# The Temporal Game: A New Perspective on Temporal Relation Extraction

## TL;DR

The paper introduces the Temporal Game, a novel interactive approach for temporal relation extraction that decomposes interval relations into point-wise comparisons, supporting flexible annotation and enabling reinforcement learning applications.

## Contribution

It presents a new point-based game framework for temporal relation extraction that improves annotation flexibility and facilitates training reinforcement learning agents.

## Key findings

- Supports both interval and instant entities.
- Enables fine-grained temporal annotation.
- Provides a publicly available demo and open-source code.

## Abstract

In this paper we demo the Temporal Game, a novel approach to temporal relation extraction that casts the task as an interactive game. Instead of directly annotating interval-level relations, our approach decomposes them into point-wise comparisons between the start and end points of temporal entities. At each step, players classify a single point relation, and the system applies temporal closure to infer additional relations and enforce consistency. This point-based strategy naturally supports both interval and instant entities, enabling more fine-grained and flexible annotation than any previous approach. The Temporal Game also lays the groundwork for training reinforcement learning agents, by treating temporal annotation as a sequential decision-making task. To showcase this potential, the demo presented in this paper includes a Game mode, in which users annotate texts from the TempEval-3 dataset and receive feedback based on a scoring system, and an Annotation mode, that allows custom documents to be annotated and resulting timeline to be exported. Therefore, this demo serves both as a research tool and an annotation interface. The demo is publicly available at https://temporal-game.inesctec.pt, and the source code is open-sourced to foster further research and community-driven development in temporal reasoning and annotation.