论文标题
自适应卷积用于语义角色标签
Adaptive Convolution for Semantic Role Labeling
论文作者
论文摘要
语义角色标签（SRL）旨在通过形成谓词题目结构来阐述句子的含义。最近的研究表明，有效使用语法可以改善SRL性能。但是，语法是一个复杂的语言线索，很难在像SRL这样的下游任务中有效地应用。这项工作使用自适应卷积有效地编码语法，该卷积将强大的灵活性赋予现有的卷积网络。现有的CNN可能有助于编码像SRL的语法这样的复杂结构，但仍然存在缺点。与传统的卷积网络相反，将相同过滤器用于不同输入的传统卷积网络，自适应卷积使用以句法知情输入为条件的自适应生成的过滤器。我们通过集成产生特定输入过滤器的滤波器生成网络来实现这一目标。这有助于模型关注输入中存在的重要句法特征，从而扩大语法感知和语法 - 敏捷SRL系统之间的差距。我们进一步研究了一种散列技术，可以根据可训练的参数来压缩SRL的滤波器生成网络的大小。 Conll-2009数据集的实验证实，所提出的模型大大胜过英语和中文的大多数SRL系统
Semantic role labeling (SRL) aims at elaborating the meaning of a sentence by forming a predicate-argument structure. Recent researches depicted that the effective use of syntax can improve SRL performance. However, syntax is a complicated linguistic clue and is hard to be effectively applied in a downstream task like SRL. This work effectively encodes syntax using adaptive convolution which endows strong flexibility to existing convolutional networks. The existing CNNs may help in encoding a complicated structure like syntax for SRL, but it still has shortcomings. Contrary to traditional convolutional networks that use same filters for different inputs, adaptive convolution uses adaptively generated filters conditioned on syntactically informed inputs. We achieve this with the integration of a filter generation network which generates the input specific filters. This helps the model to focus on important syntactic features present inside the input, thus enlarging the gap between syntax-aware and syntax-agnostic SRL systems. We further study a hashing technique to compress the size of the filter generation network for SRL in terms of trainable parameters. Experiments on CoNLL-2009 dataset confirm that the proposed model substantially outperforms most previous SRL systems for both English and Chinese languages
