Identification of connected arguments based on reasoning schemes “from expert opinion”

Salomatina, Natalia V.; Kononenko, Irina; Sidorova, Elena A.; Pimenov, Ivan

doi:10.1088/1742-6596/1715/1/012013

Cited by 5 publications

(5 citation statements)

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“…Salomatina et al [26] described a combined approach to partial extraction of the argumentative structure of text, which can be used if there are no sufficient annotated data to effectively apply machine learning techniques for the direct detection of arguments and their relationships.…”

Section: Argumentation Mining In Russianmentioning

confidence: 99%

Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

Fishcheva¹,

Goloviznina²,

Kotelnikov³

2021

Computational Linguistics and Intellectual Technologies

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Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentative structure. A significant obstacle to research in this area for the Russian language is the lack of annotated Russian-language text corpora. This article explores the possibility of improving the quality of argumentation mining using the extension of the Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based on the machine translation of the Persuasive Essays Corpus (PersEssays). To make it possible to use these two corpora combined, we propose a Joint Argument Annotation Scheme based on the schemes used in ArgMicro and PersEssays. We solve the problem of classifying argumentative discourse units (ADUs) into two classes -"pro" ("for") and "opp" ("against") using traditional machine learning techniques (SVM, Bagging and XGBoost) and a deep neural network (BERT model). An ensemble of XGBoost and BERT models was proposed, which showed the highest performance of ADUs classification for both corpora.

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Section: Argumentation Mining In Russianmentioning

confidence: 99%

Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

Fishcheva¹,

Goloviznina²,

Kotelnikov³

2021

Computational Linguistics and Intellectual Technologies

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“…Salomatina et al (Salomatina et al, 2021) propose an approach to the partial extraction of the argumentative structure of a text by using patterns of argumentation indicators. They also try to recognize the relations between extracted arguments.…”

Section: Previous Workmentioning

confidence: 99%

RuArg-2022: Argument Mining Evaluation

Kotelnikov,

Loukachevitch,

Nikishina

et al. 2022

Preprint

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Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argumentation structure of texts. This paper is a report of the organizers on the first competition of argumentation analysis systems dealing with Russian language texts within the framework of the Dialogue conference. During the competition, the participants were offered two tasks: stance detection and argument classification. A corpus containing 9,550 sentences (comments on social media posts) on three topics related to the COVID-19 pandemic (vaccination, quarantine, and wearing masks) was prepared, annotated, and used for training and testing. The system that won the first place in both tasks used the NLI (Natural Language Inference) variant of the BERT architecture, automatic translation into English to apply a specialized BERT model, retrained on Twitter posts discussing COVID-19, as well as additional masking of target entities. This system showed the following results: for the stance detection task an F1-score of 0.6968, for the argument classification task an F1-score of 0.7404. We hope that the prepared dataset and baselines will help to foster further research on argument mining for the Russian language.

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“…Salomatina et al (Salomatina et al, 2021) proposed a method for finding an argumentative structure based on using the patterns of argumentation indicators and their role in the thematic structure of the text. This method can be used in the absence of a sufficient amount of annotated data.…”

Section: Argumentation Mining In Russianmentioning

confidence: 99%

“…There are several publicly available pre-trained language models for the Russian language, including RuBERT (Kuratov and Arkhipov, 2019), SBERT (SBERT, 2020), andruGPT-3 (ruGPT-3, 2020). Recently, several papers have appeared on the argument mining in Russian (Fishcheva and Kotelnikov, 2019;Fishcheva et al, 2021;Salomatina et al, 2021;Ilina et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Argumentative Text Generation in Economic Domain

Fishcheva¹,

Osadchiy²,

Bochenina³

et al. 2022

Computational Linguistics and Intellectual Technologies

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The development of large and super-large language models, such as GPT-3, T5, Switch Transformer, ERNIE, etc., has significantly improved the performance of text generation. One of the important research directions in this area is the generation of texts with arguments. The solution of this problem can be used in business meetings, political debates, dialogue systems, for preparation of student essays. One of the main domains for these applications is the economic sphere. The key problem of the argument text generation for the Russian language is the lack of annotated argumentation corpora. In this paper, we use translated versions of the Argumentative Microtext, Persuasive Essays and UKP Sentential corpora to fine-tune RuBERT model. Further, this model is used to annotate the corpus of economic news by argumentation. Then the annotated corpus is employed to fine-tune the ruGPT-3 model, which generates argument texts. The results show that this approach improves the accuracy of the argument generation by more than 20 percentage points (63.2% vs. 42.5%) compared to the original ruGPT-3 model.

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Identification of connected arguments based on reasoning schemes “from expert opinion”

Cited by 5 publications

References 10 publications

Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

RuArg-2022: Argument Mining Evaluation

Argumentative Text Generation in Economic Domain

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