论文标题
特定于函数的单词表示的多向关联优化
Multidirectional Associative Optimization of Function-Specific Word Representations
论文作者
论文摘要
我们提出了一个神经框架，用于学习相互关联的单词组之间的关联，例如主题 - 动物对象（SVO）结构中的框架。我们的模型诱导特定于联合函数的单词矢量空间，例如合理的SVO组成靠近。该模型即使在关节空间中也保留有关单词组成员资格的信息，因此可以有效地应用于SVO结构上的许多任务推理。我们通过报告估计选择偏好和事件相似性的任务来报告提议框架的鲁棒性和多功能性。结果表明，与我们任务无关的模型学到的表示形式的组合优于先前工作的特定任务架构，同时将参数数量降低了95％。
We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.
