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CausalQA: A Benchmark for Causal Question Answering
P. 3296–3308.
Bondarenko A., Wolska M., Heindorf S., Blübaum L., Ngonga Ngomo A., Stein B., Braslavski P., Hagen M., Potthast M.
Language:
English
Keywords: question answering
Publication based on the results of:
In book
International Committee on Computational Linguistics, 2023.
Biryukova K., Chelnokova D., Erkenova J. et al., Communications in Computer and Information Science 2024 Vol. 2364 CCIS P. 109 – 121
Added: February 25, 2026
Galitsky B., Ilvovsky D., Goncharova E., , in: Artificial Intelligence. RCAI 2021. Lecture Notes in Computer ScienceVol. 12948.: Springer, 2021. P. 215–231.
Added: October 28, 2021
Glushkova T., Machnev A., Fenogenova A. et al., , in: Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected PapersVol. 12602.: Springer, 2021. P. 57–68.
Added: November 22, 2020
Smirnov D., Ilvovsky D., В кн.: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 17 июня — 20 июня 2020 г.). Доклады студенческой сессии.: [б.и.], 2020..
Modern question answering models can achieve near-human accuracy of answers for factual questions about a given piece of text in English. In the meantime, such models fail to achieve the same performance on datasets of question, which require some background information, not presented in the question context. This paper describes experimental evaluation of simple question ...
Added: September 16, 2020
Nikolaev K., Malafeev A., , in: Analysis of Images, Social Networks and Texts. 7th International Conference AIST 2018.: Springer, 2018. Ch. 12 P. 121–126.
This paper deals with automatic classification of questions in the Russian language. In contrast to previously used methods, we introduce a convolutional neural network for question classification. We took advantage of an existing corpus of 2008 questions, manually annotated in accordance with a pragmatic 14-class typology. We modified the data by reducing the typology to ...
Added: February 15, 2019
Nikolaev K., Malafeev A., , in: Analysis of Images, Social Networks and Texts. 6th International Conference, 2017, Revised Selected PapersVol. 10716.: Cham: Springer, 2018. Ch. 7 P. 72–81.
This paper deals with automatic classification of questions in the Russian language, a natural early step in building a question answering system. We developed a typology of Russian questions using interrogative particles, pronouns and word order as the main features. A corpus of 2008 questions was manually compiled and annotated according to our typology. We ...
Added: December 1, 2017
Galitsky B., Ilvovsky D., Kuznetsov S. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Бекасово, 29 мая - 2 июня 2013 г.). В 2-х т.Т. 1: Основная программа конференции. Вып. 12 (19).: М.: РГГУ, 2013. P. 239–255.
We develop a graph representation and learning technique for parse structures
for sentences and paragraphs of text. We introduce parse thicket
as a set of syntactic parse trees augmented by a number of arcs for intersentence
word-word relations such as coreference and taxonomies. These
arcs are also derived from other sources, including Rhetoric Structure and
Speech Act theory. We introduce ...
Added: November 1, 2013