Changes between Initial Version and Version 1 of en/NlpInPracticeCourse/2021/MachineTranslation


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Timestamp:
Aug 30, 2022, 10:39:47 AM (20 months ago)
Author:
Ales Horak
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copied from private/NlpInPracticeCourse/MachineTranslation

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  • en/NlpInPracticeCourse/2021/MachineTranslation

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     1= Machine translation =
     2
     3[[https://is.muni.cz/auth/predmet/fi/ia161|IA161]] [[en/NlpInPracticeCourse|NLP in Practice Course]], Course Guarantee: Aleš Horák
     4
     5Prepared by: Pavel Rychlý
     6
     7== State of the Art ==
     8
     9The Neural Machine Translation system are structured as Encoder-Decoder pair.
     10They are trained on parallel corpora, each training example is a pair of source sentence and a reference translation.
     11Big advances could be done by preparing cleaner data and feeding the network with the right order of sentences.
     12
     13
     14=== References ===
     15
     16 1. Alammar, Jay (2018). The Illustrated Transformer [Blog post]. Retrieved from https://jalammar.github.io/illustrated-transformer/
     17 1. Popel, Martin, et al. "Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals." Nature communications 11.1 (2020): 1-15.
     18 1. Thompson, Brian and Koehn, Philipp. "Vecalign: Improved Sentence Alignment in Linear Time and Space", Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019
     19
     20
     21== Practical Session ==
     22
     23===== Technical Requirements ====
     24
     25The task will proceed using Python notebook run in web browser in the Google Colaboratory environment.
     26
     27In case of running the codes in a local environment, the requirements are Python 3.6+, jupyter notebook.
     28
     29
     30=== Translation with a Sequence to Sequence Network and Attention ===
     31
     32Access [https://colab.research.google.com/drive/1t9y01lL6gPw8f9GU1phC5qlS8AqqdvS0?usp=sharing|Python notebook in the Google Colab environment].
     33
     34
     35OR
     36
     37download the notebook or plain python file from the shared notebook (File > Download) and run in your local environment.
     38
     39
     40Follow the notebook. Choose one of the task at the end of the notebook.
     41
     42==== upload ====
     43
     44Upload your modified notebook or python script with results to the homework vault (odevzdávárna).
     45