BREAK: Breaking the Dialogue State Tracking Barrier with Beam Search and Re-ranking
Seungpil Won, Heeyoung Kwak, Joongbo Shin, Janghoon Han, Kyomin Jung · 2023
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.Our proposed method 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.