Pavel Braslavski


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KazQAD: Kazakh Open-Domain Question Answering Dataset
Rustem Yeshpanov | Pavel Efimov | Leonid Boytsov | Ardak Shalkarbayuli | Pavel Braslavski
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

We introduce KazQAD—a Kazakh open-domain question answering (ODQA) dataset—that can be used in both reading comprehension and full ODQA settings, as well as for information retrieval experiments. KazQAD contains just under 6,000 unique questions with extracted short answers and nearly 12,000 passage-level relevance judgements. We use a combination of machine translation, Wikipedia search, and in-house manual annotation to ensure annotation efficiency and data quality. The questions come from two sources: translated items from the Natural Questions (NQ) dataset (only for training) and the original Kazakh Unified National Testing (UNT) exam (for development and testing). The accompanying text corpus contains more than 800,000 passages from the Kazakh Wikipedia. As a supplementary dataset, we release around 61,000 question-passage-answer triples from the NQ dataset that have been machine-translated into Kazakh. We develop baseline retrievers and readers that achieve reasonable scores in retrieval (NDCG10 = 0.389 MRR = 0.382), reading comprehension (EM = 38.5 F1 = 54.2), and full ODQA (EM = 17.8 F1 = 28.7) settings. Nevertheless, these results are substantially lower than state-of-the-art results for English QA collections, and we think that there should still be ample room for improvement. We also show that the current OpenAI’s ChatGPTv3.5 is not able to answer KazQAD test questions in the closed-book setting with acceptable quality. The dataset is freely available under the Creative Commons licence (CC BY-SA) at url


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Answer Candidate Type Selection: Text-To-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
Mikhail Salnikov | Maria Lysyuk | Pavel Braslavski | Anton Razzhigaev | Valentin A. Malykh | Alexander Panchenko
Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)

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You Told Me That Joke Twice: A Systematic Investigation of Transferability and Robustness of Humor Detection Models
Alexander Baranov | Vladimir Kniazhevsky | Pavel Braslavski
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

In this study, we focus on automatic humor detection, a highly relevant task for conversational AI. To date, there are several English datasets for this task, but little research on how models trained on them generalize and behave in the wild. To fill this gap, we carefully analyze existing datasets, train RoBERTa-based and Naïve Bayes classifiers on each of them, and test on the rest. Training and testing on the same dataset yields good results, but the transferability of the models varies widely. Models trained on datasets with jokes from different sources show better transferability, while the amount of training data has a smaller impact. The behavior of the models on out-of-domain data is unstable, suggesting that some of the models overfit, while others learn non-specific humor characteristics. An adversarial attack shows that models trained on pun datasets are less robust. We also evaluate the sense of humor of the chatGPT and Flan-UL2 models in a zero-shot scenario. The LLMs demonstrate competitive results on humor datasets and a more stable behavior on out-of-domain data. We believe that the obtained results will facilitate the development of new datasets and evaluation methodologies in the field of computational humor. We’ve made all the data from the study and the trained models publicly available at

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Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Mikhail Salnikov | Hai Le | Prateek Rajput | Irina Nikishina | Pavel Braslavski | Valentin Malykh | Alexander Panchenko
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

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A System for Answering Simple Questions in Multiple Languages
Anton Razzhigaev | Mikhail Salnikov | Valentin Malykh | Pavel Braslavski | Alexander Panchenko
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Our research focuses on the most prevalent type of queries— simple questions —exemplified by questions like “What is the capital of France?”. These questions reference an entity such as “France”, which is directly connected (one hop) to the answer entity “Paris” in the underlying knowledge graph (KG). We propose a multilingual Knowledge Graph Question Answering (KGQA) technique that orders potential responses based on the distance between the question’s text embeddings and the answer’s graph embeddings. A system incorporating this novel method is also described in our work. Through comprehensive experimentation using various English and multilingual datasets and two KGs — Freebase and Wikidata — we illustrate the comparative advantage of the proposed method across diverse KG embeddings and languages. This edge is apparent even against robust baseline systems, including seq2seq QA models, search-based solutions and intricate rule-based pipelines. Interestingly, our research underscores that even advanced AI systems like ChatGPT encounter difficulties when tasked with answering simple questions. This finding emphasizes the relevance and effectiveness of our approach, which consistently outperforms such systems. We are making the source code and trained models from our study publicly accessible to promote further advancements in multilingual KGQA.


