Event temporal relation extraction~(ETRE) is usually formulated as a
multi-label classification task, where each type of relation is simply treated
as a one-hot label. This formulation ignores the meaning of relations and wipes
out their intrinsic dependency. After examining the relation definitions in
various ETRE tasks, we observe that all relations can be interpreted using the
start and end time points of events. For example, relation \textit{Includes}
could be interpreted as event 1 starting no later than event 2 and ending no
earlier than event 2. In this paper, we propose a unified event temporal
relation extraction framework, which transforms temporal relations into logical
expressions of time points and completes the ETRE by predicting the relations
between certain time point pairs. Experiments on TB-Dense and MATRES show
significant improvements over a strong baseline and outperform the
state-of-the-art model by 0.3\% on both datasets. By representing all relations
in a unified framework, we can leverage the relations with sufficient data to
assist the learning of other relations, thus achieving stable improvement in
low-data scenarios. When the relation definitions are changed, our method can
quickly adapt to the new ones by simply modifying the logic expressions that
map time points to new event relations. The code is released at
https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE.

本文提出了一个统一的事件时间关系提取框架，并将时间关系转化成时间点的逻辑表达式，通过预测某些时间点的关系完成事件时间关系提取。该方法不仅在 TB-Dense 和 MATRES 上取得了显著提升，也可以在低数据情况下实现稳定的改进。

超越分类：一种用于事件时间关系提取的统一框架

More than Classification: A Unified Framework for Event Temporal  Relation Extraction

Detecting events and their evolution through time is a crucial task in
natural language understanding. Recent neural approaches to event temporal
relation extraction typically map events to embeddings in the Euclidean space
and train a classifier to detect temporal relations between event pairs.
However, embeddings in the Euclidean space cannot capture richer asymmetric
relations such as event temporal relations. We thus propose to embed events
into hyperbolic spaces, which are intrinsically oriented at modeling
hierarchical structures. We introduce two approaches to encode events and their
temporal relations in hyperbolic spaces. One approach leverages hyperbolic
embeddings to directly infer event relations through simple geometrical
operations. In the second one, we devise an end-to-end architecture composed of
hyperbolic neural units tailored for the temporal relation extraction task.
Thorough experimental assessments on widely used datasets have shown the
benefits of revisiting the tasks on a different geometrical space, resulting in
state-of-the-art performance on several standard metrics. Finally, the ablation
study and several qualitative analyses highlighted the rich event semantics
implicitly encoded into hyperbolic spaces.

该研究通过将事件嵌入在双曲空间中，利用双曲嵌入进行事件之间的关系预测，取得了当前最优的表现。

通过双曲几何提取事件时序关系

Extracting Event Temporal Relations via Hyperbolic Geometry

We propose a novel deep structured learning framework for event temporal
relation extraction. The model consists of 1) a recurrent neural network (RNN)
to learn scoring functions for pair-wise relations, and 2) a structured support
vector machine (SSVM) to make joint predictions. The neural network
automatically learns representations that account for long-term contexts to
provide robust features for the structured model, while the SSVM incorporates
domain knowledge such as transitive closure of temporal relations as
constraints to make better globally consistent decisions. By jointly training
the two components, our model combines the benefits of both data-driven
learning and knowledge exploitation. Experimental results on three high-quality
event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that
incorporated with pre-trained contextualized embeddings, the proposed model
achieves significantly better performances than the state-of-the-art methods on
all three datasets. We also provide thorough ablation studies to investigate
our model.

本文提出了一种新的深度结构化学习框架，用于事件时间关系提取，其结构包括递归神经网络和结构化支持向量机，通过联合训练两部分模型，采用预训练上下文嵌入，证明了提出的模型的性能明显优于现有的方法，并提供了详尽的削减研究。