More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing

Skeppstedt, Maria; Peldszus, Andreas; Stede, Manfred

doi:10.18653/v1/w18-5218

Cited by 15 publications

(15 citation statements)

References 13 publications

(13 reference statements)

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“…The volume of data, particularly data annotated at the most fine grained level, is still far below what would be required to apply many of the techniques previously discussed in a domain independent manner. Attempts are being made to overcome this lack of data, including the use of crowdsourced annotation (Ghosh et al 2014;Skeppstedt, Peldszus, and Stede 2018) and automatic methods to extend the data currently annotated (Bilu, Hershcovich, and Slonim 2015). As these efforts combine with increasing attention to manual analysis, the volume of data available should increase rapidly.…”

Section: Discussionmentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

281

186

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Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

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Section: Discussionmentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

281

186

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“…Fishcheva and Kotelnikov [7] showed that the machine translation of the English-language Argumentative Microtext Corpus (ArgMicro) [24], [27] into Russian allows obtaining the performance of ADUs classification that is not inferior to human translation. In this paper, following [7], we investigate the possibility of improving the performance of ADUs classification based on the extension of the Russian version of the ArgMicro corpus through machine translation of the Persuasive Essays Corpus (PersEssays) [29].…”

Section: Introductionmentioning

confidence: 99%

“…Materials and Methods4.1 Text corporaWithin this study, we used the Argumentative Microtext Corpus (ArgMicro)[24],[27] and the Persuasive Essay Corpus (PersEssays)[29]. Fishcheva and Kotelnikov[7] showed that the best result among…”

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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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“…We noticed that in many cases, the major claim is restated in the conclusion section of an essay, summing up the entire argument. Skeppstedt et al (2018) also noticed this and coined the name RESTATEMENT to model this phenomenon. In our scheme, the RESTATEMENT relation holds between two sentences if the second one repeats high-level argument material that has been previously described by the first, without adding a new idea into the discourse.…”

Section: Restatementmentioning

confidence: 96%

Annotating argumentative structure in English-as-a-Foreign-Language learner essays

2021

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Argument mining (AM) aims to explain how individual argumentative discourse units (e.g. sentences or clauses) relate to each other and what roles they play in the overall argumentation. The automatic recognition of argumentative structure is attractive as it benefits various downstream tasks, such as text assessment, text generation, text improvement, and summarization. Existing studies focused on analyzing well-written texts provided by proficient authors. However, most English speakers in the world are non-native, and their texts are often poorly structured, particularly if they are still in the learning phase. Yet, there is no specific prior study on argumentative structure in non-native texts. In this article, we present the first corpus containing argumentative structure annotation for English-as-a-foreign-language (EFL) essays, together with a specially designed annotation scheme. The annotated corpus resulting from this work is called “ICNALE-AS” and contains 434 essays written by EFL learners from various Asian countries. The corpus presented here is particularly useful for the education domain. On the basis of the analysis of argumentation-related problems in EFL essays, educators can formulate ways to improve them so that they more closely resemble native-level productions. Our argument annotation scheme is demonstrably stable, achieving good inter-annotator agreement and near-perfect intra-annotator agreement. We also propose a set of novel document-level agreement metrics that are able to quantify structural agreement from various argumentation aspects, thus providing a more holistic analysis of the quality of the argumentative structure annotation. The metrics are evaluated in a crowd-sourced meta-evaluation experiment, achieving moderate to good correlation with human judgments.

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More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing

Cited by 15 publications

References 13 publications

Argument Mining: A Survey

Argument Mining: A Survey

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

Annotating argumentative structure in English-as-a-Foreign-Language learner essays

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