@inproceedings{nourbakhsh-etal-2026-tracer,
    title = "{TRACER}: Early Failure Detection for Task-Oriented Dialogue",
    author = "Nourbakhsh, Erfan  and
      Slavin, Rocky  and
      Yang, Ke  and
      Rios, Anthony",
    editor = "Choi, Jinho D.  and
      Chen, Yun-Nung  and
      Funakoshi, Kotaro  and
      Emami, Ali",
    booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2026",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-07/2026.sigdial-1.44/",
    pages = "614--635",
    abstract = "Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present TRACER, a method for early failure detection in task-oriented dialogue. TRACER predicts from a partial dialogue whether the full conversation will eventually fail by combining simple trajectory signals from belief-state changes with text representations of the evolving dialogue state. We evaluate the method in both oracle and generated belief-state settings, and test how well it works when only 25{\%}, 50{\%}, 75{\%}, or 100{\%} of the dialogue is visible. Across these settings, TRACER detects useful failure signals well before the end of the conversation and outperforms heuristic, classical, and single-stream baselines. These results suggest that early failure detection can provide a practical warning signal for dialogue systems before the interaction fully breaks down. Source code can be found here: \url{https://github.com/erfan-nourbakhsh/TRACER}."
}