Overview of Touché 2020: Argument Retrieval

Bondarenko, Alexander; Fröbe, Maik; Beloucif, Meriem; Gienapp, Lukas; Ajjour, Yamen; Panchenko, Alexander; Biemann, Chris; Stein, Benno; Wachsmuth, Henning; Potthast, Martin; Hagen, Matthias

doi:10.1007/978-3-030-58219-7_26

Cited by 32 publications

(24 citation statements)

References 26 publications

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“…In retrieval tasks, human relevance judgments of retrieval results for a fixed set of topics allows for evaluating the effectiveness of competing retrieval models. Known as the Cranfield paradigm or TREC-style evaluation (Voorhees, 2001), it is also employed in textual argument retrieval within the Touché shared tasks (Bondarenko et al, 2020).…”

Section: Crowdsourcing Relevance Judgementsmentioning

confidence: 99%

Image Retrieval for Arguments Using Stance-Aware Query Expansion

Kiesel¹,

Reichenbach²,

Stein³

et al. 2021

Proceedings of the 8th Workshop on Argument Mining

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Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far. This paper shows how to integrate images into argument mining research, specifically into argument retrieval. By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve "argumentative images" for the respective stance. Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance. An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.

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Section: Crowdsourcing Relevance Judgementsmentioning

confidence: 99%

Image Retrieval for Arguments Using Stance-Aware Query Expansion

Kiesel¹,

Reichenbach²,

Stein³

et al. 2021

Proceedings of the 8th Workshop on Argument Mining

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“…In this section, we briefly review the most popular argument search engines and explain both similarities as well as differences to our GUI. For work in the area of argument retrieval, we refer to the CLEF lab Touché [3][4][5].…”

Section: Related Workmentioning

confidence: 99%

“…Wachsmuth et al [15] present Args, one of the first argument search engine prototypes. 3 Args runs on the dataset from Ajjour et al [2] which is now also the official dataset of the CLEF lab Touché [3][4][5]. The dataset draws its arguments from five debate portals indexed by the Java framework Apache Lucene.…”

Section: Related Workmentioning

confidence: 99%

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QuARk: A GUI for Quality-Aware Ranking of Arguments

Nilles

Dumani

Schenkel

2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…The input to this system is not structured but rather a query in a free textual form. The Touché shared task on argument retrieval at CLEF (Bondarenko et al, 2020b(Bondarenko et al, , 2021 featured a related track. The task was to retrieve from a large web corpus documents answering comparative question queries like "What IDE is better for Java: NetBeans or Eclipse?".…”

Section: Related Workmentioning

confidence: 99%

Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language

Chekalina¹,

Bondarenko²,

Biemann³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

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We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices. Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web. We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.

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Overview of Touché 2020: Argument Retrieval

Cited by 32 publications

References 26 publications

Image Retrieval for Arguments Using Stance-Aware Query Expansion

Image Retrieval for Arguments Using Stance-Aware Query Expansion

QuARk: A GUI for Quality-Aware Ranking of Arguments

Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language

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