Unsupervised Keyword Extraction for Full-Sentence VQA
Unsupervised Keyword Extraction for Full-Sentence VQA
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DOI：
10.18653/v1/2020.nlpbt-1.6
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Kohei Uehara;T. Harada
中科院分区：
文献类型：
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作者：
Kohei Uehara;T. Harada
在大多数现有的可视化问题分类（VQA）研究中，答案由简短的，通常是单个单词组成，根据在数据集构建过程中给注释者的指示。这项研究设想了一个VQA任务的自然情况下，答案更有可能是句子，而不是单个单词。为了弥补这种自然的VQA和现有的VQA方法之间的差距，提出了一种新的无监督关键词提取方法。该方法基于这样的原则，即整句回答可以被分解为两部分：一部分包含回答问题的新信息（即关键字），另一部分包含问题中已经包含的信息。判别解码器的设计，以实现这种分解，并在VQA数据集上包含整句答案的方法进行了实验。实验结果表明，该模型可以准确地提取关键词，而不需要给出明确的注释来描述它们。
In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during dataset construction. This study envisions a VQA task for natural situations, where the answers are more likely to be sentences rather than single words. To bridge the gap between this natural VQA and existing VQA approaches, a novel unsupervised keyword extraction method is proposed. The method is based on the principle that the full-sentence answers can be decomposed into two parts: one that contains new information answering the question (i.e. keywords), and one that contains information already included in the question. Discriminative decoders were designed to achieve such decomposition, and the method was experimentally implemented on VQA datasets containing full-sentence answers. The results show that the proposed model can accurately extract the keywords without being given explicit annotations describing them.