Puzikov, Yevgeniy (2021)
Evaluation Discrepancy Discovery: A Sentence Compression Case-study.
doi: 10.48550/arXiv.2101.09079
Report, Bibliographie
Kurzbeschreibung (Abstract)
Reliable evaluation protocols are of utmost importance for reproducible NLP research. In this work, we show that sometimes neither metric nor conventional human evaluation is sufficient to draw conclusions about system performance. Using sentence compression as an example task, we demonstrate how a system can game a well-established dataset to achieve state-of-the-art results. In contrast with the results reported in previous work that showed correlation between human judgements and metric scores, our manual analysis of state-of-the-art system outputs demonstrates that high metric scores may only indicate a better fit to the data, but not better outputs, as perceived by humans.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Puzikov, Yevgeniy |
| Art des Eintrags: | Bibliographie |
| Titel: | Evaluation Discrepancy Discovery: A Sentence Compression Case-study |
| Sprache: | Englisch |
| Publikationsjahr: | 22 Januar 2021 |
| Verlag: | arXiv |
| Reihe: | Computation and Language |
| Kollation: | 15 Seiten |
| Veranstaltungstitel: | arXiv |
| DOI: | 10.48550/arXiv.2101.09079 |
| URL / URN: | https://arxiv.org/abs/2101.09079 |
| Kurzbeschreibung (Abstract): | Reliable evaluation protocols are of utmost importance for reproducible NLP research. In this work, we show that sometimes neither metric nor conventional human evaluation is sufficient to draw conclusions about system performance. Using sentence compression as an example task, we demonstrate how a system can game a well-established dataset to achieve state-of-the-art results. In contrast with the results reported in previous work that showed correlation between human judgements and metric scores, our manual analysis of state-of-the-art system outputs demonstrates that high metric scores may only indicate a better fit to the data, but not better outputs, as perceived by humans. |
| Zusätzliche Informationen: | 1. Version |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 17 Feb 2021 08:31 |
| Letzte Änderung: | 19 Dez 2024 10:04 |
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