Papers Temporal Relation Classification

Looking for the Bottleneck in Fine-grained Temporal Relation Classification

Temporal relation classification is the task of determining the temporal relation between pairs of temporal entities in a text. Despite recent advancements in natural language processing, temporal relation classification…

Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?

The automatic detection of temporal relations among events has been mainly investigated with encoder-only models such as RoBERTa. Large Language Models (LLM) have recently shown promising performance in temporal reasonin…

Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time, and document creation time (DCT). It leads to two crucial limits: 1) Tw…

tieval: An Evaluation Framework for Temporal Information Extraction Systems

Temporal information extraction (TIE) has attracted a great deal of interest over the last two decades, leading to the development of a significant number of datasets. Despite its benefits, having access to a large volum…

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction

In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description. To achieve this, we propose to conduct…

Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning

“Event Temporal Relation Classification (ETRC) is crucial to natural language understanding. In recent years, the mainstream ETRC methods may not take advantage of lots of semantic information contained in golden tempora…

RSGT: Relational Structure Guided Temporal Relation Extraction

Temporal relation extraction aims to extract temporal relations between event pairs, which is crucial for natural language understanding. Few efforts have been devoted to capturing the global features. In this paper, we …

DCT-Centered Temporal Relation Extraction

Most previous work on temporal relation extraction only focused on extracting the temporal relations among events or suffered from the issue of different expressions of events, timexes and Document Creation Time (DCT). M…

How about Time? Probing a Multilingual Language Model for Temporal Relations

This paper presents a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages. Results show an advantage of contextualize…

Selecting Optimal Context Sentences for Event-Event Relation Extraction

Understanding events entails recognizing the structural and temporal orders between event mentions to build event structures/ graphs for input documents. To achieve this goal, our work addresses the problems of subevent …

Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction

Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense h…

TIMERS: Document-level Temporal Relation Extraction

We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal argum…

Extracting Temporal Event Relation with Syntax-guided Graph Transformer

Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that when the events of interest are far away …

TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events

Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs. This restricts the ability of sys…

Word-Level Loss Extensions for Neural Temporal Relation Classification

Unsupervised pre-trained word embeddings are used effectively for many tasks in natural language processing to leverage unlabeled textual data. Often these embeddings are either used as initializations or as fixed word r…

Temporal Event Knowledge Acquisition via Identifying Narratives

Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal "before/after" event knowledge across sentences in narrative stories. The double temporality s…

A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events

We present a sequential model for temporal relation classification between intra-sentence events. The key observation is that the overall syntactic structure and compositional meanings of the multi-word context between e…

Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a signif…

On the contribution of word embeddings to temporal relation classification

Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose en…

Temporal Relation Classification using a Model of Tense and Aspect