Overview of Touché 2023: Argument and Causal Retrieval

Bondarenko, Alexander; Fröbe, Maik; Kiesel, Johannes; Schlatt, Ferdinand; Barrière, Valentin; Ravenet, Brian; Hemamou, Léo; Luck, Simon; Reimer, Jan Heinrich; Stein, Benno; Potthast, Martin; Hagen, Matthias

doi:10.1007/978-3-031-28241-6_61

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We define dthe concept of intra-multilinguality, where the target and the comment can come in different languages, by using a platform that automatically translates the textual content so that the users can interact in their native languages. We collected and annotated parts of the dataset presented and made them available online for two shared tasks [59,60]. Finally, we proposed a series of methods to learn with a limited amount of labels, by pre-training over similar datasets and leveraging information from non-annotated data with the help of self-training methods.…”

Section: Discussionmentioning

confidence: 99%

Multilingual Multi-Target Stance Recognition in Online Public Consultations

Barrière¹,

Balahur

2023

Mathematics

Self Cite

View full text Add to dashboard Cite

Machine Learning is an interesting tool for stance recognition in a large-scale context, in terms of data size, but also regarding the topics and themes addressed or the languages employed by the participants. Public consultations of citizens using online participatory democracy platforms offer this kind of setting and are good use cases for automatic stance recognition systems. In this paper, we propose to use three datasets of public consultations, in order to train a model able to classify the stance of a citizen within a text, towards a proposal or a debate question. We studied stance detection in several contexts: using data from an online platform without interactions between users, using multilingual data from online debates that are in one language, and using data from online intra-multilingual debates, which can contain several languages inside the same unique debate discussion. We propose several baselines and methods in order to take advantage of the different available data, by comparing the results of models using out-of-dataset annotations, and binary or ternary annotations from the target dataset. We finally proposed a self-supervised learning method to take advantage of unlabelled data. We annotated both the datasets with ternary stance labels and made them available.

show abstract

Section: Discussionmentioning

confidence: 99%

Multilingual Multi-Target Stance Recognition in Online Public Consultations

Barrière¹,

Balahur

2023

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, the analysis of the argumentative stance of images and texts has gained significant attention. Several shared tasks have been conducted in this area, like the same-side stance classification (Körner et al, 2021) on texts, and the image retrieval for arguments (Bondarenko et al, 2022(Bondarenko et al, , 2023 on images. However, especially for images, the task of stance detection is far from being solved .…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 10th Workshop on Argument Mining

2023

View full text Add to dashboard Cite

Previous work on the automatic identification of fallacies in natural language text has typically approached the problem in constrained experimental setups that make it difficult to understand the applicability and usefulness of the proposals in the real world. In this paper, we present the first analysis of the limitations that these data-driven approaches could show in real situations. For that purpose, we first create a validation corpus consisting of natural language argumentation schemes. Second, we provide new empirical results to the emerging task of identifying fallacies in natural language text. Third, we analyse the errors observed outside of the testing data domains considering the new validation corpus. Finally, we point out some important limitations observed in our analysis that should be taken into account in future research in this topic. Specifically, if we want to deploy these systems in the Wild. Arg. Scheme CQs Natural Language Argumentation Schemes AFEO ✓ Major Premise: "Prof Whittaker is a professor of virology at the Cornell University College" Minor Premise: "Prof Whittaker said that viruses can be spread by sneezing" Conclusion: "Viruses can be spread by sneezing" AFEO ✗ Major Premise: "Stephen Hawking was an expert on AI" Minor Premise: "Stephen Hawking said that AI could spell the end of the human race" Conclusion: "AI could spell the end of the human race" AFPK ✓ Major Premise: "Alice lives in New York" Minor Premise: "Alice says that New York City Hall is in Lower Manhattan" Conclusion: "New York City Hall is in Lower Manhattan" AFPK ✗ Major Premise: "David is a cab driver in London" Minor Premise: "David says that the best way to get to Tower Bridge is by cab" Conclusion: "The best way to get to Tower Bridge is by cab" AFPP ✓ Major Premise: "Most people wear black clothes at a funeral" Minor Premise: "If most people wear black clothes at a funeral, that is acceptable to do" Conclusion: "It is acceptable to wear black clothes at a funeral" AFPP ✗ Major Premise: "Most people drive at least 10 miles per hour over the speed limit" Minor Premise: "If most people drive at least 10 miles per hour over the speed... ...limit, that is acceptable to do" Conclusion: "It is acceptable to drive at least 10 miles per hour over the speed limit" AFPO ✓ General Acceptance Premise: "The majority of climate scientists agree that humans... ...are causing global warming and climate change" Presumption Premise: "If the majority of climate scientists agree that humans... ...are causing global warming and climate change, there is a reason to believe that is true" Conclusion: "There is reason to believe that humans... ...are causing global warming and climate change" AFPO ✗General Acceptance Premise: "The majority of people we asked agreed that the Earth may be flat " Presumption Premise: "If the majority of people we asked agreed that the Earth... ...may be flat, there is a reason to believe that is true" Conclusion: "There is reason to believe that the Earth may be flat" SSAS ✓ FirstStep Premise: "I should ...

show abstract