Neural network-based Marked Temporal Point Process (MTPP) models have been widely adopted to model event sequences in high-stakes applications, raising concerns about the trustworthiness of outputs from these models. This study focuses on Explanation for MTPP, aiming to identify the minimal and rational explanation, that is, the minimum subset of events in history, based on which the prediction accuracy of MTPP matches that based on full history to a great extent and better than that based on the complement of the subset. This study finds that directly defining Explanation for MTPP as counterfactual explanation or factual explanation can result in irrational explanations. To address this issue, we define Explanation for MTPP as a combination of counterfactual explanation and factual explanation. This study proposes Counterfactual and Factual Explainer for MTPP (CFF) to solve Explanation for MTPP with a series of deliberately designed techniques. Experiments demonstrate the correctness and superiority of CFF over baselines regarding explanation quality and processing efficiency.

Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the next event. However, existing studies overlook that the distribution of event marks is highly imbalanced in many real-world applications, with some marks being frequent but others rare. The imbalance poses a significant challenge to the performance of the next event prediction, especially for events of rare marks. To address this issue, we propose a thresholding method, which learns thresholds to tune the mark probability normalized by the mark's prior probability to optimize mark prediction, rather than predicting the mark directly based on the mark probability as in existing studies. In conjunction with this method, we predict the mark first and then the time. In particular, we develop a novel neural Marked Temporal Point Process (MTPP) model to support effective time sampling and estimation of mark probability without computationally expensive numerical improper integration. Extensive experiments on real-world datasets demonstrate the superior performance of our solution against various baselines for the next event mark and time prediction. The code is available at https://github.com/undes1red/IFNMTPP.

In the construction of multiple-choice Machine Reading Comprehension(MRC), in addition to questions and answers, it is necessary to generate distractors corresponding to them. Recent models based on Seq2Seq have shown good results in text generation, while previous work has only succeeded in producing a few interfering words or phrases per question. Our goal is to generate more meaningful distractors based on reading comprehension of articles that are closer to the semantics of the question to help better diagnose gaps in text understanding. A major drawback of recent studies is that they do not take the relationship between the distractors and background text into account when generating distractors. This often results in the distractors being either too general or too close to the correct answer. We propose a Topic Enhanced Multi-head Co-Attention model (TMCA) based on hierarchical networks to better capture the interactions between sentences. By adding query-relevance loss, our model enables the distractors to be as semantically relevant to the question as possible based on the reading comprehension of the article, while trying to ensure that they are false answers. The results show that the proposed approach achieves superior performance over the baselines in terms of automatic metrics on two multiple-choice datasets (RACE and DREAM). For further evaluation, we use another advanced reading comprehension model and human evaluation to demonstrate that our model outperforms several strong baselines in generating high-quality and educationally meaningful distracters.