ELEC-E5550 - Statistical Natural Language Processing D, 12.01.2021-14.04.2021
Kurssiasetusten perusteella kurssi on päättynyt 14.04.2021 Etsi kursseja: ELEC-E5550
Osion kuvaus
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Lecture schedule 2021:
- 12 Jan 1 Introduction & Project groups / Mikko Kurimo
- 19 jan 2 Statistical language models / Mikko Kurimo
- 26 jan 3 Word2vec / Tiina Lindh-Knuutila
- 02 feb 4 Sentence level processing / Mikko Kurimo
- 09 feb 5 Speech recognition / Janne Pylkkönen
- 16 feb 6 Chatbots and dialogue agents / Mikko Kurimo
- 23 feb Exam week, no lecture
- 02 mar 7 Statistical machine translation / Jaakko Väyrynen
- 09 mar 8 Morpheme-level processing / Mathias Creutz
- 16 mar 9 Neural language modeling and BERT / Mittul Singh
- 23 mar 10 Neural machine translation / Stig-Arne Grönroos
- 30 mar 11 Societal impacts and course conclusion / Krista Lagus and Mikko Kurimo
Below you can find slides of 2020 lectures until they are substituted by 2021 ones as the course progresses. Lecture recordings will also be added here.
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Zoom link to participate in the lectures Verkko-osoite
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NEW Drive link to view the recorded lectures Verkko-osoite
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OLD Drive link to view the recorded lectures Verkko-osoite
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- statistical language models and their applications
- maximum likelihood estimation of n-grams
- class-based n-grams
- the main smoothing methods for n-grams
- introduction to other statistical and neural language models
Lecture 2 in the course text books:
- Manning-Schutze: Chapter 6 pp. 191-228
- Jurafsky-Martin 3rd (online) edition: Chapter 3 pp. 37-62 (and Chapter 7 pp.131-150 for simple NNLMs)
- statistical language models and their applications
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List as many potential applications for statistical language models as you can!
Typically they are tasks where you need the probability or to find the most probable word or sentence given some background informationPlease type or upload your answer here, e.g. as a photo, text or pdf file and earn a lecture activity point.
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Please type or upload your answer here, e.g. as a photo, text or pdf file and earn a lecture activity point.
Discuss with each other in breakout rooms and propose answers for these 3 questions:
1. Finish the POS tagging by Viterbi search example by hand.
- Return the values of the boxes and the final tag sequence. Either take a photo of your drawing, fill in the given ppt, or just type the values into the text box
2. Did everyone get the same tags? Is the result correct? Why / why not?
3. What are the pros and cons of HMM tagger?
All submissions, even incorrect or incomplete ones, will be awarded by one activity point. -
Please type or upload your answer here, e.g. as a photo, text or pdf file and earn a lecture activity point.
Discuss with each other in breakout rooms and propose answers for these questions:
Think about an application where ASR would be useful, but where
it is not yet commonly used. How would ASR change the user
experience? What are the biggest challenges for ASR in that use
case?All submissions, even incorrect or incomplete ones, will be awarded by one activity point.
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Please type or upload your answer here, e.g. as a photo, text or pdf file and earn a lecture activity point.
Discuss with each other in breakout rooms and propose answers for these 6 questions:
- Which chatbots and dialogue agents have you used? What can they do, what not?
- Try ELIZA, e.g. https://www.eclecticenergies.com/ego/eliza or http://psych.fullerton.edu/mbirnbaum/psych101/Eliza.htm When does it fail? How to improve it?
- Try PARRY, e.g. https://www.chatbots.org/chatbot/parry/ or https://www.botlibre.com/browse?id=857177 When does it fail? How to improve it?
- Try more chatbots or dialogue agents, e.g. transformer: https://convai.huggingface.co/ or anyone from: https://www.chatbots.org/
- What do you think: How to make better chatbots? How to automatically evaluate chatbots?
- What ethical issues do chatbots have? Any suggestions how to solve them?
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Lecture based on:
- Chapter 13.2-13.4 in Manning & Schutze
- Chapter 21 in the OLD Jurafsky & Martin: Speech and Language Processing
- Chapter 11 in the NEW Jurafsky & Martin: Speech and Language Processing
- Koehn: "Statistical Machine Translation", http://www.statmt.org/book/
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Please type or upload your answer here, e.g. as a photo, text or pdf file and earn a lecture activity point.
Discuss with each other in breakout rooms and propose answers for these 2 questions:
Consider different levels of language and different kinds of source-target pairs:
- What would be easy/hard to translate with MT?
- Have you seen failed/succesful usage or applications of MT?
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Discuss with group:
1. Do you think it is possible to detect speaker’s emotions from text? Explain!
2. What good might there be, if we could create a “WORRY-O-METER”?
3. What problems do you foresee?Please type or upload your answer here, e.g. as a photo, text or pdf file and earn a lecture activity point.
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Try out Medicine Radar at: Laaketutka.fi E.g.
- search for a medicine you know
- Look at the list of other medicines, other symptoms, typical dosages. Is this what you would expect?
Discuss:
1. What is interesting about this prototype?
2. What could it be useful for?
3. Come up with questions & share them with your group!Please type or upload your answer here, e.g. as a photo, text or pdf file and earn a lecture activity point.