@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://aclanthology.org/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}."
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<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: https://github.com/erfan-nourbakhsh/TRACER.</abstract>
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%0 Conference Proceedings
%T TRACER: Early Failure Detection for Task-Oriented Dialogue
%A Nourbakhsh, Erfan
%A Slavin, Rocky
%A Yang, Ke
%A Rios, Anthony
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F nourbakhsh-etal-2026-tracer
%X 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: https://github.com/erfan-nourbakhsh/TRACER.
%U https://aclanthology.org/2026.sigdial-1.44/
%P 614-635
Markdown (Informal)
[TRACER: Early Failure Detection for Task-Oriented Dialogue](https://aclanthology.org/2026.sigdial-1.44/) (Nourbakhsh et al., SIGDIAL 2026)
ACL
- Erfan Nourbakhsh, Rocky Slavin, Ke Yang, and Anthony Rios. 2026. TRACER: Early Failure Detection for Task-Oriented Dialogue. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 614–635, Atlanta, Georgia, USA. Association for Computational Linguistics.