Easy Things First: Installments Improve Referring Expression Generation for Objects in Photographs
Easy Things First: Installments Improve Referring Expression Generation for Objects in Photographs
复制标题
首先简单的事情：分期付款改善照片中对象的引用表达生成
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
David Schlangen
中科院分区：
文献类型：
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作者：
Sina Zarrieß;David Schlangen
到目前为止，关于指代表达式生成的研究主要集中在“一次指代”上，其目的是生成一个单一的、有区别的表达式。然而，在互动环境中，参考文献在“分期付款”中建立的情况并不少见，在这种情况下，参考文献信息是零敲碎打地提供的，直到成功地fiRmed为止。我们表明，这种策略在只对对象属性和类别具有不确定访问权限的技术系统中也可以是有利的。我们在图像中对象的RE数据集上训练一个最近引入的基础词义模型，并学习预测语义合适的表达。在人类评估中，我们观察到用户对不充分的对象名称很敏感--不幸的是，这些名称并不是不可能由低级视觉输入生成的。我们提出了一种受人类任务导向交互启发的解决方案，并实现了避免和修复语义不准确单词的策略。我们通过上下文感知、指代分部和fi来增强基于单词的REG，并认为它们大大提高了系统的指代成功。
Research on generating referring expressions has so far mostly focussed on “one-shot reference”, where the aim is to generate a single, discriminating expression. In interactive settings, however, it is not uncommon for reference to be established in “installments”, where referring information is offered piecewise until success has been confirmed. We show that this strategy can also be advantageous in technical systems that only have uncertain access to object attributes and categories. We train a recently introduced model of grounded word meaning on a data set of REs for objects in images and learn to predict semantically appropriate expressions. In a human evaluation, we observe that users are sensitive to inadequate object names - which unfortunately are not unlikely to be generated from low-level visual input. We propose a solution inspired from human task-oriented interaction and implement strategies for avoiding and repairing semantically inaccurate words. We enhance a word-based REG with context-aware, referential installments and find that they substantially improve the referential success of the system.
2009 年 TUNA-REG 挑战赛：概述和评估结果
DOI：
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发表时间：
2009
期刊：
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影响因子：
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作者：
A Gatt
通讯作者：
A Gatt