Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024)

Abstract: Understanding and analyzing event temporal relations is a crucial task in Natural Language Processing (NLP). This task, known as Event Temporal Relation Extraction (ETRE), aims to identify and extract temporal connections between events in text. Recent studies focus on locating the relative position of event pairs on the timeline by designing logical expressions or auxiliary tasks to predict their temporal occurrence. Despite these advances, this modeling approach neglects the multidimensional information in temporal relation and the hierarchical process of reasoning. In this study, we propose a novel hierarchical modeling approach for this task by introducing a Temporal Cognitive Tree (TCT) that mimics human logical reasoning. Additionally, we also design a integrated model incorporating prompt optimization and deductive reasoning to exploit multidimensional supervised information. Extensive experiments on TB-Dense and MATRES datasets demonstrate that our approach outperforms existing methods.

## introduction event relations usually refer to the mutual connections and influences between events. understanding and analyzing event relations are crucial for individuals to comprehend the world. in the field of natural language processing (nlp), extracting temporal relations between events is a critical task that aims to identify and interpret the temporal connections within textual data, as illustrated in figure 1 , given a sentence containing two events and a set of candidate temporal relations, our objective is to determine that the relation between the event1 based and the event2 finish is includes. researchers have invested substantial effort in the event temporal relation extraction (etre) task and have explored this topic in various ways. ## method in this section, we will introduce our entire model. our overall model is illustrated in figure 2 . first, we will define the task of event temporal relation extraction. then, we will present the design of our temporal cognitive tree (tct). following this, we will present two modules proposed in our model based on tct: a temporal judgment module based on multi-task prompt learning, and a temporal inference module based on deductive reasoning. finally, we will explain how we integrate these two modules to obtain the final temporal relation extraction model. ## related work early works mainly utilized traditional machine learning and statistics-based methods for etre @xcite . with the development of deep learning, some works have combined pre-trained language models with graph-based models to improve encoding performance for alleviating the problem of longdistance dependency @xcite @xcite . some works focus on the problem of data scarcity in existing datasets, and propose to introduce external knowledge for knowledge enhancement @xcite @xcite @xcite . there are also works that employ multi-task learning to compensate for the limitations of single-text classification tasks @xcite @xcite . additionally, some of the latest work concerned with the significance of temporal semantics, and further enhanced the performance of temporal relation extraction by combining some rule constraints @xcite . recently, the rapid development of large language models(llms) has drawn attention to the potential of applying llms to etre task. @xcite utilized prompt engineering techniques and conducted extensive experiments on chatgpt to demonstrate that there is still considerable room for directly predicting on chatgpt compared to supervised learning with smaller-scale models. additionally, @xcite validated the limitations of chatgpt in etre tasks in their work, with the best test result on the tb-dense dataset achieving a micro-f1 score of 41.0%. ## conclusion and future work in this paper, we propose a novel hierarchical modeling approach for etre. specifically, we introduce a temporal cognitive tree (tct) that aligns with human logical reasoning processes. our approach integrates optimization by prompts and deductive reasoning, enhancing the model's ability to understand and extract temporal relations from a multidimensional perspective. extensive experiments demonstrate that our approach achieves significant performance without the need for external knowledge. in future work, we aim to explore the possibilities of optimizing and extending this approach to accommodate relation extraction tasks with varying fields and data volumes.