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Analysis of systems' performance in natural language processing competitions.

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Bibliographic Details
Title: Analysis of systems' performance in natural language processing competitions.
Authors: Nava-Muñoz, Sergio1,2 (AUTHOR) nava@cimat.mx, Graff, Mario2,3 (AUTHOR) mario.graff@infotec.mx, Escalante, Hugo Jair4 (AUTHOR) hugojair@inaoep.mx
Source: Pattern Recognition Letters. Oct2024, Vol. 186, p346-353. 8p.
Subjects: Natural language processing, Evaluation methodology, Natural languages, Confidence intervals, Contests
Abstract: Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. This manuscript describes an evaluation methodology for statistically analyzing competition results and competition. The methodology is designed to be universally applicable; however, it is illustrated using eight natural language competitions as case studies involving classification and regression problems. The proposed methodology offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. Furthermore, we introduce metrics that allow organizers to assess the difficulty of competitions. Our analysis shows the potential usefulness of our methodology for effectively evaluating competition results. • A procedure using bootstrapping to analyze competition performance is presented. • Advanced tools & visuals to enhance score ranking for competitions' winner selection are described. • A study analyzing several NLP competitions using the methods and tools is described. • We highlight the need for robusts and effective tools when comparing the performance of models in competitions. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. This manuscript describes an evaluation methodology for statistically analyzing competition results and competition. The methodology is designed to be universally applicable; however, it is illustrated using eight natural language competitions as case studies involving classification and regression problems. The proposed methodology offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. Furthermore, we introduce metrics that allow organizers to assess the difficulty of competitions. Our analysis shows the potential usefulness of our methodology for effectively evaluating competition results. • A procedure using bootstrapping to analyze competition performance is presented. • Advanced tools & visuals to enhance score ranking for competitions' winner selection are described. • A study analyzing several NLP competitions using the methods and tools is described. • We highlight the need for robusts and effective tools when comparing the performance of models in competitions. [ABSTRACT FROM AUTHOR]
ISSN:01678655
DOI:10.1016/j.patrec.2024.03.010