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The semantics of a text is manifested not only by what is read, but also bywhat is not read. In this article, we will study how the implicit "not read"information such as end-of-paragraph (\eop) and end-of-sequence (\eos) affectthe quality of text generation. Specifically, we find that the pre-trainedlanguage model GPT2 can generate better continuations by learning to generatethe \eop in the fine-tuning stage. Experimental results on English storygeneration show that \eop can lead to higher BLEU score and lower \eosperplexity. We also conduct experiments on a self-collected Chinese essaydataset with Chinese-GPT2, a character level LM without \eop or \eos duringpre-training. Experimental results show that the Chinese GPT2 can generatebetter essay endings with \eop.

Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2