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Dialogue Generation

Dialogue › Dialogue Generation 24 평가 기록 14 datasets

Dialogue generation is the task of "understanding" natural language inputs - within natural language processing in order to produce output. The systems are usually intended for conversing with humans, for instance back and forth dialogue with a conversation agent like a chatbot. Some example benchmarks for this task (see others such as Natural Language Understanding) include FusedChat and Ubuntu DIalogue Corpus (UDC). Models can be evaluated via metrics such as BLEU, ROUGE, and METEOR albeit with challenges in terms of weak correlation with human judgement, that may be addressed by new ones like UnSupervised and Reference-free (USR) and Metric for automatic Unreferenced dialog evaluation (MaUde).

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
LMEDR
Avg F1: 21.99
논문 코드
FV (w/o MI)
BLEU: 1.99Dis-1: 0.0056Dis-2: 0.0431Dis-3: 0.125
논문 코드
Classification-based model
Slot Accuracy: 0.973Joint SA: 0.600Inform: 75.1Inform_mct: 90.8
논문 코드
mm
1 in 10 R@2: 5
논문 코드
∞-former (Sticky memories)
F1: 9.01ROUGE-1: 15.37Rouge-L: 12.56Meteor: 7.55
논문 코드
∞-former (Sticky memories + initialized GPT-2 Small)
Perplexity: 32.48
논문 코드
SpaceFusion
relevance (human): 2.72interest (human): 2.53
논문 -
MrRNN Act.-Ent.
Precision: 4.82Recall: 5.22F1: 4.63
논문 코드
MrRNN Act.-Ent.
Accuracy: 34.48%
논문 코드
MrRNN Act.-Ent.
Precision: 16.84Recall: 9.72F1: 11.43
논문 코드
MrRNN Act.-Ent.
Accuracy: 95.04%
논문 코드

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