Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset. Empirical results on 6 tasks and 21 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in all the evaluations.
翻译:经过事先培训的嵌入是结构化预测任务的强大字词表达方式。最近的工作发现,通过配置不同类型的嵌入方式,可以实现更好的字表达方式。然而,选择嵌入方式以形成最佳融合式代表方式,通常取决于任务和候选人嵌入方式的收集情况,而不断增多的嵌入类型使得它成为一个更困难的问题。在本文件中,我们提议对嵌入方式进行自动整合,以根据神经结构研究最近进展的启发,为结构化预测任务寻找更好的嵌入方式。具体地说,一个控制者根据对任务单个嵌入类型的有效性的当前信念,对嵌入方式的配置进行交替抽样,并更新基于奖励的信念。我们遵循强化学习的战略,以优化控制器的参数,并根据任务模式的准确性进行奖赏,该模式以抽样组合为基础,作为投入和根据任务数据集培训而提供。具体来说,6项任务和21项数据的嵌入结果,根据对嵌入方式对嵌入方式的配置,根据对每个任务进行精细的基线显示我们的业绩基准,21项调整了所有业绩评估。