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NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition
Amina Miftahova | Alexander Pugachev | Artem Skiba | Ekaterina Artemova | Tatiana Batura | Pavel Braslavski | Vladimir Ivanov
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper presents the two submissions of NamedEntityRangers Team to the MultiCoNER Shared Task, hosted at SemEval-2022. We evaluate two state-of-the-art approaches, of which both utilize pre-trained multi-lingual language models differently. The first approach follows the token classification schema, in which each token is assigned with a tag. The second approach follows a recent template-free paradigm, in which an encoder-decoder model translates the input sequence of words to a special output, encoding named entities with predefined labels. We utilize RemBERT and mT5 as backbone models for these two approaches, respectively. Our results show that the oldie but goodie token classification outperforms the template-free method by a wide margin. Our code is available at:

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Entity Linking over Nested Named Entities for Russian
Natalia Loukachevitch | Pavel Braslavski | Vladimir Ivanov | Tatiana Batura | Suresh Manandhar | Artem Shelmanov | Elena Tutubalina
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction. The NEREL collection is currently the largest Russian dataset annotated with entities and relations. It includes 933 news texts with annotation of 29 entity types and 49 relation types. The paper describes the main design principles behind NEREL’s entity linking annotation, provides its statistics, and reports evaluation results for several entity linking baselines. To date, 38,152 entity mentions in 933 documents are linked to Wikidata. The NEREL dataset is publicly available.

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CausalQA: A Benchmark for Causal Question Answering
Alexander Bondarenko | Magdalena Wolska | Stefan Heindorf | Lukas Blübaum | Axel-Cyrille Ngonga Ngomo | Benno Stein | Pavel Braslavski | Matthias Hagen | Martin Potthast
Proceedings of the 29th International Conference on Computational Linguistics

At least 5% of questions submitted to search engines ask about cause-effect relationships in some way. To support the development of tailored approaches that can answer such questions, we construct Webis-CausalQA-22, a benchmark corpus of 1.1 million causal questions with answers. We distinguish different types of causal questions using a novel typology derived from a data-driven, manual analysis of questions from ten large question answering (QA) datasets. Using high-precision lexical rules, we extract causal questions of each type from these datasets to create our corpus. As an initial baseline, the state-of-the-art QA model UnifiedQA achieves a ROUGE-L F1 score of 0.48 on our new benchmark.


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NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
Natalia Loukachevitch | Ekaterina Artemova | Tatiana Batura | Pavel Braslavski | Ilia Denisov | Vladimir Ivanov | Suresh Manandhar | Alexander Pugachev | Elena Tutubalina
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via


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Large Dataset and Language Model Fun-Tuning for Humor Recognition
Vladislav Blinov | Valeria Bolotova-Baranova | Pavel Braslavski
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents. We collected a dataset of jokes and funny dialogues in Russian from various online resources and complemented them carefully with unfunny texts with similar lexical properties. The dataset comprises of more than 300,000 short texts, which is significantly larger than any previous humor-related corpus. Manual annotation of 2,000 items proved the reliability of the corpus construction approach. Further, we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 on a test set, which constitutes a considerable gain over baseline methods. The dataset is freely available for research community.


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YARN: Spinning-in-Progress
Pavel Braslavski | Dmitry Ustalov | Mikhail Mukhin | Yuri Kiselev
Proceedings of the 8th Global WordNet Conference (GWC)

YARN (Yet Another RussNet), a project started in 2013, aims at creating a large open WordNet-like thesaurus for Russian by means of crowdsourcing. The first stage of the project was to create noun synsets. Currently, the resource comprises 48K+ word entries and 44K+ synsets. More than 200 people have taken part in assembling synsets throughout the project. The paper describes the linguistic, technical, and organizational principles of the project, as well as the evaluation results, lessons learned, and the future plans.


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A Spinning Wheel for YARN: User Interface for a Crowdsourced Thesaurus
Pavel Braslavski | Dmitry Ustalov | Mikhail Mukhin
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics


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English-to-Russian MT evaluation campaign
Pavel Braslavski | Alexander Beloborodov | Maxim Khalilov | Serge Sharoff
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Towards a Reference Corpus of Web Genres for the Evaluation of Genre Identification Systems
Georg Rehm | Marina Santini | Alexander Mehler | Pavel Braslavski | Rüdiger Gleim | Andrea Stubbe | Svetlana Symonenko | Mirko Tavosanis | Vedrana Vidulin
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We present initial results from an international and multi-disciplinary research collaboration that aims at the construction of a reference corpus of web genres. The primary application scenario for which we plan to build this resource is the automatic identification of web genres. Web genres are rather difficult to capture and to describe in their entirety, but we plan for the finished reference corpus to contain multi-level tags of the respective genre or genres a web document or a website instantiates. As the construction of such a corpus is by no means a trivial task, we discuss several alternatives that are, for the time being, mostly based on existing collections. Furthermore, we discuss a shared set of genre categories and a multi-purpose tool as two additional prerequisites for a reference corpus of web genres.