Control Prefixes for Text Generation

• URL: http://arxiv.org/abs/2110.08329v1
• Date: Fri, 15 Oct 2021 19:32:17 GMT
• Title: Control Prefixes for Text Generation
• Authors: Jordan Clive, Kris Cao, Marek Rei
• Abstract summary: We propose a dynamic method, Control Prefixes, which allows for the inclusion of conditional input-dependent information in each prompt. We present state-of-the-art results on several data-to-text datasets, including WebNLG.
• Score: 17.682443394199375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt learning methods adapt pre-trained language models to downstream applications by using a task-specific prompt together with the input. Most of the current work on prompt learning in text generation relies on a shared dataset-level prompt for all examples in the dataset. We extend this approach and propose a dynamic method, Control Prefixes, which allows for the inclusion of conditional input-dependent information in each prompt. Control Prefixes is at the intersection of prompt learning and controlled generation, empowering the model to have finer-grained control during text generation. The method incorporates attribute-level learnable representations into different layers of a pre-trained transformer, allowing for the generated text to be guided in a particular direction. We provide a systematic evaluation of the technique and apply it to five datasets from the GEM benchmark for natural language generation (NLG). We present state-of-the-art results on several data-to-text datasets, including WebNLG.

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