Vera Davydova


PAUQ: Text-to-SQL in Russian
Daria Bakshandaeva | Oleg Somov | Ekaterina Dmitrieva | Vera Davydova | Elena Tutubalina
Findings of the Association for Computational Linguistics: EMNLP 2022

Semantic parsing is an important task that allows to democratize human-computer interaction. One of the most popular text-to-SQL datasets with complex and diverse natural language (NL) questions and SQL queries is Spider. We construct and complement a Spider dataset for Russian, thus creating the first publicly available text-to-SQL dataset for this language. While examining its components - NL questions, SQL queries and databases content - we identify limitations of the existing database structure, fill out missing values for tables and add new requests for underrepresented categories. We select thirty functional test sets with different features that can be used for the evaluation of neural models’ abilities. To conduct the experiments, we adapt baseline architectures RAT-SQL and BRIDGE and provide in-depth query component analysis. On the target language, both models demonstrate strong results with monolingual training and improved accuracy in multilingual scenario. In this paper, we also study trade-offs between machine-translated and manually-created NL queries. At present, Russian text-to-SQL is lacking in datasets as well as trained models, and we view this work as an important step towards filling this gap.

SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19
Vera Davydova | Elena Tutubalina
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This paper is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field.

Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022
Davy Weissenbacher | Juan Banda | Vera Davydova | Darryl Estrada Zavala | Luis Gasco Sánchez | Yao Ge | Yuting Guo | Ari Klein | Martin Krallinger | Mathias Leddin | Arjun Magge | Raul Rodriguez-Esteban | Abeed Sarker | Lucia Schmidt | Elena Tutubalina | Graciela Gonzalez-Hernandez
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

For the past seven years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content. This seventh iteration consists of ten tasks that include English and Spanish posts on Twitter, Reddit, and WebMD. Interest in the #SMM4H shared tasks continues to grow, with 117 teams that registered and 54 teams that participated in at least one task—a 17.5% and 35% increase in registration and participation, respectively, over the last iteration. This paper provides an overview of the tasks and participants’ systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.