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Adaptive Joint Learning of Compositional and Non-Compositional Phrase Embeddings

机译:成分和非成分词组嵌入的自适应联合学习

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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.
机译:我们提出了一种新颖的方法,可以通过使用组成评分功能对两种类型的嵌入自适应加权,从而共同学习组成词和非组成词组的嵌入。计分函数用于量化每个短语的组成程度,并且该函数的参数与用于学习短语嵌入的目标共同优化。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任务,并表明构词分数与动词-对象构词的人类评分有很强的相关性,大大优于现有技术。此外,我们的嵌入改进了对及物动词消歧任务的先前最佳模型。我们还表明,一种简单的集成技术可以进一步提高两个任务的结果。

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