机器翻译 | Improving Neural Machine Translation Models with Monolingual Data论文翻译 题目 基于单语数据的神经机器翻译模型改进 摘要 神经机器翻译(NMT)在仅使用并行数据进行训练的情况下,在几种语言对上取得了最先进的表现。目标侧单语数据在提高基于短语的统计机器翻译的流畅性方面起着重要作用,我们研究了单语数据...
原文地址: Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ 翻译这篇帖子一方面是为了记录自己的学习过程,强迫自己认真读帖,另一方面有关这篇帖子的翻译(包括作者推...
^Mengzhou Xia, Guoping Huang, Lemao Liu, and Shuming Shi. 2019. Graph based translation mem- ory for neural machine translation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 7297–7304. ^Improving neural machine translation models with monolingual datahttps://...
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language BribriThis paper presents a neural machine translation model and dataset for the Chibchan language Bribri, with an average performance of BLEU 16.9卤1.7. This was trained on an extremely small ...
Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this p...
Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) 可视化讲解神经机器翻译模型(基于注意力机制的Seq2Seq模型) Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image...
Neuralmachranslationmodelsusuallyusetheencoder-decoderframeworkand generatetranslationfromlefttoright(orrighttoleft)withoutfullyutilizingthe target-sideglobalinformation.Afewrecentapproachesseektoexploittheglobal informationthroughtwo-passdecoding,yethavelimitationsintranslationquality ...
目标端monolingual数据在提升NMT流畅度上有着重要的作用。 通过将monoligual训练数据和back-translation的数据进行配对,我们可以将其作为额外训练平行语料库数据,从而提升NMT的翻译性能。 NMT Training with Monolingual Training Data 两种做法: providing monolingual training examples with an empty(dummy) source sentence....
Almost all neural machine translation models employ the encoder-decoder framework (Cho et al., 2014a). The encoder-decoder framework consists of four basic components: the embedding layers, the encoder and decoder networks, and the classification layer. Fig. 1 shows a typical autoregressive NMT mo...
GPT2:CarryMeRookie:大模型系列论文 GPT2: Language Models are Unsupervised Multitask Learners Sequence2Sequence:CarryMeRookie:论文阅读:Sequence to Sequence Learning with Neural Networks Neural Machine Translation by Jointly Learning to Align and Translate ...