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Distilling the Knowledge of Large-scale Generative Models into Retrieval
Models for Efficient Open-domain Conversation

v1v2 (latest)

Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021

28 August 2021

Enkhbayar Erdenee

ArXiv (abs)PDF HTML

Papers citing "Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation"

5 / 5 papers shown

Accurate Knowledge Distillation with n-best Reranking

Accurate Knowledge Distillation with n-best RerankingNorth American Chapter of the Association for Computational Linguistics (NAACL), 2023

Hendra Setiawan

380

4

0

20 May 2023

Measuring and Improving Semantic Diversity of Dialogue Generation

Measuring and Improving Semantic Diversity of Dialogue GenerationConference on Empirical Methods in Natural Language Processing (EMNLP), 2022

169

22

0

11 Oct 2022

Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional
Characters with only a Few Utterances

Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few UtterancesNorth American Chapter of the Association for Computational Linguistics (NAACL), 2022

Enkhbayar Erdenee

179

43

0

22 Apr 2022

Understanding and Improving the Exemplar-based Generation for
Open-domain Conversation

Understanding and Improving the Exemplar-based Generation for Open-domain Conversation

Enkhbayar Erdenee

177

3

0

13 Dec 2021

Billion-scale similarity search with GPUs

Billion-scale similarity search with GPUsIEEE Transactions on Big Data (TBD), 2017

976

4,561

0

28 Feb 2017