mlsanityData to 7 Oct 2026

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

arXiv 2209.13464 ↗First posted 27 Sep 2022cs.CL cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 36 of its 57 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

36 of 57 references.

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2022Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systemscited by 15
  2. 2023Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervisioncited by 7
  3. 2024The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)cited by 2
  4. 2025Entriever: Energy-based Retriever for Knowledge-Grounded Dialog Systemscited by 0