Statistical Machine Translation Context Modelling with Recurrent Neural Network and LDA

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT.

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Bilingual recursive neural network based data selection for statistical machine translation

Knowledge-Based Systems ◽

10.1016/j.knosys.2016.05.003 ◽

2016 ◽

Vol 108 ◽

pp. 15-24 ◽

Cited By ~ 8

Author(s):

Derek F. Wong ◽

Yi Lu ◽

Lidia S. Chao

Keyword(s):

Neural Network ◽

Machine Translation ◽

Statistical Machine Translation ◽

Data Selection ◽

Selection For ◽

Recursive Neural Network

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Context based machine translation with recurrent neural network for English–Amharic translation

Machine Translation ◽

10.1007/s10590-021-09262-4 ◽

2021 ◽

Author(s):

Yeabsira Asefa Ashengo ◽

Rosa Tsegaye Aga ◽

Surafel Lemma Abebe

Keyword(s):

Neural Network ◽

Machine Translation ◽

Recurrent Neural Network

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Neural Machine Translation Using Sequence Modeling

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37687 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2153-2169

Author(s):

N Revathi

Keyword(s):

Neural Network ◽

Machine Translation ◽

Language Processing ◽

Recurrent Neural Network ◽

Deep Neural Network ◽

Short Term Memory ◽

Short Term ◽

Neural Machine Translation ◽

Term Memory ◽

Sequence Modeling

Abstract: Language is a main mode of communication, and translation is a critical tool for understanding information in a foreign language. Without the help of human translators, machine translation allows users to absorb unfamiliar linguistic material. The main goal of this project is to create a practical language translation from English to Hindi. Given its relevance and potential in the English-Hindi translation, machine translation is an efficient way to turn content into a new language without employing people. Among all available translation machines, Neural Machine Translation (NMT) is one of the most efficient ways. So, in this case, we're employing Sequence to Sequence Modeling, which includes the Recurrent Neural Network (RNN), Long and Short Term Memory (LSTM), and Encoder-Decoder methods. Deep Neural Network (DNN) comprehension and principles of deep learning, i.e. machine translation, are disclosed in the field of Natural Language Processing (NLP). In machine reclining techniques, DNN plays a crucial role. Keywords: Sequence to Sequence, Encoder-Decoder, Recurrent Neural Network, Long & Short term Memory, Deep Neural Network.

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