论文标题
用于语义依赖性的跨语性转移的mutlitask学习
Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies
论文作者
论文摘要
我们描述了一种开发宽覆盖语义依赖性解析器的方法，该语言没有语义注释资源可用。我们利用多任务学习框架以及注释投影方法。我们通过并行数据将监督的语义依赖解析注释从丰富的资源语言转移到低资源语言，并在投影数据上培训语义解析器。我们将监督的句法解析用作多任务学习框架中的辅助任务，并表明，通过不同的多任务学习设置，我们一致地在单任务基准方面进行了改进。在英语为源的环境中，捷克语是目标语言，我们最好的多任务模型将单任任务基线的标记的F1得分提高了1.8，在域内半eval数据中（Oepen等，2015），在室外测试集中2.5。此外，我们观察到句法和语义依赖方向匹配是改善结果的重要因素。
We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask learning framework coupled with an annotation projection method. We transfer supervised semantic dependency parse annotations from a rich-resource language to a low-resource language through parallel data, and train a semantic parser on projected data. We make use of supervised syntactic parsing as an auxiliary task in a multitask learning framework, and show that with different multitask learning settings, we consistently improve over the single-task baseline. In the setting in which English is the source, and Czech is the target language, our best multitask model improves the labeled F1 score over the single-task baseline by 1.8 in the in-domain SemEval data (Oepen et al., 2015), as well as 2.5 in the out-of-domain test set. Moreover, we observe that syntactic and semantic dependency direction match is an important factor in improving the results.
