In sequential event prediction, which finds applications in finance, retail,
social networks, and healthcare, a crucial task is forecasting multiple future
events within a specified time horizon. Traditionally, this has been addressed
through autoregressive generation using next-event prediction models, such as
Marked Temporal Point Processes. However, autoregressive methods use their own
output for future predictions, potentially reducing quality as the prediction
horizon extends. In this paper, we challenge traditional approaches by
introducing a novel benchmark, HoTPP, specifically designed to evaluate a
model's ability to predict event sequences over a horizon. This benchmark
features a new metric inspired by object detection in computer vision,
addressing the limitations of existing metrics in assessing models with
imprecise time-step predictions. Our evaluations on established datasets
employing various models demonstrate that high accuracy in next-event
prediction does not necessarily translate to superior horizon prediction, and
vice versa. HoTPP aims to serve as a valuable tool for developing more robust
event sequence prediction methods, ultimately paving the way for further
advancements in the field.

通过引入新的 HoTPP 基准和度量方法，这篇论文挑战传统方法，评估模型在时间范围内预测事件序列的能力，并且证明下一个事件预测的高准确性不一定能转化为更好的时间范围预测，为进一步改进这一领域的方法铺平道路。

HoTPP 基准：我们在长期事件预测方面表现如何？

HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?

Prediction over event sequences is critical for many real-world applications
in Information Retrieval and Natural Language Processing. Future Event
Generation (FEG) is a challenging task in event sequence prediction because it
requires not only fluent text generation but also commonsense reasoning to
maintain the logical coherence of the entire event story. In this paper, we
propose a novel explainable FEG framework, Coep. It highlights and integrates
two types of event knowledge, sequential knowledge of direct event-event
relations and inferential knowledge that reflects the intermediate character
psychology between events, such as intents, causes, reactions, which
intrinsically pushes the story forward. To alleviate the knowledge forgetting
issue, we design two modules, Im and Gm, for each type of knowledge, which are
combined via prompt tuning. First, Im focuses on understanding inferential
knowledge to generate commonsense explanations and provide a soft prompt vector
for Gm. We also design a contrastive discriminator for better generalization
ability. Second, Gm generates future events by modeling direct sequential
knowledge with the guidance of Im. Automatic and human evaluation demonstrate
that our approach can generate more coherent, specific, and logical future
events.

该论文提出了一种新型的可解释的未来事件生成框架 Coep，该框架集成了两种类型的事件知识，包括直接事件 - 事件关系的序列知识和反映事件之间的心理联系的推理知识，并设计了两个模块 Im 和 Gm，以帮助生成未来事件。