2018/12/16 by Daniela Massiceti, Massiceti, Daniela, Puneet K. Dokania +5 · 5 citations
Computer Science · Psychology · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Language, Metaphor, and Cognition #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Speech and dialogue systems #cs.CL #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1812.06417
2018 NeurIPS Workshop on Critiquing and Correcting Trends in Machine Learning
openalex publication_date 2018/12/16 · arxiv created 2019/10/22 · arxiv updated 2019/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We characterise some of the quirks and shortcomings in the exploration of Visual Dialogue - a sequential question-answering task where the questions and corresponding answers are related through given visual stimuli. To do so, we develop an embarrassingly simple method based on Canonical Correlation Analysis (CCA) that, on the standard dataset, achieves near state-of-the-art performance on mean rank (MR). In direct contrast to current complex and over-parametrised architectures that are both compute and time intensive, our method ignores the visual stimuli, ignores the sequencing of dialogue, does not need gradients, uses off-the-shelf feature extractors, has at least an order of magnitude fewer parameters, and learns in practically no time. We argue that these results are indicative of issues in current approaches to Visual Dialogue and conduct analyses to highlight implicit dataset biases and effects of over-constrained evaluation metrics. Our code is publicly available.