Little attention has been paid on \underline{EA}rly \underline{R}umor
\underline{D}etection (EARD), and EARD performance was evaluated
inappropriately on a few datasets where the actual early-stage information is
largely missing. To reverse such situation, we construct BEARD, a new
\underline{B}enchmark dataset for \underline{EARD}, based on claims from
fact-checking websites by trying to gather as many early relevant posts as
possible. We also propose HEARD, a novel model based on neural
\underline{H}awkes process for \underline{EARD}, which can guide a generic
rumor detection model to make timely, accurate and stable predictions.
Experiments show that HEARD achieves effective EARD performance on two commonly
used general rumor detection datasets and our BEARD dataset.

本研究提出了基于神经 Hawkes 过程的新型模型 HEARD，针对以往很少关注的早期谣言检测（EARD）问题，构建了 BEARD 基准数据集，并在传统谣言检测数据集和该数据集上取得了有效的 EARD 表现。

基于神经 Hawkes 过程的早期谣言检测与新基准数据集

Early Rumor Detection Using Neural Hawkes Process with a New Benchmark  Dataset

The neural Hawkes process (Mei & Eisner, 2017) is a generative model of
irregularly spaced sequences of discrete events. To handle complex domains with
many event types, Mei et al. (2020a) further consider a setting in which each
event in the sequence updates a deductive database of facts (via
domain-specific pattern-matching rules); future events are then conditioned on
the database contents. They show how to convert such a symbolic system into a
neuro-symbolic continuous-time generative model, in which each database fact
and the possible event has a time-varying embedding that is derived from its
symbolic provenance.
In this paper, we modify both models, replacing their recurrent LSTM-based
architectures with flatter attention-based architectures (Vaswani et al.,
2017), which are simpler and more parallelizable. This does not appear to hurt
our accuracy, which is comparable to or better than that of the original models
as well as (where applicable) previous attention-based methods (Zuo et al.,
2020; Zhang et al., 2020a).

本文在神经 Hawkes 过程、演绎式数据库、神经符号化模型、注意力机制等关键技术的基础上，采用注意力机制的扁平化结构替换其原来的 LSTM 架构，并展示了相对较高的精度。

不规则间隔事件及其参与者的变形器嵌入

Transformer Embeddings of Irregularly Spaced Events and Their Participants

Events in the world may be caused by other, unobserved events. We consider
sequences of events in continuous time. Given a probability model of complete
sequences, we propose particle smoothing---a form of sequential importance
sampling---to impute the missing events in an incomplete sequence. We develop a
trainable family of proposal distributions based on a type of bidirectional
continuous-time LSTM: Bidirectionality lets the proposals condition on future
observations, not just on the past as in particle filtering. Our method can
sample an ensemble of possible complete sequences (particles), from which we
form a single consensus prediction that has low Bayes risk under our chosen
loss metric. We experiment in multiple synthetic and real domains, using
different missingness mechanisms, and modeling the complete sequences in each
domain with a neural Hawkes process (Mei & Eisner 2017). On held-out incomplete
sequences, our method is effective at inferring the ground-truth unobserved
events, with particle smoothing consistently improving upon particle filtering.

本文提出了一种基于粒子平滑的方法，通过使用一种基于双向连续时间（LSTM）的可训练提议分布来填补残缺的序列，从而推断出遗漏的事件序列，该方法在多个合成和真实领域实验中表现出色，在推断地面真实未观察到的事件方面十分有效。