We present a novel method for jointly learning compositional and
non-compositional phrase embeddings by adaptively weighting both types of
embeddings using a compositionality scoring function. The scoring function is
used to quantify the level of compositionality of each phrase, and the
parameters of the function are jointly optimized with the objective for
learning phrase embeddings. In experiments, we apply the adaptive joint
learning method to the task of learning embeddings of transitive verb phrases,
and show that the compositionality scores have strong correlation with human
ratings for verb-object compositionality, substantially outperforming the
previous state of the art. Moreover, our embeddings improve upon the previous
best model on a transitive verb disambiguation task. We also show that a simple
ensemble technique further improves the results for both tasks.

本文提出了一种新的方法，通过自适应加权组成和非组成短语嵌入来学习短语嵌入，其中使用组成性评分函数量化每个短语的组成水平，并联合优化功能参数。在实验中，我们将自适应联合学习方法应用于学习及物动词短语的嵌入，并显示组成分数与动词 - 宾语组合的人类评分具有很强的相关性，明显优于以往的技术水平。此外，我们的嵌入在及物动词消歧任务上也优于以前的最佳模型。我们还展示了一种简单的集成技术，进一步提高了两项任务的结果。