Rethinking the Agreement in Human Evaluation Tasks
By Jacopo Amidei, Paul Piwek, Alistair Willis. 2018
54 citations recorded by the source corpus.
Subfields: Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Evaluation & Benchmarks
- Elizabeth Clark
- Jianfeng Gao
- Asli Celikyilmaz
- Mitesh M. Khapra
- Ananya B. Sai
- Noah A. Smith
- Hung-yi Lee
- Cheng-Han Chiang
Citations in the corpus
- All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021, methodology): “evaluation measures like agreement (Amidei et al., 2018), as well as analyses of past NLG papers’ human evaluations (van der Lee et al.”
- Evaluation of Text Generation: A Survey (2020, background): “However, as Amidei et al. (2018) point out, given the richness and variety of natural language, pushing for the highest possible inter-annotator agreement may not be the right choice when it comes to NLG evaluation.”
- Agreement is overrated: A plea for correlation to assess human evaluation reliability (2019, background): “However, Sampson and Babarczy (2008), Lom-mel et al. (2014), Joshi et al. (2016) and Amidei et al. (2018b) suggest that a low level of IAA can be explained with human language variability.”
- A Survey of Evaluation Metrics Used for NLG Systems (2020, background): “Achieving a high-enough IAA is more difficult on some NLG tasks which have room for subjectivity [2].”
- Best practices for the human evaluation of automatically generated text (2019, background): “Gkatzia and Mahamood (2015) focused on NLG papers from 2005-2014; Amidei et al. (2018a) provided a 2013-2018 overview of evaluation in question generation; and Gatt and Krahmer (2018) provided a more general survey of the state-of-the-art in NLG.”
- Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries (2021, background): “Various methodologies have been proposed to measure inter- and intra-annotator consistency in human evaluation (Amidei et al., 2018).”
- Can Large Language Models Be an Alternative to Human Evaluations? (2023, background): “However, it is important to note that these limitations and potential harms also apply to human evaluation: the bias of human evaluators can affect the human evaluation result (Lentz and De Jong, 1997; Amidei et al., 2018).”