语义角色模型从动词域到名词域的无监督迁移
Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
摘要：
语义角色标注（Semantic role labeling，SRL）是一个NLP任务，涉及将谓词参数分配给类型，称为语义角色。尽管SRL的研究主要集中在动词谓词上，并且许多可用于SRL的资源仅为动词提供注释，但语义关系通常由其他语言结构触发，例如，名词化在这项工作中，我们调查的情况下，我们假设的角色注释的数据为源动词域，但只有未标记的数据为目标名词域的转移。我们的关键假设，使两个域之间的转移，是一个角色的选择偏好（即，对可接受论元的偏好或约束）并不强烈地依赖于关系是由动词还是名词触发的。例如，相同的一组参数可以填补动词谓词“acquire”及其名词形式“acquisition”的获取者角色。我们从变分自编码的角度来处理转移任务。标签器充当编码器（预测给定句子的角色标签），而选择偏好在解码器组件中捕获（为预测角色生成参数）。在训练数据中没有标记名义角色，而是学习目标推动标记器分配预测参数的角色。跨域共享解码器参数鼓励了针对两个域预测的标签之间的一致性，并且促进了传输。该方法大大优于基线，如无监督和“直接转移”的方法，对英语CoNLL-2009数据集。
英文摘要：
Semantic role labeling (SRL) is an NLP task involving the assignment of predicate arguments to types, called semantic roles. Though research on SRL has primarily focused on verbal predicates and many resources available for SRL provide annotations only for verbs, semantic relations are often triggered by other linguistic constructions, e.g., nominalizations. In this work, we investigate a transfer scenario where we assume role-annotated data for the source verbal domain but only unlabeled data for the target nominal domain. Our key assumption, enabling the transfer between the two domains, is that selectional preferences of a role (i.e., preferences or constraints on the admissible arguments) do not strongly depend on whether the relation is triggered by a verb or a noun. For example, the same set of arguments can fill the Acquirer role for the verbal predicate `acquire' and its nominal form `acquisition'. We approach the transfer task from the variational autoencoding perspective. The labeler serves as an encoder (predicting role labels given a sentence), whereas selectional preferences are captured in the decoder component (generating arguments for the predicting roles). Nominal roles are not labeled in the training data, and the learning objective instead pushes the labeler to assign roles predictive of the arguments. Sharing the decoder parameters across the domains encourages consistency between labels predicted for both domains and facilitates the transfer. The method substantially outperforms baselines, such as unsupervised and `direct transfer' methods, on the English CoNLL-2009 dataset.
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