@inproceedings{won-etal-2023-break,
    title = "{BREAK}: Breaking the Dialogue State Tracking Barrier with Beam Search and Re-ranking",
    author = "Won, Seungpil  and
      Kwak, Heeyoung  and
      Shin, Joongbo  and
      Han, Janghoon  and
      Jung, Kyomin",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.159",
    doi = "10.18653/v1/2023.acl-long.159",
    pages = "2832--2846",
    abstract = "Despite the recent advances in dialogue state tracking (DST), the joint goal accuracy (JGA) of the existing methods on MultiWOZ 2.1 still remains merely 60{\%}. In our preliminary error analysis, we find that beam search produces a pool of candidates that is likely to include the correct dialogue state. Motivated by this observation, we introduce a novel framework, called BREAK (Beam search and RE-rAnKing), that achieves outstanding performance on DST. BREAK performs DST in two stages: (i) generating k-best dialogue state candidates with beam search and (ii) re-ranking the candidates to select the correct dialogue state. This simple yet powerful framework shows state-of-the-art performance on all versions of MultiWOZ and M2M datasets. Most notably, we push the joint goal accuracy to 80-90{\%} on MultiWOZ 2.1-2.4, which is an improvement of 23.6{\%}, 26.3{\%}, 21.7{\%}, and 10.8{\%} over the previous best-performing models, respectively. The data and code will be available at \url{https://github.com/tony-won/DST-BREAK}",
}

Despite the recent advances in dialogue state tracking (DST), the joint goal accuracy (JGA) of the existing methods on MultiWOZ 2.1 still remains merely 60{\%}. In our preliminary error analysis, we find that beam search produces a pool of candidates that is likely to include the correct dialogue state. Motivated by this observation, we introduce a novel framework, called BREAK (Beam search and RE-rAnKing), that achieves outstanding performance on DST. BREAK performs DST in two stages: (i) generating k-best dialogue state candidates with beam search and (ii) re-ranking the candidates to select the correct dialogue state. This simple yet powerful framework shows state-of-the-art performance on all versions of MultiWOZ and M2M datasets. Most notably, we push the joint goal accuracy to 80-90{\%} on MultiWOZ 2.1-2.4, which is an improvement of 23.6{\%}, 26.3{\%}, 21.7{\%}, and 10.8{\%} over the previous best-performing models, respectively. The data and code will be available at \url{https://github.com/tony-won/DST-BREAK}
Won, Seungpil Kwak, Heeyoung Shin, Joongbo Han, Janghoon Jung, Kyomin
BREAK: Breaking the Dialogue State Tracking Barrier with Beam Search and Re-ranking