Neural Machine Translation model for University Email Application

• URL: http://arxiv.org/abs/2007.16011v1
• Date: Mon, 20 Jul 2020 15:05:16 GMT
• Title: Neural Machine Translation model for University Email Application
• Authors: Sandhya Aneja and Siti Nur Afikah Bte Abdul Mazid and Nagender Aneja
• Abstract summary: A state-of-the-art Sequence-to-Sequence Neural Network for ML -> EN and EN -> ML translations is compared with Google Translate. The low BLEU score of Google Translation indicates that the application based regional models are better.
• Score: 1.4731169524644787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML -> EN and EN -> ML translations is compared with Google Translate using Gated Recurrent Unit Recurrent Neural Network machine translation model with attention decoder. The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better. The low BLEU score of EN -> ML of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.

Related papers

• The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation [53.907805815477126]
  This paper presents the first relatively large-scale Amharic-English parallel sentence dataset. We build bi-directional Amharic-English translation models by fine-tuning the existing Facebook M2M100 pre-trained model. The results show that the normalization of Amharic homophone characters increases the performance of Amharic-English machine translation in both directions.
  arXiv  Detail & Related papers  (2022-10-27T07:18:53Z)
• Improving Multilingual Neural Machine Translation System for Indic Languages [0.0]
  We propose a multilingual neural machine translation (MNMT) system to address the issues related to low-resource language translation. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models.
  arXiv  Detail & Related papers  (2022-09-27T09:51:56Z)
• The first neural machine translation system for the Erzya language [0.0951828574518325]
  We present the first neural machine translation system for translation between the endangered Erzya language and Russian. The BLEU scores are 17 and 19 for translation to Erzya and Russian respectively, and more than half of the translations are rated as acceptable by native speakers. We release the translation models along with the collected text corpus, a new language identification model, and a multilingual sentence encoder adapted for the Erzya language.
  arXiv  Detail & Related papers  (2022-09-19T22:21:37Z)
• Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag [1.1470070927586016]
  Most people in Sri Lanka are unable to read and understand English properly. There is a huge requirement of translating English content to local languages to share information among locals.
  arXiv  Detail & Related papers  (2022-02-17T19:45:50Z)
• Improving Multilingual Translation by Representation and Gradient Regularization [82.42760103045083]
  We propose a joint approach to regularize NMT models at both representation-level and gradient-level. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance.
  arXiv  Detail & Related papers  (2021-09-10T10:52:21Z)
• ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback [70.5469946314539]
  ChrEnTranslate is an online machine translation demonstration system for translation between English and an endangered language Cherokee. It supports both statistical and neural translation models as well as provides quality estimation to inform users of reliability.
  arXiv  Detail & Related papers  (2021-07-30T17:58:54Z)
• Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings [72.69253034282035]
  We exploit a language independent multilingual sentence representation to easily generalize to a new language. Blindly decoding from Portuguese using a basesystem containing several Romance languages we achieve scores of 36.4 BLEU for Portuguese-English and 12.8 BLEU for Russian-English. We explore a more practical adaptation approach through non-iterative backtranslation, exploiting our model's ability to produce high quality translations.
  arXiv  Detail & Related papers  (2021-03-11T14:22:08Z)
• Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT [129.99918589405675]
  We present an effective approach that reuses an LM that is pretrained only on the high-resource language. The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model. Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq)
  arXiv  Detail & Related papers  (2020-09-16T11:37:10Z)
• HausaMT v1.0: Towards English-Hausa Neural Machine Translation [0.012691047660244334]
  We build a baseline model for English-Hausa machine translation. The Hausa language is the second largest Afro-Asiatic language in the world after Arabic.
  arXiv  Detail & Related papers  (2020-06-09T02:08:03Z)
• It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information [90.35685796083563]
  Cross-mutual information (XMI) is an asymmetric information-theoretic metric of machine translation difficulty. XMI exploits the probabilistic nature of most neural machine translation models. We present the first systematic and controlled study of cross-lingual translation difficulties using modern neural translation systems.
  arXiv  Detail & Related papers  (2020-05-05T17:38:48Z)
• Neural Machine Translation for Low-Resourced Indian Languages [4.726777092009554]
  Machine translation is an effective approach to convert text to a different language without any human involvement. In this paper, we have applied NMT on two of the most morphological rich Indian languages, i.e. English-Tamil and English-Malayalam. We proposed a novel NMT model using Multihead self-attention along with pre-trained Byte-Pair-Encoded (BPE) and MultiBPE embeddings to develop an efficient translation system.
  arXiv  Detail & Related papers  (2020-04-19T17:29:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.