Previous methods on knowledge base question generation (KBQG) primarily focus on refining the quality of a single generated question. However, considering the remarkable paraphrasing ability of humans, we believe that diverse texts can express identical semantics through varied expressions. The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity. Current metrics inadequately assess the aforementioned diversity. They calculate the ratio of unique n-grams in the generated question, which tends to measure duplication rather than true diversity. Accordingly, we devise a new diversity evaluation metric, which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. Clearly, the second challenge is how to enhance diversifying question generation. To address this challenge, we introduce a dual model framework interwoven by two selection strategies to generate diverse questions leveraging external natural questions. The main idea of our dual framework is to extract more diverse expressions and integrate them into the generation model to enhance diversifying question generation. Extensive experiments on widely used benchmarks for KBQG show that our approach can outperform pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT.

Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment.However, traditional CQG, focusing primarily on the immediate context, lacks the conversational foresight necessary to guide conversations toward specified conclusions.This limitation significantly restricts their ability to achieve conclusion-oriented conversational outcomes.In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair.To address this, we propose a novel approach, called Proactive Conversational Question Planning with self-Refining (PCQPR).Concretely, by integrating a planning algorithm inspired by Monte Carlo Tree Search (MCTS) with the analytical capabilities of large language models (LLMs), PCQPR predicts future conversation turns and continuously refines its questioning strategies.This iterative self-refining mechanism ensures the generation of contextually relevant questions strategically devised to reach a specified outcome.Our extensive evaluations demonstrate that PCQPR significantly surpasses existing CQG methods, marking a paradigm shift towards conclusion-oriented conversational question-answering systems. * This work was done during an

Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs.In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models.To achieve this, we propose a novel approach to model diverse subgraphs with metalearner (DSM).Specifically, we devise a graph contrastive learning-based retriever to identify semantically similar subgraphs, so that we can construct the semantics-aware learning tasks for the meta-learner to learn semanticsspecific and semantics-agnostic knowledge on and across these tasks.Extensive experiments on two widely-adopted benchmarks for KBQG show that DSM derives new state-of-the-art performance and benefits the question answering tasks as a means of data augmentation.Codes and datasets are available online 1 .

GNN-based anomaly detection has recently attracted considerable attention. Existing attempts have thus far focused on jointly learning the node representations and the classifier for detecting the anomalies. Inspired by the recent advances of self-supervised learning (SSL) on graphs, we explore another possibility of decoupling the node representation learning and the classification for anomaly detection. We conduct a preliminary study to show that decoupled training using existing graph SSL schemes to represent nodes can obtain performance gains over joint training, but it may deteriorate when the behavior patterns and the label semantics become highly inconsistent. To be less biased by the inconsistency, we propose a simple yet effective graph SSL scheme, called Deep Cluster Infomax (DCI) for node representation learning, which captures the intrinsic graph properties in more concentrated feature spaces by clustering the entire graph into multiple parts. We conduct extensive experiments on four real-world datasets for anomaly detection. The results demonstrate that decoupled training equipped with a proper SSL scheme can outperform joint training in AUC. Compared with existing graph SSL schemes, DCI can help decoupled training gain more improvements.