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Refocusing on Relevance: Personalization in NLG
[article]
2021
arXiv
pre-print
Many NLG tasks such as summarization, dialogue response, or open domain question answering focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user's intent or context of work is not easily recoverable based solely on that source text -- a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional
arXiv:2109.05140v1
fatcat:pqghixqbq5grlbrootumsgbr4u