지능형 대화 시스템은 줄곧 서비스의 목표와 무관한 사용자 입력을 전달받아, 그 처리 성능을 의심받는다. 특히 종단간 대화 이해 생성 모델이나, 기계학습 기반 대화 이해 모델은 학습 시간대에 한정된 범위의 도메인 입력에만 노출됨으로, 사용자 발화를 자신이 처리 가능한 도메인으로 과신하는 경향이 있다. 본 연구에서는 대화 생성 모델이 처리할 수 없는 입력과 신뢰도가 낮은 생성 결과를 배제하기 위해 불확실성 정량화 기법을 대화 의도 분류 모델에 적용한다. 여러 번의 추론 샘플링이 필요 없는 실용적인 예측 신뢰도 획득 방법과 함께, 평가 시간대와 또다른 도메인으로 구성된 분포 외 입력 데이터를 학습에 노출시키는 것이 분포 외 입력을 구분하는데 도움이 되는지를 실험으로 확인한다.
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