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Cited by 126Natural Language Processing TechniquesSpeech and dialogue systemsTopic Modeling

Evaluating Evaluation Methods for Generation in the Presence of Variation

Stent, Amanda, Marge, Matthew, Singhai, Mohit·2005·Lecture Notes in Computer Science
DOI 10.1007/978-3-540-30586-6_38
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Crossref · 10.1007/978-3-540-30586-6_38

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  • ROUGE and BLEU cannot capture syntactic and semantic similarity.

    “In spite of being easily and widely used, the above method is incapable of capturing syntactic and semantic similarity (Stent et al., 2005).”

    From ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate (2023) · cites this paper as Stent et al., 2005

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References 19 · Cited by 126

Authors
Stent, A., Marge, M., & Singhai, M.
Affiliations
Stony Brook University (US)
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2005
Pages
341-351
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-540-30586-6_38
OpenAlex
W1552182777
Cited by
126
References
19
Citations by year
2026: 1 · 2025: 6 · 2024: 5 · 2023: 10 · 2022: 15 · 2021: 11 · 2020: 6 · 2019: 14 · 2018: 14 · 2017: 7 · 2016: 6 · 2015: 2 · 2014: 1 · 2013: 1 · 2012: 1
FWCI
1.90
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
automatic evaluation metrics, generation systems, syntactic variation, paraphrase, adequacy, fluency, semantic similarity, syntactic correctness

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