Facts2Story: Controlling Text Generation by Key Facts

• URL: http://arxiv.org/abs/2012.04332v1
• Date: Tue, 8 Dec 2020 10:14:29 GMT
• Title: Facts2Story: Controlling Text Generation by Key Facts
• Authors: Eyal Orbach (Bar Ilan University), Yoav Goldberg (Bar Ilan University and Allen Institute for Artificial Intelligence)
• Abstract summary: We propose a controlled generation task based on expanding a sequence of facts, expressed in natural language, into a longer narrative. We show that while auto-regressive, unidirectional Language Models such as GPT2 produce better fluency, they struggle to adhere to the requested facts. We propose a plan-and-cloze model (using fine-tuned XLNet) which produces competitive fluency while adhering to the requested content.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in self-attention neural network architectures have raised the bar for open-ended text generation. Yet, while current methods are capable of producing a coherent text which is several hundred words long, attaining control over the content that is being generated -- as well as evaluating it -- are still open questions. We propose a controlled generation task which is based on expanding a sequence of facts, expressed in natural language, into a longer narrative. We introduce human-based evaluation metrics for this task, as well as a method for deriving a large training dataset. We evaluate three methods on this task, based on fine-tuning pre-trained models. We show that while auto-regressive, unidirectional Language Models such as GPT2 produce better fluency, they struggle to adhere to the requested facts. We propose a plan-and-cloze model (using fine-tuned XLNet) which produces competitive fluency while adhering to the requested content.

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