039 – Lena Voita – NLP

Lena Voita is a Ph.D. student at the University of Edinburgh and University of Amsterdam. Previously, She was a research scientist at Yandex Research and worked closely with the Yandex Translate team. She still teaches NLP at the Yandex School of Data Analysis. She has created an exciting new NLP course on her website lena-voita.github.io which you folks need to check out! She has one of the most well presented blogs we have ever seen, where she discusses her research in an easily digestible manner. Lena has been investigating many fascinating topics in machine learning and NLP. Today we are going to talk about three of her papers and corresponding blog articles;

Source and Target Contributions to NMT Predictions — Where she talks about the influential dichotomy between the source and the prefix of neural translation models.
https://arxiv.org/pdf/2010.10907.pdf
https://lena-voita.github.io/posts/source_target_contributions_to_nmt.html

Information-Theoretic Probing with MDL — Where Lena proposes a technique of evaluating a model using the minimum description length or Kolmogorov complexity of labels given representations rather than something basic like accuracy
https://arxiv.org/pdf/2003.12298.pdf
https://lena-voita.github.io/posts/mdl_probes.html

Evolution of Representations in the Transformer – Lena investigates the evolution of representations of individual tokens in Transformers — trained with different training objectives (MT, LM, MLM)
https://arxiv.org/abs/1909.01380
https://lena-voita.github.io/posts/emnlp19_evolution.html

Panel Dr. Tim Scarfe, Yannic Kilcher, Sayak Paul

00:00:00 Kenneth Stanley / Greatness can not be planned house keeping
00:21:09 Kilcher intro
00:28:54 Hello Lena
00:29:21 Tim – Lenas NMT paper
00:35:26 Tim – Minimum Description Length / Probe paper
00:40:12 Tim – Evolution of representations
00:46:40 Lenas NLP course
00:49:18 The peppermint tea situation
00:49:28 Main Show Kick Off
00:50:22 Hallucination vs exposure bias
00:53:04 Lenas focus on explaining the models not SOTA chasing
00:56:34 Probes paper and NLP intepretability
01:02:18 Why standard probing doesnt work
01:12:12 Evolutions of representations paper
01:23:53 BERTScore and BERT Rediscovers the Classical NLP Pipeline paper
01:25:10 Is the shifting encoding context because of BERT bidirectionality
01:26:43 Objective defines which information we lose on input
01:27:59 How influential is the dataset?
01:29:42 Where is the community going wrong?
01:31:55 Thoughts on GOFAI/Understanding in NLP?
01:36:38 Lena’s NLP course
01:47:40 How to foster better learning / understanding
01:52:17 Lena’s toolset and languages
01:54:12 Mathematics is all you need
01:56:03 Programming languages

https://lena-voita.github.io/
https://www.linkedin.com/in/elena-voita/
https://scholar.google.com/citations?user=EcN9o7kAAAAJ&hl=ja

Pod version: https://anchor.fm/machinelearningstreettalk/episodes/039—Lena-Voita—NLP-epcj2q

Erata: CPPNs are “*compositional* pattern producing networks” — Tim accidently said “convolutional instead of compositional”

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