#039 - Lena Voita - NLP
Show notes
ena 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 digestable 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 https://twitter.com/lena_voita
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