• Course on most relevant tasks for building NLP applications.
• Allow to understand
  • When and how to apply NLP techniques in real-world scenario.
  • Not only the use pre-existing NLP libraries,
  • But be able to reimplement and adapt own models.
• Provide leads to explore and learn further
  • Master projects ideas welcome!
  • Open for collaborations hitz.eus and ixa.eus
• Regular sessions: 13 sessions of 150 minutes
  • Each session mixes theoretical and hands-on laboratories
• Extra session for the presentations!
• Material available in egela and google-drive
  • http://ixa2.si.ehu.eus/~jibloleo/nlpapp2
• Course is divided in three main parts:
  • Information Extraction systems (Oier Lopez de Lacalle) 6 sessions in total
  • Question Answering systems (Ander Barrena) 4 sessions in total
  • Recommender System and Conversational systems (Mikel Larrañaga) 3 sessions in total

1. Information Extraction (Oier)

   • Tuesday, March 15, 2022, 3:00 – 5:30pm
   • Tuesday, March 22, 2022, 3:00 – 5:30pm
   • Thursday, March 24, 2022,3:00 – 5:30pm
   • Thursday, March 31, 2022, 3:00 – 5:30pm
   • Tuesday, April 5, 2022, 3:00 – 5:30pm
   • Wednesday April 6, 2022, 3:00 – 5:30pm

2. Question Answering (Ander)

   • Thursday, April 7, 2022, 3:00 – 5:30pm
   • Monday, April 25, 2022, 3:00 – 5:30pm
   • Thursday, April 28, 2022, 3:00 – 5:30pm
   • Monday, May 2, 2022, 3:00 – 5:30pm

3. Conversational Systems (Mikel)

   • Wednesday, May 4, 2022, 3:00 – 5:30pm
   • Wednesday, May 11, 2022, 3:00 – 5:30pm
   • Monday, May 18, 2022, 3:00 – 5:30pm

• Laboratories are focused to put the theory in practice (no submission).
• You need to complete and submit 3 assignments.
  • Assignment 1. IE: Intent-classification and Slot-filling
  • Assignment 2. QA: QA+IR in open domain
  • Assignment 3. CS: Recommender system
  • Deadline for the assignments: 1st of June
• Main Project: on any open topic related to NLP application.
  • Do the implementation, write-up a technical report (~6 pages), present in class.
  • Presentations: 1st of June (to be confirmed)
  • Deadline for the final report: 8th of June (to be confirmed)

• Basic programming experience, university-level course in computer science, experience in Python. Basic math skills (algebra or pre-calculus), but not much!
• Knowledge about machine learning or deep learning is required.
• Laboratories:
  • Python (scikit-learn, pytorch, tensorflow…) using servers from Google Colaboratory
  • Time might be tight => auto-study / finish labs at home / ask for help to lecturers
  • Time might be plenty => review slides / do assignments

Class assignments: 50% of the grading

Final project: 50% of the grading

• Each group of student (2/3 people) will propose a subject for the final project to one of the lecturers, depending on his/her interests.
• Project proposal are due to May 19 (note that you will have 2 weeks for finishing!).
• The final project will be graded based on the written report, technicality and presentation, with the following percentages:
  • write-up 15%, including features like clarity, structure, background, references, discussion
  • technical 20%, incl. features like correctness and depth of the work
  • poster presentation 15%, including clarity, structure, discussion