Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset

TL;DR

This paper presents a baseline study on real-world human-robot dialogue and information extraction using the MobileCS dataset, aiming to improve task-oriented dialogue systems in practical settings.

Contribution

It introduces baseline models for dialogue construction and information extraction on the MobileCS dataset, addressing real-life noisy and casual conversations.

Findings

Baseline models demonstrate initial performance on real-world dialogues.

Challenges identified in handling noisy, casual conversations.

Results provide a foundation for future research in human-robot dialogue systems.

Abstract

Recently, there have merged a class of task-oriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data are in fact simulated data and thus are fundamentally different from real-life conversations, which are more noisy and casual. Recently, the SereTOD challenge is organized and releases the MobileCS dataset, which consists of real-world dialog transcripts between real users and customer-service staffs from China Mobile. Based on the MobileCS dataset, the SereTOD challenge has two tasks, not only evaluating the construction of the dialogue system itself, but also examining information extraction from dialog transcripts, which is crucial for building the knowledge base for TOD. This paper mainly presents a baseline study of the two tasks with the MobileCS dataset. We introduce how the two baselines are constructed, the problems encountered, and the results. We anticipate that the baselines can facilitate exciting future research to build human-robot dialogue systems for real-life tasks.