Com­pu­ta­tion­al Ar­gu­ment­a­tion

Course, master, summer 2020, L.079.05811

Lecture

• Instructor. Henning Wachsmuth
• Location. Tentatively entirely online (otherwise, in F1.110)
• First date. April 23, 2020 (latest date of video upload)
• Last date. July 16, 2020 (latest date of video upload)
• Time. Thursday, 1pm – 4pm c.t. (live chat, weekly except for holidays)

Tutorial

• Instructor. Milad Alshomary, Maximilian Spliethöver
• Location. Tentatively entirely online (otherwise, in F1.110)
• First date. April 29, 2020 (latest date of video upload)
• Last date. July 15, 2020 (latest date of video upload)
• Time. Wednesday, 11am – 1pm c.t. (live chat, in some weeks)

• This edtion of the course ended. For the latest version of the slides, see the 2021 edition and 2022 edition of the course.

Argumentation is an integral part of both professional and everyday communication. Whenever a topic or question is subject to controversy, people consider arguments to form opinions, to make decisions, or to convince others of a certain stance. In the last years, the computational analysis and synthesis of natural language argumentation has become an emerging research area, due to its importance for the next generation of web search engines and intelligent personal assistance. Based on fundamental techniques from natural language processing, computational argumentation ranges from the mining of arguments from natural language text, over the assessment of argumentation quality, to the retrieval of arguments in web search. 

The students learn both fundamentals from argumentation theory and state-of-the-art techniques from computational argumentation. The use of respective methods is practiced in assignments.

The course covered lectures on the following topics:

1. Introduction to computational argumentation

2. Basics of natural language processing

3. Basics of argumentation

4. Argument acquisition

5. Argument mining

6. Argument assessment 

7. Argument generation

8. Applications of computational argumentation

9. Conclusion

Four assignment sheets will be given, each with a sophisticated programming task related to the topics of the course.

Results can be submitted in groups of 1–4 students. We recommend not to submit alone, since the effort may be high otherwise.

All tutorial and assignment information will be handled via PANDA.

Details on tutorials and assignments in the slides with general organizational information and and in the respective tutorial slides.

Voluntary self-assessment

Under the following URL, you'll find a self-assessment test, which is designed to help decide whether this course is the right choice for you or not:

https://umfragen.uni-paderborn.de/index.php/387353?lang=en 

The test covers a selection of basics from linguistics, statistics, and machine learning, all of which are considered as prerequisites for the course (along with good programming skills).

The test is voluntary, fully anonymous, and will not affect admission to the course. It is only intended for one's own consideration. However, students that struggle with several questions are advised to expand their skills first and maybe rather register next year. 

Required course achievement 

There will be an individualized assessment test with questions on the basics mentioned above for the self-assessment test. The test needs to be passed in order to be allowed to take the exam. 

The test will be conducted after a few weeks. It will take place offline, i.e., each registered student gets a test and needs to submit the solutions within a few days.

Details in the slides with general organizational information.

Final examination

To pass the course, an oral exam has to be taken after the lecture time. The time periods for the exams are the second half of July and late September. 

All details are found in the organizational slides on exam dates and application.