Retrieval of Temporal Event Sequences from Textual Descriptions

TL;DR

This paper introduces TESRBench, a new benchmark for evaluating retrieval of temporal event sequences from text, and proposes TPP-Embedding, a novel model that combines language models with temporal point processes for improved retrieval accuracy.

Contribution

The paper presents TESRBench, a comprehensive dataset benchmark, and introduces TPP-Embedding, a novel model integrating LLMs and TPPs for better temporal event sequence retrieval.

Findings

TPP-Embedding outperforms baseline models on TESRBench datasets.

TESRBench provides diverse real-world datasets for evaluation.

The model effectively unifies event semantics and temporal dynamics.

Abstract

Retrieving temporal event sequences from textual descriptions is crucial for applications such as analyzing e-commerce behavior, monitoring social media activities, and tracking criminal incidents. To advance this task, we introduce TESRBench, a comprehensive benchmark for temporal event sequence retrieval (TESR) from textual descriptions. TESRBench includes diverse real-world datasets with synthesized and reviewed textual descriptions, providing a strong foundation for evaluating retrieval performance and addressing challenges in this domain. Building on this benchmark, we propose TPP-Embedding, a novel model for embedding and retrieving event sequences. The model leverages the TPP-LLM framework, integrating large language models (LLMs) with temporal point processes (TPPs) to encode both event texts and times. By pooling representations and applying a contrastive loss, it unifies temporal dynamics and event semantics in a shared embedding space, aligning sequence-level embeddings of event sequences and their descriptions. TPP-Embedding demonstrates superior performance over baseline models across TESRBench datasets, establishing it as a powerful solution for the temporal event sequence retrieval task.