Jury: A Comprehensive Evaluation Toolkit
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Abstract (arXiv)
Evaluation plays a critical role in deep learning as a fundamental block of any prediction-based system. However, the vast number of Natural Language Processing (NLP) tasks and the development of various metrics have led to challenges in evaluating different systems with different metrics. To address these challenges, we introduce jury, a toolkit that provides a unified evaluation framework with standardized structures for performing evaluation across different tasks and metrics. The objective of jury is to standardize and improve metric evaluation for all systems and aid the community in overcoming the challenges in evaluation. Since its open-source release, jury has reached a wide audience and is available at https://github.com/obss/jury.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 3 Oct 2023.
Builds on
10 of 25 references.
References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
- 2021Datasets: A Community Library for Natural Language Processingcited by 526
- 2020A Survey of Evaluation Metrics Used for NLG Systemscited by 164
- 2017Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generationcited by 61
- 2021Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correctioncited by 11
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Citing papers per year: 2024: 1.
1 paper.