Asking the Right Questions in Low Resource Template Extraction
This work addresses data efficiency in information extraction for end users, but appears incremental as it builds on existing QA mapping approaches.
The paper tackled the problem of improving data efficiency in template extraction by mapping tasks to question answering, and found that using questions as prompts outperforms other prompt styles and does not require NLP expertise to author.
Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency. Especially in template extraction (TE), mapping an ontology to a set of questions can be more time-efficient than collecting labeled examples. We ask whether end users of TE systems can design these questions, and whether it is beneficial to involve an NLP practitioner in the process. We compare questions to other ways of phrasing natural language prompts for TE. We propose a novel model to perform TE with prompts, and find it benefits from questions over other styles of prompts, and that they do not require an NLP background to author.