No abstract

Sequence labeling, in which a class or label is assigned to each token in a given input order, is a fundamental task in natural language processing. Many advanced neural network architectures have recently been proposed to solve the sequential labeling problem affecting this task. By contrast, only a few approaches have been proposed to address the sequential ensemble problem. In this paper, we resolve the sequential ensemble problem by applying the sequential alignment method in a proposed ensemble framework. Specifically, we propose a simple but efficient ensemble candidate generation framework with which multiple heterogeneous systems can easily be prepared from a single neural sequence labeling network. To evaluate the proposed framework, experiments were conducted with part-of-speech (POS) tagging and dependency label prediction problems. The results indicate that the proposed framework achieved accuracy values that were higher by 0.19 and 0.33 than those achieved by the hard-voting method on the Penn-treebank POS-tagged and Universal dependency-tagged datasets, respectively.

No abstract

Controllable text generation is the primary technique for controlling specific attributes such as topic, keywords and obtaining augmented data. This work proposes a novel controllable text generation framework to improve the controllability of generation models. First, we introduce semantic control grammar, a controllable input format to generate sentences that satisfy the constraints. Second, we adopt a generation and rerank method to obtain semantically reranked controlled sentences. Extensive experiments and analyses are conducted on benchmark, natural language understanding, data-to-text generation, and text classification datasets. Through automatic evaluations, we show that our method leads to improvement over strong baselines. The results show that our model can control sentence and word attributes and semantically generate natural sentences. Furthermore, our techniques improve the generation quality of the model.