Abstract
Answering open-ended questions is an essential capability for any intelligent agent. One of the most interesting recent open-ended question answering challenges is Visual Question Answering (VQA) which attempts to evaluate a system’s visual understanding through its answers to natural language questions about images. There exist many approaches to VQA, the majority of which do not exhibit deeper semantic understanding of the candidate answers they produce. We study the importance of generating plausible answers to a given question by introducing the novel task of `Answer Proposal’: for a given open-ended question, a system should generate a ranked list of candidate answers informed by the semantics of the question. We experiment with various models including a neural generative model as well as a semantic graph matching one. We provide both intrinsic and extrinsic evaluations for the task of Answer Proposal, showing that our best model learns to propose plausible answers with a high recall and performs competitively with some other solutions to VQA.
Abstract (translated by Google)
回答开放式问题是任何智能代理人必备的能力。视觉问题回答（Visual Question Answering，简称VQA）是最近最有趣的开放式问题答案之一，它试图通过对图像自然语言问题的回答来评估系统的视觉理解。 VQA存在很多方法，其中大多数不会对候选答案产生更深的语义理解。我们研究通过引入“答案建议书”的新任务来为给定问题产生合理答案的重要性：对于给定的开放式问题，系统应该产生由问题的语义通知的候选答案的排序列表。我们尝试了各种模型，包括一个神经生成模型以及一个与之匹配的语义图。我们针对答案的任务提供了内在和外在的评估，表明我们最好的模型学习提出高回忆的合理答案，并与VQA的其他解决方案进行竞争。
URL
https://arxiv.org/abs/1610.06620