performs multiple convolution and pooling operations, and uses a fully connected layer to produce a fixed-length encoding of the sentence as input to a Recur...
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: The Prague Bulletin of Mathematical Linguistics
سال: 2017
ISSN: 1804-0462
DOI: 10.1515/pralin-2017-0007