VOLTA: Diverse and Controllable Question-Answer Pair Generation with Variational Mutual Information Maximizing Autoencoder

Previous question-answer pair generation methods aimed to produce fluent and meaningful question-answer pairs but tend to have poor diversity. Recent attempts addressing this issue suffer from either low model capacity or overcomplicated architecture. Furthermore, they overlooked the problem where the controllability of their models is highly dependent on the input. In this paper, we propose a model named VOLTA that enhances generative diversity by leveraging the Variational Autoencoder framework with a shared backbone network as its encoder and decoder. In addition, we propose adding InfoGAN-style latent codes to enable input-independent controllability over the generation process. We perform comprehensive experiments and the results show that our approach can significantly improve diversity and controllability over state-of-the-art models.

ConcreteGraph: A Data Augmentation Method Leveraging the Properties of Concept Relatedness Estimation

The concept relatedness estimation (CRE) task is to determine whether two given concepts are related. Although existing methods for the semantic textual similarity (STS) task can be easily adapted to this task, the CRE task has some unique properties that can be leveraged to augment the datasets for addressing its data scarcity problem. In this paper, we construct a graph named ConcreteGraph (Concept relatedness estimation Graph) to take advantage of the CRE properties. For the sampled new concept pairs from the ConcreteGraph, we add an additional step of filtering out the new concept pairs with low quality based on simple yet effective quality thresholding. We apply the ConcreteGraph data augmentation on three Transformer-based models to show its efficacy. Detailed ablation study for quality thresholding further shows that even a limited amount of high-quality data is more beneficial than a large quantity of unthresholded data. This paper is the first one to work on the WORD dataset and the proposed ConcreteGraph can boost the accuracy of the Transformers by more than 2%. All three Transformers, with the help of ConcreteGraph, can outperform the current state-of-theart method, Concept Interaction Graph (CIG), on the CNSE and CNSS datasets.

NLP in Human Rights Research -- Extracting Knowledge Graphs About Police and Army Units and Their Commanders

In this working paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account specific units and personnel alleged to have committed abuses of human rights and international criminal law. This working paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this working paper are the system's code and training dataset. We find that the experimental NLP system performs the task at a fair to good level. Its performance is sufficient to justify further development into a live workflow that will give insight into whether its performance translates into savings in time and resource that would make it an effective technical intervention.