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Language Translator.pptx

  1. Language Translator Presented by: M. Chaitanya Kumar M. GangaVasudha Using machine learning
  2. Abstract LanguageTranslator with machine means automatic translation, It the field of Artificial Intelligence. It is computer program which is design to translate text from one language (source language) to another language (target language) without the help of human.The aim of LanguageTranslation is to provide a system that translate text of source language into target language and translation express the same meaning as it in source language
  3. Why Machine Translation • business: international trade, investment, contracts, finance • commerce: travel, purchase of foreign goods and services, customer support • media: accessing information via search, sharing information via social networks, localization of content and advertising • education: sharing of ideas, collaboration, translation of research papers • government: foreign relations, negotiation
  4. Building the pipeline 1.Pre-processing: load and examine data, cleaning, tokenization, padding 2.Modelling: build, train, and test the model 3.Prediction: generate specific translations of source to target language, and compare the output translations to the ground truth translations 4.Iteration: iterate on the model, experimenting with different architectures
  5. For implementing, we will create two RNN layer : One RNN layer will act as ‘encoder’: In this we give our source language sentence as an input. And other RNN layer will act as ‘decoder’: which will give us the output (translated sentence in target language)
  6. summary ▪ This is very interesting as well as a complex Machine Learning project. In this we have learned many new concepts and also about Recurrent neural networks (RNNs), LSTM layers, how to implement LSTM layers. We’ve also understood the concept behind encoder and decoder and creating RNN models . And after learning all this we have finally created a Language Translator which translates source language text to target language.
  7. References ▪ [1] M. Anand Kumar, B. Premjith, S. Shivkaran, B. Kavirajan, S. Rajendran and K. P. Soman, Overview of the shared task on ▪ machine translation in Indian languages (MTIL-2017), J. Intell. Syst. (2017). ▪ [2] M. Auli, M. Galley, C. Quirk and G. Zweig, Joint language and translation modeling with recurrent neural networks, in: ▪ EMNLP, vol. 3, Association of Computational Linguistics, Seattle, WA, USA, 2013. ▪ [3] S. Chandar AP, S. Lauly, H. Larochelle, M. Khapra, B. Ravindran, V. C. Raykar and A. Saha, An autoencoder approach to ▪ learning bilingual word representations, in: Advances in Neural Information Processing Systems, pp. 1853–1861, MIT Press, ▪ Cambridge, MA, USA, 2014. ▪ [4] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk and Y. Bengio, Learning phrase representa-
  8. Thank you