Keynote lectures
Societal Challenges for Information Retrieval
Benno Stein
Biography
Benno Stein is chair of the Web-Technology and Information Systems Group at the Bauhaus-Universität Weimar. His research focuses on modeling and solving data- and knowledge-intensive information processing tasks. Common ground of his research are the principles and methods of symbolic Artificial Intelligence. Benno Stein has developed theories, algorithms, and tools for information retrieval, data mining, computational linguistics, knowledge processing, as well as for engineering design and simulation (patents granted). For several achievements of his research he has been awarded with scientific and commercial prizes.

Professional background: Study at the University of Karlsruhe (1984 - 1989). Dissertation (1995) and Habilitation (2002) in computer science at the University of Paderborn. Appointment as a full professor for Web Technology and Information Systems at the Bauhaus-Universität Weimar (2005). Research stays at IBM, Germany, and the International Computer Science Institute, Berkeley. Benno Stein serves on scientific boards, on program committees, as reviewer in various relevant conferences and journals, and he is the initiator and a co-chair of PAN, an excellence network and evaluation lab on digital text forensics with focus on authorship analysis, profiling, and reuse detection. He is cofounder and spokesperson of the Digital Bauhaus Lab Weimar, a visionary and interdisciplinary research center for Computer Science, Arts, and Engineering. Not least, he is a cofounder (1996) and scientific director of the Art Systems Software Ltd, a world leading company for simulation technology in fluidic engineering.
Tutorials
Automatic Text Simplification
Dr. Sanja Štajner

Biography
Sanja Štajner is currently a postdoctoral research fellow at the University of Mannheim, Germany. She holds a multiple Masters degree in Natural Language Processing and Human Language Technologies (Autonomous University of Barcelona, Spain and University of Wolverhampton, UK) and the PhD degree in Computer Science from the University of Wolverhampton on the topic of "Data-driven Text Simplification". She participated in Simplext and FIRST projects on automatic text simplification, and is the lead author of four ACL papers on text simplification (including the first neural text simplification system) and numerous other papers on the topics of text simplification and readability assessment at various leading international conferences and journals.

Sanja regularly teaches NLP at Masters and PhD levels, delivers invited talks and seminars at various universities and companies. She held a tutorial on "Deep Learning for Text Simplification" at RANLP 2017, and tutorial on "Data-Driven Text Simplification" at COLING 2018. She is an area chair for COLING 2018, and regular program committee member of ACL, EMNLP, LREC, IJCAI, IAAA and other international conferences and journals. She was a lead organizer of the first international workshop and shared task on Quality Assessment of Text Simplification (QATS) in 2016, and the Complex Word Identification shared task in 2018.
Paper Presentations
Avoiding Echo-Responses in a Retrieval-Based Conversation System
Denis Fedorenko, Nikita Smetanin and Artem Rodichev
Supervised Mover's Distance: A simple model for sentence comparison
Muktabh Mayank Srivastava
Direct-Bridge Combination Scenario for Persian-Spanish Minimal Parallel-Resource Statistical Machine Translation
Benyamin Ahmadnia, Javier Serrano, Gholamreza Haffari and Nik-Mohammad Balouchzahi
Deep Convolutional Networks for Supervised Morpheme Segmentation of Russian Language
Alexey Sorokin and Anastasia Kravtsova
Cleaning up after a Party: Post-processing Thesaurus Crowdsourced Data
Oksana Antropova, Elena Arslanova, Maxim Shaposhnikov, Pavel Braslavski and Mihail Mukhin
A model-free comorbidities-based events prediction in ICU unit
Tatiana Malygina and Ivan Drokin
Profiling the age of Russian bloggers
Tatiana Litvinova, Alexandr Sboev and Polina Panicheva
A comparative study of publicly available Russian sentiment lexicons
Evgeny Kotelnikov, Tatiana Peskisheva, Anastasia Kotelnikova and Elena Razova
Smart Context Generation for Disambiguation to Wikipedia
Andrey Sysoev and Irina Nikishina
Acoustic Features of Speech of Typically Developing Children Aged 5-16 Years
Aleksey Grigorev, Olga Frolova and Elena Lyakso
Stierlitz Meets SVM: Humor Detection in Russian
Anton Ermilov, Natasha Murashkina, Valeria Goryacheva and Pavel Braslavski
Interractive Attention Network for Adverse Drug Reaction Classification
Ilseyar Alimova and Valery Solovyev
Explicit Semantic Analysis as a Means for Topic Labelling
Anna Kriukova, Aliia Erofeeva, Olga Mitrofanova and Kirill Sukharev
A Multi-Feature Classifier for Verbal Metaphor Identification in Russian Texts
Yulia Badryzlova and Polina Panicheva
Lemmatization for ancient languages: rules or neural networks?
Oksana Dereza
Automatic mining of discourse connectives for Russian
Svetlana Toldova, Dina Pisarevskaya and Maria Koboseva
Four Keys to Topic Interpretability in Topic Modeling
Andrey Mavrin, Andrey Filchenkov and Sergei Koltcov
Named Entity Recognition in Russian with Word Representation Learned by a Bidirectional Language Model
Georgy Konoplich, Evgeny Putin, Andrey Filchenkov and Roman Rybka
Modeling Propaganda Battle: Decision-Making, Homophily and Echo Chambers
Alexander Petrov and Olga Proncheva
poster and demo session
to be announced
industrial session
ML problems in crowdsoursing platform Yandex.Toloka.
Vladimir Kukushkin, Yandex

Crowdsourcing platform is a two-sided market where customers and workers look up each other. Customers place their tasks (usually related to machine learning purposes, such as collecting ground-truth labels for their datasets), workers do these tasks gaining money. As a platform, we should regulate their relationships effectively, increasing satisfaction of the both sides. Generally, there are many examples of two-sided markets: Uber, booking.com, AirBnB, etc. But crowdsourcing market has its own specific features: online job, low payments, low entry barrier for users and high entry barrier for customers and many others. The speech is devoted to machine learning problems we face with every day.
to be announced
Feel free to contact us at ainlevent@gmail.com