ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models

Understand

Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates).

• Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data.
• Yet, real-world data-to-text problems often suffer from various data-scarce issues: one may have access to only a handful of or no training examples, and/or have to rely on examples in a different domain or schema.
• To fill this gap, we propose Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse settings by making efficient use of any given (or no) examples.