Gisela Vallejo


2023

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Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
Vishakh Padmakumar | Gisela Vallejo | Yao Fu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)

2022

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Evaluating the Examiner: The Perils of Pearson Correlation for Validating Text Similarity Metrics
Gisela Vallejo | Timothy Baldwin | Lea Frermann
Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association

In recent years, researchers have developed question-answering based approaches to automatically evaluate system summaries, reporting improved validity compared to word overlap-based metrics like ROUGE, in terms of correlation with human ratings of criteria including fluency and hallucination. In this paper, we take a closer look at one particular metric, QuestEval, and ask whether: (1) it can serve as a more general metric for long document similarity assessment; and (2) a single correlation score between metric scores and human ratings, as the currently standard approach, is sufficient for metric validation. We find that correlation scores can be misleading, and that score distributions and outliers should be taken into account. With these caveats in mind, QuestEval can be a promising candidate for long document similarity assessment.