论文标题
半监督自然语言理解的双重学习
Dual Learning for Semi-Supervised Natural Language Understanding
论文作者
论文摘要
自然语言理解（NLU）将句子转换为结构化的语义形式。注释培训样本的匮乏仍然是NLU的基本挑战。为了解决这些数据稀疏问题，先前基于半监督学习的工作主要集中于利用未标记的句子。在这项工作中，我们介绍了NLU，语义到句子生成（SSG）的双重任务，并通过相应的双重模型为半监视的NLU提出了一个新的框架。该框架由双伪标记和双学习方法组成，该方法使NLU模型能够通过原始和双重任务的闭环充分利用数据（标记和未标记）。通过结合双重任务，该框架可以利用纯语义形式和未标记的句子，并进一步改善NLU和SSG模型在闭环中迭代。提出的方法在两个公共数据集（ATIS和SNIPS）上进行评估。半监督环境中的实验表明，我们的方法可以显着胜过各种基准，并进行了广泛的消融研究以验证我们的框架的有效性。最后，我们的方法还可以在监督环境中的两个数据集上实现最新性能。我们的代码可在\ url {https://github.com/rhythmcao/slu-dual-learning.git}中找到。
Natural language understanding (NLU) converts sentences into structured semantic forms. The paucity of annotated training samples is still a fundamental challenge of NLU. To solve this data sparsity problem, previous work based on semi-supervised learning mainly focuses on exploiting unlabeled sentences. In this work, we introduce a dual task of NLU, semantic-to-sentence generation (SSG), and propose a new framework for semi-supervised NLU with the corresponding dual model. The framework is composed of dual pseudo-labeling and dual learning method, which enables an NLU model to make full use of data (labeled and unlabeled) through a closed-loop of the primal and dual tasks. By incorporating the dual task, the framework can exploit pure semantic forms as well as unlabeled sentences, and further improve the NLU and SSG models iteratively in the closed-loop. The proposed approaches are evaluated on two public datasets (ATIS and SNIPS). Experiments in the semi-supervised setting show that our methods can outperform various baselines significantly, and extensive ablation studies are conducted to verify the effectiveness of our framework. Finally, our method can also achieve the state-of-the-art performance on the two datasets in the supervised setting. Our code is available at \url{https://github.com/rhythmcao/slu-dual-learning.git}.
