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NLU-11: Week 6

In the second half of the course, we will take a look in more depth at machine translation and at more applications of the techniques we've learned in the first half. This week, we will look evaluation of machine translation and generation, and how to model multilingual machine translation and question answering. 

This week's tutorial is on transformers:

Tutorial 2: Transformers

Coursework 2 is now released - please read the instructions carefully (the main file to download is cw2.zip, which contains all directories of the coursework)

Lab 3: to be released

Please start having a look at the lab for next week, esp. Section 1. This lab includes many important concepts that you will need for coursework 2 which will be released on Friday 1 March. 

 

SlidesLectureCourse Content

Evaluating  Translation and Generation

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16 (Birch)

Required reading:

  • Bleu: a method for automatic evaluation of machine translation, Papenini et al. (2002) (Section 1-3) 


Additional reading:

  • COMET: A neural framework for MT evaluation, Rei et al. (2020) (sections 1-2)
  • Bertscore: Evaluating text generation with bert. Zhang, Tianyi, et al. (2019) (sections 1-3)
  • ROUGE: A Package for Automatic Evaluation of Summaries, Chin-Yew Lin (2004)  (sections 1-4)
     
Machine Translation and Multilingual data17 (Birch)

Required reading:

  • Multilingual Denoising Pre-training for Neural Machine Translation  Liu et al. (2020) 

For interest, not require reading:

  • Survey of Low-Resource Machine Translation, Haddow et al. (2021) 
  • No Language Left Behind (Costa-jussà et al. 2023)  
Question Answering18 (Minervini)

Required reading:

  • Speech and Language Processing Ed. 3, Ch. 14 on QA 🙂

  • SQuAD: 100,000+ Questions for Machine Comprehension of Text, https://arxiv.org/abs/1606.05250 (SQuAD)

Optional reading:

  • Know What You Don’t Know: Unanswerable Questions for SQuAD, https://arxiv.org/abs/1806.03822 (SQuAD v2)

  • Bidirectional Attention Flow for Machine Comprehension, https://arxiv.org/abs/1611.01603 (BiDAF)

  • Natural Questions: A Benchmark for Question Answering Research, https://aclanthology.org/Q19-1026/ (NQ)

Additional reading:

  • The Bitter Lesson
     

  • Latent Retrieval for Weakly Supervised Open Domain Question Answering: https://arxiv.org/abs/1906.00300

     

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