Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations
Wanrong Zhu, Xin Wang, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
Abstract
A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. To do this, it is critical to ensure that our evaluation protocols are correct, and benchmarks are reliable. In this work, we set forth to design a set of experiments to understand an important but often ignored problem in visually grounded language generation: given that humans have different utilities and visual attention, how will the sample variance in multi-reference datasets affect the models’ performance? Empirically, we study several multi-reference datasets and corresponding vision-and-language tasks. We show that it is of paramount importance to report variance in experiments; that human-generated references could vary drastically in different datasets/tasks, revealing the nature of each task; that metric-wise, CIDEr has shown systematically larger variances than others. Our evaluations on reference-per-instance shed light on the design of reliable datasets in the future.- Anthology ID:
- 2020.emnlp-main.708
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8806–8811
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.708
- DOI:
- 10.18653/v1/2020.emnlp-main.708
- Cite (ACL):
- Wanrong Zhu, Xin Wang, Pradyumna Narayana, Kazoo Sone, Sugato Basu, and William Yang Wang. 2020. Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8806–8811, Online. Association for Computational Linguistics.
- Cite (Informal):
- Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations (Zhu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.708.pdf
- Data
- COCO, Flickr30k, VATEX, VIST