Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations

• URL: http://arxiv.org/abs/2010.03644v1
• Date: Wed, 7 Oct 2020 20:45:14 GMT
• Title: Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations
• Authors: Wanrong Zhu, Xin Eric Wang, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
• Abstract summary: We design 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? 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.
• Score: 67.4375210552593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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