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- W3176788174 abstract "Many conversation datasets have been constructed in the recent years using crowdsourcing. However, the data collection process can be time consuming and presents many challenges to ensure data quality. Since language generation has improved immensely in recent years with the advancement of pre-trained language models, we investigate how such models can be utilized to generate entire conversations, given only a summary of a conversation as the input. We explore three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. We also show that the accuracy of conversation summarization can be improved by augmenting a conversation summarization dataset with generated conversations." @default.
- W3176788174 created "2021-07-05" @default.
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- W3176788174 date "2021-01-01" @default.
- W3176788174 modified "2023-09-22" @default.
- W3176788174 title "Summary Grounded Conversation Generation" @default.
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- W3176788174 doi "https://doi.org/10.18653/v1/2021.findings-acl.329" @default.
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