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%.

我们提出了一个用于学习关联性的神经框架，该框架可应用于主谓宾结构等词组的词向量空间的学习。结果表明，我们的模型在选择偏好和事件相似性等任务上均具有鲁棒性和多功能性，并减少了高达 95％的参数数量。

多方向联合优化功能特定词表示

Multidirectional Associative Optimization of Function-Specific Word  Representations

Prior work has proposed effective methods to learn event representations that
can capture syntactic and semantic information over text corpus, demonstrating
their effectiveness for downstream tasks such as script event prediction. On
the other hand, events extracted from raw texts lacks of commonsense knowledge,
such as the intents and emotions of the event participants, which are useful
for distinguishing event pairs when there are only subtle differences in their
surface realizations. To address this issue, this paper proposes to leverage
external commonsense knowledge about the intent and sentiment of the event.
Experiments on three event-related tasks, i.e., event similarity, script event
prediction and stock market prediction, show that our model obtains much better
event embeddings for the tasks, achieving 78% improvements on hard similarity
task, yielding more precise inferences on subsequent events under given
contexts, and better accuracies in predicting the volatilities of the stock
market.

本论文提出一种利用外部常识知识的方法，增强文本中提取的事件表示的有效性，实验结果表明该方法在事件相关任务中表现良好，如事件相似度计算、脚本事件预测和股票市场预测等方面得到了显著优化。